Benchmark Results - ScanNet Benchmark

This table lists the benchmark results for the ScanNet200 3D semantic label scenario.

Method	avg iou	head iou	common iou	tail iou	backpack	bag	ball	bar	basket	bathroom cabinet	bathroom stall	bathroom stall door	bathroom vanity	bathtub	bed	bench	bicycle	bin	blackboard	blanket	blinds	board	book	bookshelf	bottle	bowl	box	broom	bucket	bulletin board	cabinet	calendar	candle	cart	case of water bottles	ceiling	ceiling light	chair	clock	closet	closet rod	clothes	clothes dryer	coat rack	coffee kettle	coffee maker	coffee table	column	computer tower	copier	couch	counter	cup	curtain	cushion	decoration	desk	dining table	dish rack	dishwasher	door	doorframe	dresser	dumbbell	end table	fan	file cabinet	fire alarm	fire extinguisher	floor	folded chair	hat	headphones	ironing board	jacket	keyboard	keyboard piano	kitchen cabinet	kitchen counter	ladder	lamp	laptop	laundry basket	laundry detergent	laundry hamper	ledge	light	light switch	machine	mailbox	mat	microwave	mini fridge	mirror	monitor	mouse	music stand	nightstand	object	office chair	oven	paper	paper bag	paper cutter	paper towel dispenser	paper towel roll	person	piano	picture	pillow	pipe	plant	plate	plunger	poster	power outlet	power strip	printer	projector	projector screen	rack	radiator	range hood	recycling bin	refrigerator	scale	seat	shelf	shoe	shower	shower curtain	shower curtain rod	shower floor	shower head	shower wall	sign	sink	soap dispenser	sofa chair	speaker	stair rail	stairs	stand	stool	storage bin	storage container	storage organizer	stove	structure	stuffed animal	suitcase	table	telephone	tissue box	toaster	toilet	toilet paper	toilet paper dispenser	towel	trash bin	trash can	tray	tube	tv	tv stand	vacuum cleaner	vent	wall	wardrobe	washing machine	water bottle	water cooler	water pitcher	whiteboard	window	windowsill

IMFSegNet	0.334 9	0.532 13	0.251 11	0.179 7	0.486 9	0.041 16	0.139 13	0.003 1	0.283 4	0.000 1	0.274 15	0.191 15	0.457 14	0.704 14	0.795 7	0.197 9	0.830 6	0.000 3	0.710 9	0.055 16	0.064 4	0.518 6	0.305 10	0.458 17	0.216 12	0.027 5	0.284 13	0.000 1	0.000 3	0.044 12	0.406 10	0.561 7	0.000 1	0.080 12	0.000 3	0.873 9	0.021 15	0.683 8	0.000 7	0.076 9	0.494 10	0.363 9	0.648 16	0.000 1	0.000 2	0.425 9	0.649 4	0.000 10	0.668 12	0.908 7	0.740 11	0.010 14	0.206 8	0.862 10	0.000 1	0.000 11	0.560 9	0.000 7	0.359 13	0.237 11	0.631 12	0.408 11	0.411 4	0.322 15	0.246 4	0.439 10	0.599 13	0.047 4	0.213 7	0.940 10	0.139 11	0.000 1	0.369 5	0.124 10	0.188 12	0.495 11	0.624 11	0.626 8	0.320 14	0.595 4	0.495 8	0.496 10	0.000 4	0.000 1	0.340 12	0.014 6	0.032 7	0.135 5	0.000 4	0.903 8	0.277 6	0.612 8	0.196 7	0.344 12	0.848 13	0.260 6	0.000 1	0.574 13	0.073 16	0.062 4	0.000 4	0.000 1	0.091 6	0.839 3	0.776 3	0.123 12	0.392 9	0.756 12	0.274 5	0.518 12	0.029 16	0.842 4	0.000 6	0.357 13	0.000 1	0.035 7	0.000 3	0.444 12	0.793 2	0.245 5	0.000 1	0.512 16	0.512 15	0.159 15	0.713 13	0.000 10	0.000 1	0.336 13	0.484 12	0.569 2	0.852 9	0.615 6	0.120 12	0.068 10	0.228 8	0.000 1	0.733 10	0.773 2	0.190 4	0.000 10	0.608 6	0.792 4	0.000 1	0.597 7	0.000 14	0.025 2	0.000 1	0.573 17	0.000 2	0.000 1	0.508 11	0.555 8	0.363 10	0.139 12	0.610 2	0.947 8	0.305 7	0.594 9	0.527 9	0.009 17	0.633 13	0.000 1	0.060 3	0.820 5	0.604 15	0.799 9	0.000 1	0.799 11	0.034 14	0.784 13	0.000 1	0.618 6	0.424 2	0.134 16	0.646 13	0.214 14

OA-CNN-L_ScanNet200	0.333 11	0.558 5	0.269 9	0.124 13	0.448 14	0.080 9	0.272 5	0.000 3	0.000 7	0.000 1	0.342 8	0.515 4	0.524 7	0.713 13	0.789 9	0.158 12	0.384 12	0.000 3	0.806 6	0.125 7	0.000 9	0.496 8	0.332 7	0.498 14	0.227 8	0.024 6	0.474 3	0.000 1	0.003 2	0.071 9	0.487 3	0.000 11	0.000 1	0.110 8	0.000 3	0.876 7	0.013 17	0.703 3	0.000 7	0.076 9	0.473 12	0.355 11	0.906 6	0.000 1	0.000 2	0.476 6	0.706 1	0.000 10	0.672 10	0.835 13	0.748 9	0.015 13	0.223 7	0.860 11	0.000 1	0.000 11	0.572 7	0.000 7	0.509 7	0.313 7	0.662 4	0.398 13	0.396 8	0.411 13	0.276 2	0.527 4	0.711 5	0.000 7	0.076 13	0.946 6	0.166 6	0.000 1	0.022 10	0.160 7	0.183 13	0.493 13	0.699 9	0.637 6	0.403 6	0.330 12	0.406 13	0.526 6	0.024 2	0.000 1	0.392 11	0.000 11	0.016 16	0.000 12	0.196 3	0.915 5	0.112 12	0.557 10	0.197 6	0.352 10	0.877 3	0.000 12	0.000 1	0.592 12	0.103 11	0.000 14	0.067 1	0.000 1	0.089 7	0.735 7	0.625 11	0.130 9	0.568 6	0.836 7	0.271 8	0.534 9	0.043 13	0.799 11	0.001 5	0.445 5	0.000 1	0.000 8	0.024 2	0.661 4	0.000 5	0.262 3	0.000 1	0.591 8	0.517 13	0.373 8	0.788 7	0.021 8	0.000 1	0.455 4	0.517 9	0.320 8	0.823 12	0.200 16	0.001 17	0.150 5	0.100 12	0.000 1	0.736 9	0.668 10	0.103 14	0.052 6	0.662 4	0.720 8	0.000 1	0.602 6	0.112 7	0.002 6	0.000 1	0.637 9	0.000 2	0.000 1	0.621 10	0.569 5	0.398 9	0.412 5	0.234 12	0.949 6	0.363 5	0.492 14	0.495 11	0.251 4	0.665 9	0.000 1	0.001 11	0.805 7	0.833 6	0.794 11	0.000 1	0.821 5	0.314 5	0.843 11	0.000 1	0.560 10	0.245 7	0.262 6	0.713 4	0.370 11

CSC-Pretrain	0.249 17	0.455 17	0.171 16	0.079 17	0.418 15	0.059 14	0.186 10	0.000 3	0.000 7	0.000 1	0.335 10	0.250 13	0.316 16	0.766 7	0.697 17	0.142 13	0.170 14	0.003 2	0.553 14	0.112 9	0.097 1	0.201 16	0.186 14	0.476 15	0.081 16	0.000 9	0.216 17	0.000 1	0.000 3	0.001 17	0.314 17	0.000 11	0.000 1	0.055 15	0.000 3	0.832 16	0.094 3	0.659 15	0.002 5	0.076 9	0.310 16	0.293 17	0.664 14	0.000 1	0.000 2	0.175 17	0.634 6	0.130 2	0.552 17	0.686 17	0.700 17	0.076 7	0.110 15	0.770 17	0.000 1	0.000 11	0.430 17	0.000 7	0.319 15	0.166 15	0.542 17	0.327 16	0.205 16	0.332 14	0.052 16	0.375 13	0.444 17	0.000 7	0.012 17	0.930 17	0.203 3	0.000 1	0.000 12	0.046 12	0.175 14	0.413 16	0.592 14	0.471 16	0.299 15	0.152 16	0.340 16	0.247 17	0.000 4	0.000 1	0.225 15	0.058 3	0.037 4	0.000 12	0.207 2	0.862 15	0.014 14	0.548 13	0.033 16	0.233 16	0.816 16	0.000 12	0.000 1	0.542 15	0.123 5	0.121 1	0.019 2	0.000 1	0.000 11	0.463 16	0.454 17	0.045 17	0.128 17	0.557 15	0.235 14	0.441 16	0.063 11	0.484 17	0.000 6	0.308 17	0.000 1	0.000 8	0.000 3	0.318 17	0.000 5	0.000 9	0.000 1	0.545 14	0.543 12	0.164 14	0.734 9	0.000 10	0.000 1	0.215 17	0.371 16	0.198 14	0.743 14	0.205 15	0.062 15	0.000 11	0.079 14	0.000 1	0.683 16	0.547 16	0.142 9	0.000 10	0.441 11	0.579 15	0.000 1	0.464 14	0.098 9	0.041 1	0.000 1	0.590 14	0.000 2	0.000 1	0.373 13	0.494 14	0.174 15	0.105 16	0.001 17	0.895 16	0.222 16	0.537 12	0.307 16	0.180 5	0.625 14	0.000 1	0.000 12	0.591 17	0.609 14	0.398 15	0.000 1	0.766 17	0.014 16	0.638 17	0.000 1	0.377 13	0.004 13	0.206 13	0.609 17	0.465 5
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround	0.272 15	0.485 15	0.184 15	0.106 15	0.476 11	0.077 10	0.218 8	0.000 3	0.000 7	0.000 1	0.547 2	0.295 11	0.540 5	0.746 10	0.745 15	0.058 16	0.112 16	0.005 1	0.658 11	0.077 15	0.000 9	0.322 14	0.178 16	0.512 11	0.190 13	0.199 2	0.277 15	0.000 1	0.000 3	0.173 7	0.399 12	0.000 11	0.000 1	0.039 16	0.000 3	0.858 14	0.085 7	0.676 11	0.002 5	0.103 6	0.498 8	0.323 14	0.703 12	0.000 1	0.000 2	0.296 15	0.549 12	0.216 1	0.702 6	0.768 14	0.718 14	0.028 10	0.092 16	0.786 16	0.000 1	0.000 11	0.453 16	0.022 5	0.251 17	0.252 9	0.572 15	0.348 14	0.321 11	0.514 7	0.063 15	0.279 16	0.552 15	0.000 7	0.019 16	0.932 15	0.132 15	0.000 1	0.000 12	0.000 15	0.156 17	0.457 15	0.623 12	0.518 14	0.265 16	0.358 11	0.381 15	0.395 14	0.000 4	0.000 1	0.127 17	0.012 8	0.051 1	0.000 12	0.000 4	0.886 13	0.014 14	0.437 17	0.179 8	0.244 15	0.826 15	0.000 12	0.000 1	0.599 10	0.136 1	0.085 3	0.000 4	0.000 1	0.000 11	0.565 13	0.612 13	0.143 5	0.207 15	0.566 14	0.232 15	0.446 15	0.127 4	0.708 15	0.000 6	0.384 9	0.000 1	0.000 8	0.000 3	0.402 14	0.000 5	0.059 7	0.000 1	0.525 15	0.566 11	0.229 12	0.659 15	0.000 10	0.000 1	0.265 15	0.446 14	0.147 16	0.720 17	0.597 8	0.066 14	0.000 11	0.187 9	0.000 1	0.726 13	0.467 17	0.134 12	0.000 10	0.413 15	0.629 12	0.000 1	0.363 16	0.055 10	0.022 3	0.000 1	0.626 11	0.000 2	0.000 1	0.323 15	0.479 17	0.154 16	0.117 15	0.028 16	0.901 15	0.243 15	0.415 16	0.295 17	0.143 6	0.610 16	0.000 1	0.000 12	0.777 12	0.397 17	0.324 16	0.000 1	0.778 15	0.179 8	0.702 16	0.000 1	0.274 16	0.404 4	0.233 10	0.622 15	0.398 7
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
AWCS	0.305 14	0.508 14	0.225 14	0.142 11	0.463 13	0.063 13	0.195 9	0.000 3	0.000 7	0.000 1	0.467 3	0.551 3	0.504 8	0.773 6	0.764 14	0.142 13	0.029 17	0.000 3	0.626 13	0.100 11	0.000 9	0.360 13	0.179 15	0.507 13	0.137 15	0.006 8	0.300 12	0.000 1	0.000 3	0.172 8	0.364 15	0.512 9	0.000 1	0.056 14	0.000 3	0.865 13	0.093 4	0.634 17	0.000 7	0.071 13	0.396 14	0.296 16	0.876 9	0.000 1	0.000 2	0.373 13	0.436 16	0.063 9	0.749 2	0.877 10	0.721 12	0.131 3	0.124 14	0.804 15	0.000 1	0.000 11	0.515 12	0.010 6	0.452 10	0.252 9	0.578 14	0.417 8	0.179 17	0.484 10	0.171 7	0.337 14	0.606 12	0.000 7	0.115 10	0.937 14	0.142 9	0.000 1	0.008 11	0.000 15	0.157 16	0.484 14	0.402 17	0.501 15	0.339 9	0.553 7	0.529 3	0.478 12	0.000 4	0.000 1	0.404 10	0.001 10	0.022 13	0.077 9	0.000 4	0.894 12	0.219 7	0.628 7	0.093 15	0.305 14	0.886 1	0.233 9	0.000 1	0.603 9	0.112 6	0.023 9	0.000 4	0.000 1	0.000 11	0.741 6	0.664 8	0.097 15	0.253 14	0.782 10	0.264 11	0.523 11	0.154 2	0.707 16	0.000 6	0.411 8	0.000 1	0.000 8	0.000 3	0.332 16	0.000 5	0.000 9	0.000 1	0.602 7	0.595 10	0.185 13	0.656 16	0.159 6	0.000 1	0.355 11	0.424 15	0.154 15	0.729 15	0.516 10	0.220 10	0.620 3	0.084 13	0.000 1	0.707 14	0.651 13	0.173 5	0.014 9	0.381 17	0.582 14	0.000 1	0.619 3	0.049 12	0.000 7	0.000 1	0.702 4	0.000 2	0.000 1	0.302 16	0.489 15	0.317 13	0.334 7	0.392 7	0.922 14	0.254 13	0.533 13	0.394 13	0.129 14	0.613 15	0.000 1	0.000 12	0.820 5	0.649 11	0.749 13	0.000 1	0.782 14	0.282 6	0.863 6	0.000 1	0.288 15	0.006 12	0.220 11	0.633 14	0.542 3
: Long-Tailed 3D Semantic Segmentation with Adaptive Weight Constraint and Sampling. ICRA 2024
CeCo	0.340 7	0.551 9	0.247 13	0.181 6	0.475 12	0.057 15	0.142 12	0.000 3	0.000 7	0.000 1	0.387 6	0.463 6	0.499 9	0.924 2	0.774 11	0.213 6	0.257 13	0.000 3	0.546 15	0.100 11	0.006 8	0.615 2	0.177 17	0.534 7	0.246 6	0.000 9	0.400 5	0.000 1	0.338 1	0.006 16	0.484 5	0.609 5	0.000 1	0.083 11	0.000 3	0.873 9	0.089 5	0.661 14	0.000 7	0.048 15	0.560 4	0.408 6	0.892 8	0.000 1	0.000 2	0.586 1	0.616 8	0.000 10	0.692 8	0.900 8	0.721 12	0.162 1	0.228 6	0.860 11	0.000 1	0.000 11	0.575 5	0.083 3	0.550 4	0.347 4	0.624 13	0.410 10	0.360 9	0.740 3	0.109 13	0.321 15	0.660 8	0.000 7	0.121 9	0.939 13	0.143 8	0.000 1	0.400 2	0.003 13	0.190 11	0.564 6	0.652 10	0.615 11	0.421 5	0.304 13	0.579 1	0.547 5	0.000 4	0.000 1	0.296 14	0.000 11	0.030 9	0.096 7	0.000 4	0.916 4	0.037 13	0.551 12	0.171 9	0.376 7	0.865 7	0.286 5	0.000 1	0.633 5	0.102 12	0.027 8	0.011 3	0.000 1	0.000 11	0.474 14	0.742 5	0.133 7	0.311 13	0.824 8	0.242 13	0.503 14	0.068 9	0.828 9	0.000 6	0.429 7	0.000 1	0.063 5	0.000 3	0.781 2	0.000 5	0.000 9	0.000 1	0.665 2	0.633 6	0.450 6	0.818 2	0.000 10	0.000 1	0.429 5	0.532 7	0.226 13	0.825 11	0.510 11	0.377 5	0.709 2	0.079 14	0.000 1	0.753 5	0.683 8	0.102 15	0.063 5	0.401 16	0.620 13	0.000 1	0.619 3	0.000 14	0.000 7	0.000 1	0.595 13	0.000 2	0.000 1	0.345 14	0.564 6	0.411 8	0.603 1	0.384 8	0.945 9	0.266 11	0.643 5	0.367 14	0.304 1	0.663 10	0.000 1	0.010 7	0.726 15	0.767 7	0.898 3	0.000 1	0.784 13	0.435 1	0.861 7	0.000 1	0.447 11	0.000 15	0.257 7	0.656 11	0.377 10
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
OctFormer ScanNet200	0.326 13	0.539 10	0.265 10	0.131 12	0.499 6	0.110 4	0.522 3	0.000 3	0.000 7	0.000 1	0.318 11	0.427 7	0.455 15	0.743 11	0.765 13	0.175 11	0.842 4	0.000 3	0.828 5	0.204 4	0.033 6	0.429 11	0.335 6	0.601 2	0.312 3	0.000 9	0.357 10	0.000 1	0.000 3	0.047 11	0.423 9	0.000 11	0.000 1	0.105 9	0.000 3	0.873 9	0.079 9	0.670 12	0.000 7	0.117 5	0.471 13	0.432 3	0.829 11	0.000 1	0.000 2	0.584 2	0.417 17	0.089 6	0.684 9	0.837 12	0.705 16	0.021 12	0.178 11	0.892 6	0.000 1	0.028 8	0.505 13	0.000 7	0.457 9	0.200 14	0.662 4	0.412 9	0.244 15	0.496 8	0.000 17	0.451 8	0.626 9	0.000 7	0.102 11	0.943 9	0.138 13	0.000 1	0.000 12	0.149 8	0.291 3	0.534 9	0.722 7	0.632 7	0.331 10	0.253 14	0.453 11	0.487 11	0.000 4	0.000 1	0.479 6	0.000 11	0.022 13	0.000 12	0.000 4	0.900 10	0.128 11	0.684 3	0.164 10	0.413 4	0.854 10	0.000 12	0.000 1	0.512 16	0.074 15	0.003 11	0.000 4	0.000 1	0.000 11	0.469 15	0.613 12	0.132 8	0.529 7	0.871 3	0.227 16	0.582 7	0.026 17	0.787 12	0.000 6	0.339 15	0.000 1	0.000 8	0.000 3	0.626 7	0.000 5	0.029 8	0.000 1	0.587 9	0.612 8	0.411 7	0.724 10	0.000 10	0.000 1	0.407 6	0.552 5	0.513 3	0.849 10	0.655 4	0.408 4	0.000 11	0.296 2	0.000 1	0.686 15	0.645 14	0.145 8	0.022 8	0.414 14	0.633 11	0.000 1	0.637 2	0.224 3	0.000 7	0.000 1	0.650 8	0.000 2	0.000 1	0.622 9	0.535 12	0.343 12	0.483 3	0.230 13	0.943 10	0.289 10	0.618 7	0.596 5	0.140 8	0.679 8	0.000 1	0.022 6	0.783 11	0.620 12	0.906 1	0.000 1	0.806 8	0.137 10	0.865 5	0.000 1	0.378 12	0.000 15	0.168 15	0.680 8	0.227 13
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-F.T.	0.332 12	0.556 6	0.270 7	0.123 14	0.519 4	0.091 7	0.349 4	0.000 3	0.000 7	0.000 1	0.339 9	0.383 10	0.498 10	0.833 4	0.807 4	0.241 4	0.584 9	0.000 3	0.755 7	0.124 8	0.000 9	0.608 3	0.330 8	0.530 9	0.314 2	0.000 9	0.374 8	0.000 1	0.000 3	0.197 5	0.459 7	0.000 11	0.000 1	0.117 6	0.000 3	0.876 7	0.095 2	0.682 9	0.000 7	0.086 8	0.518 7	0.433 2	0.930 4	0.000 1	0.000 2	0.563 3	0.542 14	0.077 7	0.715 4	0.858 11	0.756 5	0.008 16	0.171 12	0.874 8	0.000 1	0.039 7	0.550 11	0.000 7	0.545 5	0.256 8	0.657 8	0.453 4	0.351 10	0.449 11	0.213 6	0.392 12	0.611 11	0.000 7	0.037 15	0.946 6	0.138 13	0.000 1	0.000 12	0.063 11	0.308 2	0.537 8	0.796 5	0.673 4	0.323 11	0.392 10	0.400 14	0.509 7	0.000 4	0.000 1	0.649 1	0.000 11	0.023 12	0.000 12	0.000 4	0.914 6	0.002 16	0.506 16	0.163 11	0.359 8	0.872 5	0.000 12	0.000 1	0.623 7	0.112 6	0.001 12	0.000 4	0.000 1	0.021 9	0.753 5	0.565 15	0.150 4	0.579 4	0.806 9	0.267 9	0.616 4	0.042 14	0.783 13	0.000 6	0.374 11	0.000 1	0.000 8	0.000 3	0.620 8	0.000 5	0.000 9	0.000 1	0.572 13	0.634 5	0.350 9	0.792 5	0.000 10	0.000 1	0.376 9	0.535 6	0.378 6	0.855 7	0.672 3	0.074 13	0.000 11	0.185 10	0.000 1	0.727 12	0.660 12	0.076 17	0.000 10	0.432 12	0.646 10	0.000 1	0.594 8	0.006 13	0.000 7	0.000 1	0.658 7	0.000 2	0.000 1	0.661 4	0.549 10	0.300 14	0.291 8	0.045 14	0.942 11	0.304 8	0.600 8	0.572 7	0.135 12	0.695 5	0.000 1	0.008 9	0.793 9	0.942 2	0.899 2	0.000 1	0.816 6	0.181 7	0.897 2	0.000 1	0.679 4	0.223 8	0.264 5	0.691 5	0.345 12
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
L3DETR-ScanNet_200	0.336 8	0.533 11	0.279 6	0.155 10	0.508 5	0.073 11	0.101 17	0.000 3	0.058 6	0.000 1	0.294 14	0.233 14	0.548 4	0.927 1	0.788 10	0.264 2	0.463 11	0.000 3	0.638 12	0.098 13	0.014 7	0.411 12	0.226 13	0.525 10	0.225 9	0.010 7	0.397 6	0.000 1	0.000 3	0.192 6	0.380 14	0.598 6	0.000 1	0.117 6	0.000 3	0.883 6	0.082 8	0.689 4	0.000 7	0.032 17	0.549 6	0.417 4	0.910 5	0.000 1	0.000 2	0.448 8	0.613 9	0.000 10	0.697 7	0.960 3	0.759 4	0.158 2	0.293 3	0.883 7	0.000 1	0.312 3	0.583 4	0.079 4	0.422 11	0.068 17	0.660 7	0.418 7	0.298 12	0.430 12	0.114 11	0.526 5	0.776 3	0.051 3	0.679 3	0.946 6	0.152 7	0.000 1	0.183 8	0.000 15	0.211 8	0.511 10	0.409 16	0.565 12	0.355 8	0.448 8	0.512 5	0.557 3	0.000 4	0.000 1	0.420 9	0.000 11	0.007 17	0.104 6	0.000 4	0.125 17	0.330 3	0.514 15	0.146 12	0.321 13	0.860 8	0.174 11	0.000 1	0.629 6	0.075 14	0.000 14	0.000 4	0.000 1	0.002 10	0.671 8	0.712 7	0.141 6	0.339 12	0.856 4	0.261 12	0.529 10	0.067 10	0.835 6	0.000 6	0.369 12	0.000 1	0.259 2	0.000 3	0.629 6	0.000 5	0.487 1	0.000 1	0.579 11	0.646 4	0.107 17	0.720 11	0.122 7	0.000 1	0.333 14	0.505 10	0.303 9	0.908 3	0.503 13	0.565 2	0.074 8	0.324 1	0.000 1	0.740 8	0.661 11	0.109 13	0.000 10	0.427 13	0.563 17	0.000 1	0.579 11	0.108 8	0.000 7	0.000 1	0.664 6	0.000 2	0.000 1	0.641 7	0.539 11	0.416 7	0.515 2	0.256 11	0.940 12	0.312 6	0.209 17	0.620 3	0.138 11	0.636 11	0.000 1	0.000 12	0.775 13	0.861 5	0.765 12	0.000 1	0.801 9	0.119 11	0.860 8	0.000 1	0.687 2	0.001 14	0.192 14	0.679 9	0.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
GSTran	0.334 10	0.533 12	0.250 12	0.179 8	0.487 8	0.041 16	0.139 13	0.003 1	0.273 5	0.000 1	0.273 16	0.189 16	0.465 12	0.704 14	0.794 8	0.198 8	0.831 5	0.000 3	0.712 8	0.055 16	0.063 5	0.518 6	0.306 9	0.459 16	0.217 10	0.028 4	0.282 14	0.000 1	0.000 3	0.044 12	0.405 11	0.558 8	0.000 1	0.080 12	0.000 3	0.873 9	0.020 16	0.684 7	0.000 7	0.075 12	0.496 9	0.363 9	0.651 15	0.000 1	0.000 2	0.425 9	0.648 5	0.000 10	0.669 11	0.914 6	0.741 10	0.009 15	0.200 9	0.864 9	0.000 1	0.000 11	0.560 9	0.000 7	0.357 14	0.233 12	0.633 11	0.408 11	0.411 4	0.320 16	0.242 5	0.440 9	0.598 14	0.047 4	0.205 8	0.940 10	0.139 11	0.000 1	0.372 4	0.138 9	0.191 10	0.495 11	0.618 13	0.624 9	0.321 12	0.595 4	0.496 7	0.499 8	0.000 4	0.000 1	0.340 12	0.014 6	0.032 7	0.136 4	0.000 4	0.903 8	0.279 5	0.601 9	0.198 5	0.345 11	0.849 11	0.260 6	0.000 1	0.573 14	0.072 17	0.060 5	0.000 4	0.000 1	0.089 7	0.838 4	0.775 4	0.125 11	0.381 11	0.752 13	0.274 5	0.517 13	0.032 15	0.841 5	0.000 6	0.354 14	0.000 1	0.047 6	0.000 3	0.439 13	0.787 3	0.252 4	0.000 1	0.512 16	0.507 16	0.158 16	0.717 12	0.000 10	0.000 1	0.337 12	0.483 13	0.570 1	0.853 8	0.614 7	0.121 11	0.070 9	0.229 7	0.000 1	0.732 11	0.773 2	0.193 3	0.000 10	0.606 7	0.791 5	0.000 1	0.593 9	0.000 14	0.010 5	0.000 1	0.574 16	0.000 2	0.000 1	0.507 12	0.554 9	0.361 11	0.136 13	0.608 3	0.948 7	0.304 8	0.593 10	0.533 8	0.011 16	0.634 12	0.000 1	0.060 3	0.821 4	0.613 13	0.797 10	0.000 1	0.799 11	0.036 13	0.782 14	0.000 1	0.609 7	0.423 3	0.133 17	0.647 12	0.213 15

PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 3	0.520 3	0.109 5	0.108 16	0.000 3	0.337 1	0.000 1	0.310 12	0.394 9	0.494 11	0.753 9	0.848 2	0.256 3	0.717 8	0.000 3	0.842 4	0.192 5	0.065 3	0.449 10	0.346 4	0.546 6	0.190 13	0.000 9	0.384 7	0.000 1	0.000 3	0.218 4	0.505 2	0.791 3	0.000 1	0.136 4	0.000 3	0.903 2	0.073 12	0.687 6	0.000 7	0.168 2	0.551 5	0.387 7	0.941 3	0.000 1	0.000 2	0.397 12	0.654 3	0.000 10	0.714 5	0.759 15	0.752 7	0.118 4	0.264 4	0.926 3	0.000 1	0.048 6	0.575 5	0.000 7	0.597 2	0.366 2	0.755 1	0.469 2	0.474 3	0.798 2	0.140 10	0.617 3	0.692 7	0.000 7	0.592 4	0.971 2	0.188 4	0.000 1	0.133 9	0.593 2	0.349 1	0.650 3	0.717 8	0.699 3	0.455 2	0.790 2	0.523 4	0.636 1	0.301 1	0.000 1	0.622 2	0.000 11	0.017 15	0.259 3	0.000 4	0.921 3	0.337 1	0.733 2	0.210 4	0.514 2	0.860 8	0.407 1	0.000 1	0.688 2	0.109 8	0.000 14	0.000 4	0.000 1	0.151 5	0.671 8	0.782 2	0.115 13	0.641 2	0.903 2	0.349 1	0.616 4	0.088 7	0.832 8	0.000 6	0.480 2	0.000 1	0.428 1	0.000 3	0.497 10	0.000 5	0.000 9	0.000 1	0.662 3	0.690 2	0.612 1	0.828 1	0.575 1	0.000 1	0.404 7	0.644 2	0.325 7	0.887 4	0.728 1	0.009 16	0.134 7	0.026 17	0.000 1	0.761 3	0.731 4	0.172 6	0.077 4	0.528 8	0.727 7	0.000 1	0.603 5	0.220 5	0.022 3	0.000 1	0.740 1	0.000 2	0.000 1	0.661 4	0.586 2	0.566 4	0.436 4	0.531 5	0.978 3	0.457 2	0.708 3	0.583 6	0.141 7	0.748 3	0.000 1	0.026 5	0.822 3	0.871 4	0.879 5	0.000 1	0.851 2	0.405 2	0.914 1	0.000 1	0.682 3	0.000 15	0.281 4	0.738 3	0.463 6
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV2 ScanNet200	0.346 6	0.552 8	0.270 8	0.175 9	0.497 7	0.070 12	0.239 7	0.000 3	0.000 7	0.000 1	0.232 17	0.412 8	0.584 2	0.842 3	0.804 5	0.212 7	0.540 10	0.000 3	0.433 16	0.106 10	0.000 9	0.590 5	0.290 12	0.548 5	0.243 7	0.000 9	0.356 11	0.000 1	0.000 3	0.062 10	0.398 13	0.441 10	0.000 1	0.104 10	0.000 3	0.888 5	0.076 11	0.682 9	0.030 3	0.094 7	0.491 11	0.351 12	0.869 10	0.000 1	0.063 1	0.403 11	0.700 2	0.000 10	0.660 13	0.881 9	0.761 3	0.050 8	0.186 10	0.852 13	0.000 1	0.007 9	0.570 8	0.100 2	0.565 3	0.326 6	0.641 10	0.431 6	0.290 14	0.621 6	0.259 3	0.408 11	0.622 10	0.125 2	0.082 12	0.950 5	0.179 5	0.000 1	0.263 6	0.424 5	0.193 9	0.558 7	0.880 4	0.545 13	0.375 7	0.727 3	0.445 12	0.499 8	0.000 4	0.000 1	0.475 7	0.002 9	0.034 6	0.083 8	0.000 4	0.924 2	0.290 4	0.636 6	0.115 14	0.400 5	0.874 4	0.186 10	0.000 1	0.611 8	0.128 3	0.113 2	0.000 4	0.000 1	0.000 11	0.584 12	0.636 10	0.103 14	0.385 10	0.843 6	0.283 4	0.603 6	0.080 8	0.825 10	0.000 6	0.377 10	0.000 1	0.000 8	0.000 3	0.457 11	0.000 5	0.000 9	0.000 1	0.574 12	0.608 9	0.481 4	0.792 5	0.394 5	0.000 1	0.357 10	0.503 11	0.261 10	0.817 13	0.504 12	0.304 7	0.472 4	0.115 11	0.000 1	0.750 7	0.677 9	0.202 2	0.000 10	0.509 9	0.729 6	0.000 1	0.519 12	0.000 14	0.000 7	0.000 1	0.620 12	0.000 2	0.000 1	0.660 6	0.560 7	0.486 6	0.384 6	0.346 10	0.952 5	0.247 14	0.667 4	0.436 12	0.269 3	0.691 6	0.000 1	0.010 7	0.787 10	0.889 3	0.880 4	0.000 1	0.810 7	0.336 4	0.860 8	0.000 1	0.606 8	0.009 11	0.248 9	0.681 7	0.392 9
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
ODIN - Sem200	0.368 4	0.562 4	0.297 4	0.207 4	0.380 17	0.196 1	0.828 2	0.000 3	0.321 2	0.000 1	0.400 5	0.775 1	0.460 13	0.501 17	0.769 12	0.065 15	0.870 3	0.000 3	0.913 1	0.213 3	0.000 9	0.000 17	0.389 2	0.554 4	0.312 3	0.000 9	0.591 1	0.000 1	0.000 3	0.491 1	0.487 3	0.894 2	0.000 1	0.378 2	0.303 1	0.796 17	0.088 6	0.669 13	0.081 1	0.216 1	0.256 17	0.334 13	0.898 7	0.000 1	0.000 2	0.370 14	0.599 10	0.000 10	0.581 16	0.988 2	0.749 8	0.090 6	0.242 5	0.921 4	0.000 1	0.202 5	0.609 2	0.000 7	0.655 1	0.214 13	0.654 9	0.346 15	0.408 7	0.485 9	0.169 8	0.631 2	0.704 6	0.000 7	0.814 1	0.940 10	0.127 16	0.000 1	0.000 12	0.462 4	0.227 6	0.641 4	0.885 3	0.657 5	0.434 3	0.000 17	0.550 2	0.393 15	0.000 4	0.000 1	0.590 4	0.000 11	0.048 2	0.077 9	0.000 4	0.784 16	0.131 10	0.557 10	0.316 2	0.359 8	0.833 14	0.373 2	0.000 1	0.661 4	0.108 9	0.001 12	0.000 4	0.000 1	0.301 3	0.612 11	0.565 15	0.129 10	0.482 8	0.468 16	0.274 5	0.561 8	0.376 1	0.912 2	0.181 1	0.440 6	0.000 1	0.166 4	0.000 3	0.641 5	0.000 5	0.426 2	0.000 1	0.642 5	0.626 7	0.259 11	0.787 8	0.429 4	0.000 1	0.589 1	0.523 8	0.246 11	0.857 6	0.000 17	0.228 9	0.000 11	0.265 4	0.000 1	0.752 6	0.832 1	0.090 16	0.157 1	0.791 1	0.578 16	0.000 1	0.373 15	0.539 1	0.000 7	0.000 1	0.685 5	0.000 2	0.000 1	0.632 8	0.575 3	0.663 1	0.152 11	0.358 9	0.926 13	0.397 3	0.454 15	0.610 4	0.119 15	0.685 7	0.000 1	0.000 12	0.803 8	0.740 9	0.441 14	0.000 1	0.800 10	0.000 17	0.871 3	0.000 1	0.220 17	0.487 1	0.862 1	0.682 6	0.054 17
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024
ALS-MinkowskiNet	0.414 2	0.610 2	0.322 3	0.271 2	0.542 2	0.153 3	0.159 11	0.000 3	0.000 7	0.000 1	0.404 4	0.503 5	0.532 6	0.672 16	0.804 5	0.285 1	0.888 2	0.000 3	0.900 2	0.226 2	0.087 2	0.598 4	0.342 5	0.671 1	0.217 10	0.087 3	0.449 4	0.000 1	0.000 3	0.253 3	0.477 6	1.000 1	0.000 1	0.118 5	0.000 3	0.905 1	0.071 13	0.710 2	0.076 2	0.047 16	0.665 1	0.376 8	0.981 1	0.000 1	0.000 2	0.466 7	0.632 7	0.113 4	0.769 1	0.956 4	0.795 2	0.031 9	0.314 1	0.936 1	0.000 1	0.390 2	0.601 3	0.000 7	0.458 8	0.366 2	0.719 3	0.440 5	0.564 1	0.699 4	0.314 1	0.464 7	0.784 2	0.200 1	0.283 6	0.973 1	0.142 9	0.000 1	0.250 7	0.285 6	0.220 7	0.718 1	0.752 6	0.723 2	0.460 1	0.248 15	0.475 10	0.463 13	0.000 4	0.000 1	0.446 8	0.021 5	0.025 11	0.285 1	0.000 4	0.972 1	0.149 8	0.769 1	0.230 3	0.535 1	0.879 2	0.252 8	0.000 1	0.693 1	0.129 2	0.000 14	0.000 4	0.000 1	0.447 1	0.958 1	0.662 9	0.159 2	0.598 3	0.780 11	0.344 2	0.646 3	0.106 6	0.893 3	0.135 3	0.455 3	0.000 1	0.194 3	0.259 1	0.726 3	0.475 4	0.000 9	0.000 1	0.741 1	0.865 1	0.571 2	0.817 3	0.445 3	0.000 1	0.506 2	0.630 3	0.230 12	0.916 2	0.728 1	0.635 1	1.000 1	0.252 6	0.000 1	0.804 2	0.697 7	0.137 11	0.043 7	0.717 2	0.807 3	0.000 1	0.510 13	0.245 2	0.000 7	0.000 1	0.709 3	0.000 2	0.000 1	0.703 2	0.572 4	0.646 2	0.223 10	0.531 5	0.984 1	0.397 3	0.813 1	0.798 1	0.135 12	0.800 1	0.000 1	0.097 2	0.832 2	0.752 8	0.842 7	0.000 1	0.852 1	0.149 9	0.846 10	0.000 1	0.666 5	0.359 5	0.252 8	0.777 1	0.690 2
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. CVPR 2025
BFANet ScanNet200	0.360 5	0.553 7	0.293 5	0.193 5	0.483 10	0.096 6	0.266 6	0.000 3	0.000 7	0.000 1	0.298 13	0.255 12	0.661 1	0.810 5	0.810 3	0.194 10	0.785 7	0.000 3	0.000 17	0.161 6	0.000 9	0.494 9	0.382 3	0.574 3	0.258 5	0.000 9	0.372 9	0.000 1	0.000 3	0.043 14	0.436 8	0.000 11	0.000 1	0.239 3	0.000 3	0.901 3	0.105 1	0.689 4	0.025 4	0.128 4	0.614 2	0.436 1	0.493 17	0.000 1	0.000 2	0.526 4	0.546 13	0.109 5	0.651 14	0.953 5	0.753 6	0.101 5	0.143 13	0.897 5	0.000 1	0.431 1	0.469 15	0.000 7	0.522 6	0.337 5	0.661 6	0.459 3	0.409 6	0.666 5	0.102 14	0.508 6	0.757 4	0.000 7	0.060 14	0.970 3	0.497 1	0.000 1	0.376 3	0.511 3	0.262 4	0.688 2	0.921 2	0.617 10	0.321 12	0.590 6	0.491 9	0.556 4	0.000 4	0.000 1	0.481 5	0.093 1	0.043 3	0.284 2	0.000 4	0.875 14	0.135 9	0.669 4	0.124 13	0.394 6	0.849 11	0.298 4	0.000 1	0.476 17	0.088 13	0.042 7	0.000 4	0.000 1	0.254 4	0.653 10	0.741 6	0.215 1	0.573 5	0.852 5	0.266 10	0.654 2	0.056 12	0.835 6	0.000 6	0.492 1	0.000 1	0.000 8	0.000 3	0.612 9	0.000 5	0.000 9	0.000 1	0.616 6	0.469 17	0.460 5	0.698 14	0.516 2	0.000 1	0.378 8	0.563 4	0.476 4	0.863 5	0.574 9	0.330 6	0.000 11	0.282 3	0.000 1	0.760 4	0.710 5	0.233 1	0.000 10	0.641 5	0.814 2	0.000 1	0.585 10	0.053 11	0.000 7	0.000 1	0.629 10	0.000 2	0.000 1	0.678 3	0.528 13	0.534 5	0.129 14	0.596 4	0.973 4	0.264 12	0.772 2	0.526 10	0.139 9	0.707 4	0.000 1	0.000 12	0.764 14	0.591 16	0.848 6	0.000 1	0.827 4	0.338 3	0.806 12	0.000 1	0.568 9	0.151 10	0.358 2	0.659 10	0.510 4
Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis. CVPR 2025
DITR	0.449 1	0.629 1	0.392 1	0.289 1	0.650 1	0.168 2	0.862 1	0.000 3	0.313 3	0.000 1	0.580 1	0.568 2	0.564 3	0.766 7	0.867 1	0.238 5	0.949 1	0.000 3	0.866 3	0.300 1	0.000 9	0.664 1	0.482 1	0.508 12	0.317 1	0.420 1	0.551 2	0.000 1	0.000 3	0.486 2	0.519 1	0.662 4	0.000 1	0.385 1	0.000 3	0.901 3	0.079 9	0.727 1	0.000 7	0.160 3	0.606 3	0.417 4	0.967 2	0.000 1	0.000 2	0.498 5	0.596 11	0.130 2	0.728 3	0.998 1	0.805 1	0.000 17	0.314 1	0.934 2	0.000 1	0.278 4	0.636 1	0.000 7	0.403 12	0.367 1	0.741 2	0.484 1	0.500 2	1.000 1	0.113 12	0.828 1	0.815 1	0.000 7	0.733 2	0.969 4	0.374 2	0.000 1	0.579 1	1.000 1	0.230 5	0.617 5	0.983 1	0.729 1	0.423 4	0.855 1	0.508 6	0.622 2	0.018 3	0.000 1	0.591 3	0.034 4	0.028 10	0.066 11	0.869 1	0.904 7	0.334 2	0.651 5	0.716 1	0.514 2	0.871 6	0.315 3	0.000 1	0.664 3	0.128 3	0.014 10	0.000 4	0.000 1	0.392 2	0.851 2	0.817 1	0.153 3	0.823 1	0.991 1	0.318 3	0.680 1	0.134 3	0.913 1	0.157 2	0.448 4	0.000 1	0.000 8	0.000 3	0.826 1	0.978 1	0.091 6	0.000 1	0.660 4	0.647 3	0.571 2	0.804 4	0.001 9	0.000 1	0.480 3	0.700 1	0.421 5	0.947 1	0.433 14	0.411 3	0.148 6	0.262 5	0.000 1	0.849 1	0.709 6	0.138 10	0.150 2	0.714 3	0.889 1	0.000 1	0.698 1	0.222 4	0.000 7	0.000 1	0.720 2	0.000 2	0.000 1	0.805 1	0.600 1	0.642 3	0.268 9	0.904 1	0.982 2	0.477 1	0.632 6	0.718 2	0.139 9	0.776 2	0.000 1	0.178 1	0.886 1	0.962 1	0.839 8	0.000 1	0.851 2	0.043 12	0.869 4	0.000 1	0.710 1	0.315 6	0.348 3	0.753 2	0.397 8
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation.
Minkowski 34D	0.253 16	0.463 16	0.154 17	0.102 16	0.381 16	0.084 8	0.134 15	0.000 3	0.000 7	0.000 1	0.386 7	0.141 17	0.279 17	0.737 12	0.703 16	0.014 17	0.164 15	0.000 3	0.663 10	0.092 14	0.000 9	0.224 15	0.291 11	0.531 8	0.056 17	0.000 9	0.242 16	0.000 1	0.000 3	0.013 15	0.331 16	0.000 11	0.000 1	0.035 17	0.001 2	0.858 14	0.059 14	0.650 16	0.000 7	0.056 14	0.353 15	0.299 15	0.670 13	0.000 1	0.000 2	0.284 16	0.484 15	0.071 8	0.594 15	0.720 16	0.710 15	0.027 11	0.068 17	0.813 14	0.000 1	0.005 10	0.492 14	0.164 1	0.274 16	0.111 16	0.571 16	0.307 17	0.293 13	0.307 17	0.150 9	0.163 17	0.531 16	0.002 6	0.545 5	0.932 15	0.093 17	0.000 1	0.000 12	0.002 14	0.159 15	0.368 17	0.581 15	0.440 17	0.228 17	0.406 9	0.282 17	0.294 16	0.000 4	0.000 1	0.189 16	0.060 2	0.036 5	0.000 12	0.000 4	0.897 11	0.000 17	0.525 14	0.025 17	0.205 17	0.771 17	0.000 12	0.000 1	0.593 11	0.108 9	0.044 6	0.000 4	0.000 1	0.000 11	0.282 17	0.589 14	0.094 16	0.169 16	0.466 17	0.227 16	0.419 17	0.125 5	0.757 14	0.002 4	0.334 16	0.000 1	0.000 8	0.000 3	0.357 15	0.000 5	0.000 9	0.000 1	0.582 10	0.513 14	0.337 10	0.612 17	0.000 10	0.000 1	0.250 16	0.352 17	0.136 17	0.724 16	0.655 4	0.280 8	0.000 11	0.046 16	0.000 1	0.606 17	0.559 15	0.159 7	0.102 3	0.445 10	0.655 9	0.000 1	0.310 17	0.117 6	0.000 7	0.000 1	0.581 15	0.026 1	0.000 1	0.265 17	0.483 16	0.084 17	0.097 17	0.044 15	0.865 17	0.142 17	0.588 11	0.351 15	0.272 2	0.596 17	0.000 1	0.003 10	0.622 16	0.720 10	0.096 17	0.000 1	0.771 16	0.016 15	0.772 15	0.000 1	0.302 14	0.194 9	0.214 12	0.621 16	0.197 16
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019

This table lists the benchmark results for the ScanNet200 3D semantic instance scenario.

Method	avg	alarm clock	armchair	backpack	bar	basket	bathroom cabinet	bathroom counter	bathroom stall	bathroom stall door	bathtub	bed	bench	bicycle	bin	blackboard	blanket	blinds	board	book	bookshelf	bottle	bowl	box	broom	bucket	bulletin board	cabinet	calendar	candle	cart	case of water bottles	cd case	ceiling	ceiling light	clock	closet	closet door	closet rod	closet wall	clothes dryer	coffee kettle	coffee maker	coffee table	column	computer tower	container	copier	couch	crate	cup	curtain	decoration	desk	dining table	dish rack	dishwasher	divider	door	doorframe	dumbbell	dustpan	end table	fan	fire alarm	fire extinguisher	fireplace	folded chair	furniture	guitar case	jacket	keyboard	keyboard piano	kitchen cabinet	kitchen counter	ladder	lamp	laptop	laundry basket	laundry detergent	laundry hamper	ledge	light	light switch	luggage	machine	mailbox	mattress	microwave	mini fridge	monitor	mouse	music stand	nightstand	object	office chair	ottoman	oven	paper	paper cutter	paper towel dispenser	paper towel roll	person	piano	picture	pillar	pillow	pipe	plate	plunger	poster	potted plant	power outlet	power strip	projector	projector screen	purse	rack	radiator	range hood	recycling bin	scale	seat	shelf	shoe	shower	shower curtain	shower curtain rod	shower door	shower floor	shower head	sign	sink	soap dish	soap dispenser	sofa chair	stair rail	stairs	stand	stool	storage bin	storage container	storage organizer	stove	structure	stuffed animal	suitcase	table	telephone	tissue box	toaster	toaster oven	toilet	toilet paper	toilet paper holder	toilet seat cover dispenser	towel	tray	tube	tv	tv stand	vacuum cleaner	vent	wardrobe	washing machine	water bottle	water cooler	water pitcher	whiteboard	window	windowsill

Mask3D Scannet200	0.388 1	0.542 1	0.357 2	0.237 2	0.610 1	0.091 2	0.125 6	0.000 1	0.000 2	0.000 1	0.065 3	0.668 1	0.451 1	1.000 1	0.955 1	0.640 1	0.500 2	0.039 1	0.125 3	0.063 3	0.409 1	0.311 2	0.291 1	0.609 4	0.266 2	0.000 1	0.163 2	0.000 1	0.008 1	0.044 3	0.496 2	1.000 1	0.000 1	0.018 3	0.000 2	0.756 1	0.573 1	0.808 2	0.000 2	0.010 2	0.042 4	0.130 4	0.552 2	0.042 1	0.000 1	1.000 1	0.725 4	0.750 1	0.883 1	1.000 1	0.832 4	0.024 3	0.107 2	0.614 3	0.226 1	0.250 1	0.628 2	0.792 1	0.677 3	0.400 1	0.741 2	0.278 2	0.511 2	0.077 6	0.111 2	0.313 3	0.715 2	0.302 1	0.017 4	0.200 2	0.000 1	0.188 1	0.000 2	0.178 3	0.736 2	1.000 1	0.615 1	0.514 1	0.409 2	0.380 6	0.600 1	0.000 1	0.000 1	0.400 2	0.013 2	0.254 1	0.381 1	0.000 1	0.123 5	0.400 1	0.839 2	0.258 2	0.463 1	0.926 1	0.265 2	0.000 1	0.857 2	0.099 1	0.021 2	0.500 1	0.027 1	0.028 2	1.000 1	0.502 6	0.016 2	0.076 5	0.500 1	0.612 1	0.578 1	0.005 3	0.597 3	0.194 2	0.497 1	0.000 1	0.500 1	0.000 2	0.323 5	0.000 1	1.000 1	0.000 1	0.748 1	0.708 2	0.050 5	0.890 2	1.000 1	0.008 2	0.151 4	0.301 2	1.000 1	1.000 1	0.792 3	0.945 1	1.000 1	0.511 1	0.004 2	0.753 1	0.776 3	0.287 2	0.020 2	0.003 5	0.974 3	0.033 1	0.412 6	0.000 2	0.000 2	0.000 2	0.667 2	0.000 1	0.000 1	0.491 2	0.676 2	0.352 2	0.335 1	0.060 3	0.822 6	0.527 3	1.000 1	0.517 2	0.606 1	0.853 2	0.000 1	0.004 1	0.806 1	1.000 1	0.727 1	0.000 1	0.042 2	0.739 2	0.000 1	0.399 3	0.391 1	0.504 2	0.591 1	0.571 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
TD3D Scannet200	0.320 3	0.501 3	0.264 3	0.164 3	0.506 3	0.062 3	0.500 1	0.000 1	0.000 2	0.000 1	0.208 1	0.431 3	0.252 4	1.000 1	0.733 4	0.587 2	0.000 3	0.008 2	0.000 4	0.106 1	0.000 2	0.356 1	0.123 5	0.686 1	0.101 3	0.000 1	0.152 3	0.000 1	0.000 2	0.226 2	0.280 4	0.000 3	0.000 1	0.250 2	0.000 2	0.619 2	0.061 4	0.841 1	0.000 2	0.000 3	0.167 2	0.194 2	0.333 3	0.000 2	0.000 1	0.667 2	0.820 1	0.250 3	0.790 4	1.000 1	0.879 2	0.077 1	0.094 4	0.708 1	0.217 2	0.049 3	0.634 1	0.792 1	0.331 5	0.033 6	0.716 3	0.159 3	0.396 3	0.331 5	0.099 3	0.415 1	0.842 1	0.000 2	0.458 2	0.542 1	0.000 1	0.101 2	0.000 2	0.218 2	0.513 3	0.500 3	0.458 3	0.104 3	0.516 1	0.456 1	0.268 5	0.000 1	0.000 1	0.400 2	0.022 1	0.233 2	0.143 3	0.000 1	0.677 1	0.400 1	0.504 6	0.095 4	0.083 6	0.890 2	0.061 3	0.000 1	0.906 1	0.076 2	0.231 1	0.125 3	0.000 2	0.003 3	0.792 4	0.881 1	0.000 3	0.098 4	0.125 5	0.498 5	0.459 3	0.063 1	0.715 2	0.000 3	0.241 4	0.000 1	0.396 2	0.063 1	0.605 2	0.000 1	0.000 3	0.000 1	0.448 6	0.629 4	0.202 3	0.967 1	0.250 3	0.038 1	0.192 2	0.185 3	0.083 4	1.000 1	1.000 1	0.857 2	0.000 2	0.470 2	0.012 1	0.565 4	0.798 2	0.621 1	0.111 1	0.500 1	1.000 1	0.017 2	0.509 2	0.000 2	0.008 1	1.000 1	0.525 3	0.000 1	0.000 1	0.332 4	0.679 1	0.264 3	0.333 2	0.267 1	1.000 1	0.549 2	0.299 6	0.387 3	0.328 3	0.744 5	0.000 1	0.000 2	0.435 6	1.000 1	0.283 5	0.000 1	0.196 1	0.817 1	0.000 1	0.472 1	0.222 4	0.123 5	0.560 2	0.156 3
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
ODIN - Ins200	0.381 2	0.507 2	0.375 1	0.237 1	0.484 4	0.108 1	0.500 1	0.000 1	0.125 1	0.000 1	0.058 4	0.647 2	0.385 2	0.667 4	0.853 2	0.542 3	1.000 1	0.000 3	1.000 1	0.093 2	0.000 2	0.028 5	0.274 2	0.682 2	0.550 1	0.000 1	0.269 1	0.000 1	0.000 2	0.714 1	0.566 1	1.000 1	0.000 1	0.500 1	0.125 1	0.585 3	0.066 3	0.653 6	0.083 1	0.049 1	0.264 1	0.227 1	0.667 1	0.000 2	0.000 1	0.278 6	0.723 5	0.250 3	0.786 5	1.000 1	0.744 6	0.039 2	0.209 1	0.494 5	0.000 3	0.250 1	0.446 3	0.500 3	0.750 1	0.200 3	0.780 1	0.333 1	0.602 1	0.469 3	0.163 1	0.406 2	0.530 4	0.000 2	0.668 1	0.200 2	0.000 1	0.000 3	0.500 1	0.313 1	0.769 1	1.000 1	0.511 2	0.196 2	0.286 3	0.393 5	0.337 2	0.000 1	0.000 1	0.600 1	0.000 3	0.174 3	0.226 2	0.000 1	0.579 2	0.200 3	0.887 1	0.750 1	0.428 2	0.782 3	0.438 1	0.000 1	0.795 3	0.063 3	0.003 3	0.500 1	0.000 2	0.333 1	1.000 1	0.742 2	0.083 1	0.585 1	0.417 4	0.448 6	0.496 2	0.055 2	0.734 1	0.472 1	0.174 5	0.000 1	0.250 3	0.000 2	0.688 1	0.000 1	1.000 1	0.000 1	0.631 3	0.667 3	0.275 1	0.694 6	1.000 1	0.000 3	0.328 1	0.422 1	0.000 5	1.000 1	0.500 4	0.638 3	0.000 2	0.391 3	0.000 3	0.582 3	0.800 1	0.208 5	0.000 3	0.246 2	0.667 5	0.000 3	0.638 1	0.167 1	0.000 2	0.000 2	0.778 1	0.000 1	0.000 1	0.563 1	0.614 3	0.841 1	0.333 2	0.250 2	0.938 5	0.569 1	0.500 4	0.695 1	0.264 4	0.863 1	0.000 1	0.000 2	0.550 5	1.000 1	0.668 2	0.000 1	0.000 3	0.667 3	0.000 1	0.333 4	0.333 2	0.665 1	0.434 3	0.264 2

Minkowski 34D Inst.	0.203 6	0.369 5	0.134 6	0.078 6	0.479 5	0.003 5	0.500 1	0.000 1	0.000 2	0.000 1	0.100 2	0.371 4	0.300 3	0.667 4	0.746 3	0.400 4	0.000 3	0.000 3	0.000 4	0.031 4	0.000 2	0.074 4	0.165 4	0.413 6	0.000 5	0.000 1	0.070 5	0.000 1	0.000 2	0.000 4	0.221 6	0.000 3	0.000 1	0.000 4	0.000 2	0.372 6	0.070 2	0.706 4	0.000 2	0.000 3	0.000 6	0.123 5	0.033 6	0.000 2	0.000 1	0.422 5	0.732 3	0.000 5	0.778 6	1.000 1	0.845 3	0.000 4	0.090 5	0.636 2	0.000 3	0.000 4	0.158 5	0.000 4	0.250 6	0.050 5	0.693 4	0.123 5	0.051 6	0.385 4	0.009 5	0.118 6	0.406 6	0.000 2	0.000 5	0.200 2	0.000 1	0.000 3	0.000 2	0.133 5	0.307 6	0.500 3	0.251 5	0.000 5	0.281 4	0.402 4	0.317 3	0.000 1	0.000 1	0.000 4	0.000 3	0.060 5	0.000 4	0.000 1	0.396 3	0.200 3	0.669 3	0.021 5	0.218 5	0.720 6	0.000 4	0.000 1	0.696 4	0.025 5	0.000 4	0.000 4	0.000 2	0.000 4	0.125 6	0.596 3	0.000 3	0.191 2	0.500 1	0.595 2	0.369 5	0.000 4	0.500 5	0.000 3	0.143 6	0.000 1	0.000 4	0.000 2	0.226 6	0.000 1	0.000 3	0.000 1	0.701 2	0.511 5	0.000 6	0.851 4	0.000 4	0.000 3	0.150 5	0.052 6	0.100 3	0.981 4	0.500 4	0.286 4	0.000 2	0.000 6	0.000 3	0.545 5	0.522 6	0.250 3	0.000 3	0.000 6	0.522 6	0.000 3	0.500 3	0.000 2	0.000 2	0.000 2	0.282 6	0.000 1	0.000 1	0.178 6	0.382 5	0.018 6	0.056 5	0.000 4	0.997 3	0.107 6	0.677 2	0.313 5	0.000 5	0.726 6	0.000 1	0.000 2	0.583 4	0.903 5	0.200 6	0.000 1	0.000 3	0.333 5	0.000 1	0.442 2	0.083 5	0.109 6	0.387 5	0.000 6
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.	0.209 5	0.361 6	0.157 5	0.085 5	0.506 2	0.007 4	0.500 1	0.000 1	0.000 2	0.000 1	0.000 6	0.093 6	0.221 5	0.667 4	0.524 6	0.400 4	0.000 3	0.000 3	0.000 4	0.004 5	0.000 2	0.000 6	0.109 6	0.589 5	0.000 5	0.000 1	0.059 6	0.000 1	0.000 2	0.000 4	0.322 3	0.000 3	0.000 1	0.000 4	0.000 2	0.405 4	0.055 5	0.700 5	0.000 2	0.000 3	0.028 5	0.091 6	0.083 4	0.000 2	0.000 1	0.667 2	0.768 2	0.000 5	0.807 3	1.000 1	0.776 5	0.000 4	0.000 6	0.340 6	0.000 3	0.000 4	0.103 6	0.000 4	0.750 1	0.200 3	0.634 6	0.053 6	0.246 4	0.677 2	0.006 6	0.198 4	0.432 5	0.000 2	0.000 5	0.050 5	0.000 1	0.000 3	0.000 2	0.111 6	0.356 5	0.500 3	0.188 6	0.000 5	0.220 5	0.448 2	0.050 6	0.000 1	0.000 1	0.000 4	0.000 3	0.032 6	0.000 4	0.000 1	0.396 3	0.000 5	0.573 5	0.000 6	0.228 4	0.747 5	0.000 4	0.000 1	0.573 6	0.021 6	0.000 4	0.000 4	0.000 2	0.000 4	0.500 5	0.573 4	0.000 3	0.000 6	0.125 5	0.592 3	0.364 6	0.000 4	0.450 6	0.000 3	0.364 2	0.000 1	0.000 4	0.000 2	0.340 4	0.000 1	0.000 3	0.000 1	0.610 4	0.833 1	0.221 2	0.702 5	0.000 4	0.000 3	0.135 6	0.094 5	0.125 2	0.571 5	0.500 4	0.143 6	0.000 2	0.125 4	0.000 3	0.618 2	0.667 5	0.115 6	0.000 3	0.125 3	1.000 1	0.000 3	0.500 3	0.000 2	0.000 2	0.000 2	0.502 5	0.000 1	0.000 1	0.312 5	0.248 6	0.050 5	0.000 6	0.000 4	0.997 3	0.420 4	0.500 4	0.149 6	0.451 2	0.748 3	0.000 1	0.000 2	0.636 3	0.667 6	0.600 3	0.000 1	0.000 3	0.278 6	0.000 1	0.333 4	0.000 6	0.294 3	0.381 6	0.110 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.	0.246 4	0.413 4	0.170 4	0.130 4	0.455 6	0.003 6	0.500 1	0.000 1	0.000 2	0.000 1	0.017 5	0.333 5	0.111 6	1.000 1	0.681 5	0.400 4	0.000 3	0.000 3	1.000 1	0.003 6	0.000 2	0.167 3	0.190 3	0.637 3	0.067 4	0.000 1	0.081 4	0.000 1	0.000 2	0.000 4	0.264 5	0.000 3	0.000 1	0.000 4	0.000 2	0.387 5	0.031 6	0.754 3	0.000 2	0.000 3	0.151 3	0.135 3	0.056 5	0.000 2	0.000 1	0.582 4	0.589 6	0.500 2	0.815 2	1.000 1	0.903 1	0.000 4	0.097 3	0.588 4	0.000 3	0.000 4	0.234 4	0.000 4	0.500 4	0.400 1	0.682 5	0.156 4	0.159 5	0.750 1	0.046 4	0.125 5	0.660 3	0.000 2	0.200 3	0.000 6	0.000 1	0.000 3	0.000 2	0.164 4	0.402 4	0.500 3	0.373 4	0.025 4	0.143 6	0.426 3	0.317 3	0.000 1	0.000 1	0.000 4	0.000 3	0.063 4	0.000 4	0.000 1	0.000 6	0.000 5	0.575 4	0.250 3	0.241 3	0.772 4	0.000 4	0.000 1	0.653 5	0.034 4	0.000 4	0.000 4	0.000 2	0.000 4	1.000 1	0.561 5	0.000 3	0.100 3	0.500 1	0.541 4	0.452 4	0.000 4	0.581 4	0.000 3	0.364 2	0.000 1	0.000 4	0.000 2	0.571 3	0.000 1	0.000 3	0.000 1	0.568 5	0.511 5	0.167 4	0.857 3	0.000 4	0.000 3	0.164 3	0.112 4	0.000 5	0.530 6	1.000 1	0.286 4	0.000 2	0.125 4	0.000 3	0.464 6	0.706 4	0.208 4	0.000 3	0.125 3	0.744 4	0.000 3	0.500 3	0.000 2	0.000 2	0.000 2	0.511 4	0.000 1	0.000 1	0.344 3	0.541 4	0.068 4	0.333 2	0.000 4	1.000 1	0.196 5	0.533 3	0.318 4	0.000 5	0.748 4	0.000 1	0.000 2	0.690 2	1.000 1	0.400 4	0.000 1	0.000 3	0.667 3	0.000 1	0.333 4	0.333 2	0.270 4	0.399 4	0.083 5
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.

ScanNet Benchmark

This table lists the benchmark results for the 3D semantic label scenario.

Method	avg iou	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	floor	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	wall	window

PTv3-PPT-ALC	0.798 1	0.911 11	0.812 22	0.854 8	0.770 12	0.856 15	0.555 17	0.943 1	0.660 26	0.735 2	0.979 1	0.606 7	0.492 1	0.792 4	0.934 4	0.841 2	0.819 6	0.716 9	0.947 10	0.906 1	0.822 1
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. CVPR 2025
DITR ScanNet	0.797 2	0.727 76	0.869 1	0.882 1	0.785 6	0.868 7	0.578 5	0.943 1	0.744 1	0.727 3	0.979 1	0.627 2	0.364 9	0.824 1	0.949 2	0.779 15	0.844 1	0.757 1	0.982 1	0.905 2	0.802 3
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation.
PTv3 ScanNet	0.794 3	0.941 3	0.813 21	0.851 11	0.782 7	0.890 2	0.597 1	0.916 6	0.696 11	0.713 5	0.979 1	0.635 1	0.384 3	0.793 3	0.907 10	0.821 5	0.790 36	0.696 14	0.967 4	0.903 3	0.805 2
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV2	0.785 4	0.978 1	0.800 30	0.833 29	0.788 4	0.853 20	0.545 21	0.910 9	0.713 3	0.705 6	0.979 1	0.596 9	0.390 2	0.769 15	0.832 45	0.821 5	0.792 35	0.730 2	0.975 2	0.897 6	0.785 7
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Mix3D	0.781 5	0.964 2	0.855 2	0.843 20	0.781 8	0.858 13	0.575 8	0.831 39	0.685 17	0.714 4	0.979 1	0.594 10	0.310 30	0.801 2	0.892 19	0.841 2	0.819 6	0.723 6	0.940 15	0.887 8	0.725 28
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3D	0.779 6	0.861 23	0.818 16	0.836 26	0.790 3	0.875 4	0.576 7	0.905 10	0.704 7	0.739 1	0.969 12	0.611 3	0.349 12	0.756 25	0.958 1	0.702 51	0.805 19	0.708 10	0.916 39	0.898 5	0.801 4

TTT-KD	0.773 7	0.646 97	0.818 16	0.809 41	0.774 10	0.878 3	0.581 3	0.943 1	0.687 15	0.704 7	0.978 6	0.607 6	0.336 19	0.775 11	0.912 8	0.838 4	0.823 4	0.694 15	0.967 4	0.899 4	0.794 6
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
ResLFE_HDS	0.772 8	0.939 4	0.824 7	0.854 8	0.771 11	0.840 35	0.564 13	0.900 12	0.686 16	0.677 14	0.961 18	0.537 36	0.348 13	0.769 15	0.903 12	0.785 13	0.815 9	0.676 26	0.939 16	0.880 13	0.772 11

PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 36	0.829 31	0.751 26	0.854 18	0.540 25	0.903 11	0.630 39	0.672 17	0.963 16	0.565 26	0.357 10	0.788 5	0.900 14	0.737 31	0.802 20	0.685 20	0.950 8	0.887 8	0.780 8
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OctFormer	0.766 9	0.925 7	0.808 26	0.849 13	0.786 5	0.846 30	0.566 12	0.876 19	0.690 13	0.674 16	0.960 19	0.576 22	0.226 73	0.753 27	0.904 11	0.777 16	0.815 9	0.722 7	0.923 31	0.877 16	0.776 10
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net	0.764 11	0.924 8	0.819 14	0.840 23	0.757 21	0.853 20	0.580 4	0.848 31	0.709 5	0.643 27	0.958 23	0.587 16	0.295 38	0.753 27	0.884 23	0.758 23	0.815 9	0.725 5	0.927 27	0.867 27	0.743 19

OccuSeg+Semantic	0.764 11	0.758 61	0.796 34	0.839 24	0.746 30	0.907 1	0.562 14	0.850 30	0.680 19	0.672 17	0.978 6	0.610 4	0.335 21	0.777 9	0.819 49	0.847 1	0.830 3	0.691 17	0.972 3	0.885 10	0.727 26

O-CNN	0.762 13	0.924 8	0.823 8	0.844 19	0.770 12	0.852 22	0.577 6	0.847 33	0.711 4	0.640 31	0.958 23	0.592 11	0.217 79	0.762 20	0.888 20	0.758 23	0.813 13	0.726 4	0.932 25	0.868 26	0.744 18
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
DiffSegNet	0.758 14	0.725 78	0.789 41	0.843 20	0.762 17	0.856 15	0.562 14	0.920 4	0.657 29	0.658 21	0.958 23	0.589 14	0.337 18	0.782 6	0.879 24	0.787 11	0.779 41	0.678 22	0.926 29	0.880 13	0.799 5

DTC	0.757 15	0.843 29	0.820 12	0.847 16	0.791 2	0.862 11	0.511 38	0.870 22	0.707 6	0.652 23	0.954 40	0.604 8	0.279 49	0.760 21	0.942 3	0.734 32	0.766 50	0.701 13	0.884 61	0.874 22	0.736 20

OA-CNN-L_ScanNet20	0.756 16	0.783 47	0.826 6	0.858 6	0.776 9	0.837 39	0.548 20	0.896 15	0.649 31	0.675 15	0.962 17	0.586 17	0.335 21	0.771 14	0.802 54	0.770 19	0.787 38	0.691 17	0.936 20	0.880 13	0.761 13

ConDaFormer	0.755 17	0.927 6	0.822 10	0.836 26	0.801 1	0.849 25	0.516 35	0.864 27	0.651 30	0.680 13	0.958 23	0.584 19	0.282 46	0.759 23	0.855 35	0.728 34	0.802 20	0.678 22	0.880 66	0.873 23	0.756 16
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
LSK3DNet	0.755 17	0.899 16	0.823 8	0.843 20	0.764 16	0.838 38	0.584 2	0.845 34	0.717 2	0.638 33	0.956 30	0.580 21	0.229 72	0.640 49	0.900 14	0.750 26	0.813 13	0.729 3	0.920 35	0.872 24	0.757 14
Tuo Feng, Wenguan Wang, Fan Ma, Yi Yang: LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels. CVPR 2024
PNE	0.755 17	0.786 45	0.835 5	0.834 28	0.758 19	0.849 25	0.570 10	0.836 38	0.648 32	0.668 19	0.978 6	0.581 20	0.367 7	0.683 40	0.856 33	0.804 8	0.801 24	0.678 22	0.961 6	0.889 7	0.716 35
P. Hermosilla: Point Neighborhood Embeddings.
PointTransformerV2	0.752 20	0.742 68	0.809 25	0.872 2	0.758 19	0.860 12	0.552 18	0.891 17	0.610 46	0.687 8	0.960 19	0.559 30	0.304 33	0.766 18	0.926 6	0.767 20	0.797 28	0.644 38	0.942 13	0.876 19	0.722 31
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net	0.752 20	0.906 14	0.793 38	0.802 47	0.689 46	0.825 52	0.556 16	0.867 23	0.681 18	0.602 50	0.960 19	0.555 32	0.365 8	0.779 8	0.859 30	0.747 27	0.795 32	0.717 8	0.917 38	0.856 35	0.764 12
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointConvFormer	0.749 22	0.793 43	0.790 39	0.807 43	0.750 28	0.856 15	0.524 31	0.881 18	0.588 58	0.642 30	0.977 10	0.591 12	0.274 52	0.781 7	0.929 5	0.804 8	0.796 29	0.642 39	0.947 10	0.885 10	0.715 36
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNet	0.749 22	0.909 12	0.818 16	0.811 39	0.752 24	0.839 37	0.485 53	0.842 35	0.673 21	0.644 26	0.957 28	0.528 42	0.305 32	0.773 12	0.859 30	0.788 10	0.818 8	0.693 16	0.916 39	0.856 35	0.723 30
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP	0.748 24	0.623 100	0.804 28	0.859 5	0.745 31	0.824 54	0.501 42	0.912 8	0.690 13	0.685 10	0.956 30	0.567 25	0.320 27	0.768 17	0.918 7	0.720 39	0.802 20	0.676 26	0.921 33	0.881 12	0.779 9

StratifiedFormer	0.747 25	0.901 15	0.803 29	0.845 18	0.757 21	0.846 30	0.512 37	0.825 42	0.696 11	0.645 25	0.956 30	0.576 22	0.262 63	0.744 33	0.861 29	0.742 29	0.770 48	0.705 11	0.899 51	0.860 32	0.734 21
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion	0.746 26	0.771 55	0.819 14	0.848 15	0.702 43	0.865 10	0.397 91	0.899 13	0.699 9	0.664 20	0.948 62	0.588 15	0.330 23	0.746 32	0.851 39	0.764 21	0.796 29	0.704 12	0.935 21	0.866 28	0.728 24
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNet	0.746 26	0.870 21	0.838 3	0.858 6	0.729 36	0.850 24	0.501 42	0.874 20	0.587 59	0.658 21	0.956 30	0.564 27	0.299 35	0.765 19	0.900 14	0.716 42	0.812 15	0.631 44	0.939 16	0.858 33	0.709 37
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
DiffSeg3D2	0.745 28	0.725 78	0.814 20	0.837 25	0.751 26	0.831 46	0.514 36	0.896 15	0.674 20	0.684 11	0.960 19	0.564 27	0.303 34	0.773 12	0.820 48	0.713 45	0.798 27	0.690 19	0.923 31	0.875 20	0.757 14

ODIN	0.744 29	0.658 93	0.752 64	0.870 3	0.714 40	0.843 33	0.569 11	0.919 5	0.703 8	0.622 40	0.949 59	0.591 12	0.343 15	0.736 34	0.784 56	0.816 7	0.838 2	0.672 31	0.918 37	0.854 39	0.725 28
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024
Retro-FPN	0.744 29	0.842 30	0.800 30	0.767 61	0.740 32	0.836 41	0.541 23	0.914 7	0.672 22	0.626 37	0.958 23	0.552 33	0.272 54	0.777 9	0.886 22	0.696 52	0.801 24	0.674 29	0.941 14	0.858 33	0.717 33
Peng Xiang, Xin Wen, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
EQ-Net	0.743 31	0.620 101	0.799 33	0.849 13	0.730 35	0.822 56	0.493 50	0.897 14	0.664 23	0.681 12	0.955 34	0.562 29	0.378 4	0.760 21	0.903 12	0.738 30	0.801 24	0.673 30	0.907 43	0.877 16	0.745 17
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT	0.742 32	0.860 24	0.765 55	0.819 34	0.769 14	0.848 27	0.533 27	0.829 40	0.663 24	0.631 36	0.955 34	0.586 17	0.274 52	0.753 27	0.896 17	0.729 33	0.760 56	0.666 33	0.921 33	0.855 37	0.733 22

LRPNet	0.742 32	0.816 38	0.806 27	0.807 43	0.752 24	0.828 50	0.575 8	0.839 37	0.699 9	0.637 34	0.954 40	0.520 46	0.320 27	0.755 26	0.834 43	0.760 22	0.772 45	0.676 26	0.915 41	0.862 30	0.717 33

LargeKernel3D	0.739 34	0.909 12	0.820 12	0.806 45	0.740 32	0.852 22	0.545 21	0.826 41	0.594 57	0.643 27	0.955 34	0.541 35	0.263 62	0.723 38	0.858 32	0.775 18	0.767 49	0.678 22	0.933 23	0.848 43	0.694 42
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 35	0.776 51	0.790 39	0.851 11	0.754 23	0.854 18	0.491 52	0.866 25	0.596 56	0.686 9	0.955 34	0.536 37	0.342 16	0.624 56	0.869 26	0.787 11	0.802 20	0.628 45	0.927 27	0.875 20	0.704 39

MinkowskiNet	0.736 35	0.859 25	0.818 16	0.832 30	0.709 41	0.840 35	0.521 33	0.853 29	0.660 26	0.643 27	0.951 51	0.544 34	0.286 44	0.731 36	0.893 18	0.675 61	0.772 45	0.683 21	0.874 73	0.852 41	0.727 26
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 37	0.890 17	0.837 4	0.864 4	0.726 37	0.873 5	0.530 30	0.824 43	0.489 93	0.647 24	0.978 6	0.609 5	0.336 19	0.624 56	0.733 64	0.758 23	0.776 43	0.570 71	0.949 9	0.877 16	0.728 24

online3d	0.727 38	0.715 83	0.777 48	0.854 8	0.748 29	0.858 13	0.497 47	0.872 21	0.572 66	0.639 32	0.957 28	0.523 43	0.297 37	0.750 30	0.803 53	0.744 28	0.810 16	0.587 67	0.938 18	0.871 25	0.719 32

PointTransformer++	0.725 39	0.727 76	0.811 24	0.819 34	0.765 15	0.841 34	0.502 41	0.814 48	0.621 42	0.623 39	0.955 34	0.556 31	0.284 45	0.620 58	0.866 27	0.781 14	0.757 60	0.648 36	0.932 25	0.862 30	0.709 37

SparseConvNet	0.725 39	0.647 96	0.821 11	0.846 17	0.721 38	0.869 6	0.533 27	0.754 64	0.603 52	0.614 42	0.955 34	0.572 24	0.325 25	0.710 39	0.870 25	0.724 37	0.823 4	0.628 45	0.934 22	0.865 29	0.683 45

MatchingNet	0.724 41	0.812 40	0.812 22	0.810 40	0.735 34	0.834 43	0.495 49	0.860 28	0.572 66	0.602 50	0.954 40	0.512 48	0.280 48	0.757 24	0.845 41	0.725 36	0.780 40	0.606 55	0.937 19	0.851 42	0.700 41

INS-Conv-semantic	0.717 42	0.751 64	0.759 58	0.812 38	0.704 42	0.868 7	0.537 26	0.842 35	0.609 48	0.608 46	0.953 44	0.534 39	0.293 39	0.616 59	0.864 28	0.719 41	0.793 33	0.640 40	0.933 23	0.845 47	0.663 51

PointMetaBase	0.714 43	0.835 31	0.785 43	0.821 32	0.684 48	0.846 30	0.531 29	0.865 26	0.614 43	0.596 54	0.953 44	0.500 51	0.246 68	0.674 41	0.888 20	0.692 53	0.764 52	0.624 47	0.849 88	0.844 48	0.675 47

contrastBoundary	0.705 44	0.769 58	0.775 49	0.809 41	0.687 47	0.820 59	0.439 79	0.812 49	0.661 25	0.591 56	0.945 70	0.515 47	0.171 98	0.633 53	0.856 33	0.720 39	0.796 29	0.668 32	0.889 58	0.847 44	0.689 43
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 45	0.774 53	0.800 30	0.793 52	0.760 18	0.847 29	0.471 57	0.802 52	0.463 100	0.634 35	0.968 14	0.491 54	0.271 56	0.726 37	0.910 9	0.706 47	0.815 9	0.551 83	0.878 67	0.833 49	0.570 83

RFCR	0.702 46	0.889 18	0.745 70	0.813 37	0.672 51	0.818 63	0.493 50	0.815 47	0.623 40	0.610 44	0.947 64	0.470 63	0.249 67	0.594 63	0.848 40	0.705 48	0.779 41	0.646 37	0.892 56	0.823 55	0.611 66
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click	0.701 47	0.825 35	0.796 34	0.723 68	0.716 39	0.832 45	0.433 81	0.816 45	0.634 37	0.609 45	0.969 12	0.418 89	0.344 14	0.559 75	0.833 44	0.715 43	0.808 18	0.560 77	0.902 48	0.847 44	0.680 46

JSENet	0.699 48	0.881 20	0.762 56	0.821 32	0.667 52	0.800 76	0.522 32	0.792 55	0.613 44	0.607 47	0.935 90	0.492 53	0.205 85	0.576 68	0.853 37	0.691 55	0.758 58	0.652 35	0.872 76	0.828 52	0.649 55
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click	0.693 49	0.743 67	0.794 36	0.655 91	0.684 48	0.822 56	0.497 47	0.719 74	0.622 41	0.617 41	0.977 10	0.447 76	0.339 17	0.750 30	0.664 81	0.703 50	0.790 36	0.596 60	0.946 12	0.855 37	0.647 56
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-II	0.692 50	0.732 72	0.772 50	0.786 53	0.677 50	0.866 9	0.517 34	0.848 31	0.509 86	0.626 37	0.952 49	0.536 37	0.225 75	0.545 81	0.704 71	0.689 58	0.810 16	0.564 76	0.903 47	0.854 39	0.729 23
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 51	0.884 19	0.754 62	0.795 50	0.647 59	0.818 63	0.422 83	0.802 52	0.612 45	0.604 48	0.945 70	0.462 66	0.189 93	0.563 74	0.853 37	0.726 35	0.765 51	0.632 43	0.904 45	0.821 58	0.606 70
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 52	0.704 85	0.741 74	0.754 65	0.656 54	0.829 48	0.501 42	0.741 69	0.609 48	0.548 64	0.950 55	0.522 45	0.371 5	0.633 53	0.756 59	0.715 43	0.771 47	0.623 48	0.861 84	0.814 61	0.658 52
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 53	0.866 22	0.748 67	0.819 34	0.645 61	0.794 79	0.450 69	0.802 52	0.587 59	0.604 48	0.945 70	0.464 65	0.201 88	0.554 77	0.840 42	0.723 38	0.732 71	0.602 58	0.907 43	0.822 57	0.603 73

VACNN++	0.684 54	0.728 75	0.757 61	0.776 58	0.690 44	0.804 74	0.464 62	0.816 45	0.577 65	0.587 57	0.945 70	0.508 50	0.276 51	0.671 42	0.710 69	0.663 66	0.750 64	0.589 65	0.881 64	0.832 51	0.653 54

KP-FCNN	0.684 54	0.847 28	0.758 60	0.784 55	0.647 59	0.814 66	0.473 56	0.772 58	0.605 50	0.594 55	0.935 90	0.450 74	0.181 96	0.587 64	0.805 52	0.690 56	0.785 39	0.614 51	0.882 63	0.819 59	0.632 62
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet	0.684 54	0.712 84	0.784 44	0.782 57	0.658 53	0.835 42	0.499 46	0.823 44	0.641 34	0.597 53	0.950 55	0.487 56	0.281 47	0.575 69	0.619 85	0.647 74	0.764 52	0.620 50	0.871 79	0.846 46	0.688 44

PointContrast_LA_SEM	0.683 57	0.757 62	0.784 44	0.786 53	0.639 63	0.824 54	0.408 86	0.775 57	0.604 51	0.541 66	0.934 94	0.532 40	0.269 58	0.552 78	0.777 57	0.645 77	0.793 33	0.640 40	0.913 42	0.824 54	0.671 48

Superpoint Network	0.683 57	0.851 27	0.728 78	0.800 49	0.653 56	0.806 72	0.468 59	0.804 50	0.572 66	0.602 50	0.946 67	0.453 73	0.239 71	0.519 86	0.822 46	0.689 58	0.762 55	0.595 62	0.895 54	0.827 53	0.630 63

VI-PointConv	0.676 59	0.770 57	0.754 62	0.783 56	0.621 67	0.814 66	0.552 18	0.758 62	0.571 69	0.557 62	0.954 40	0.529 41	0.268 60	0.530 84	0.682 75	0.675 61	0.719 74	0.603 57	0.888 59	0.833 49	0.665 50
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D	0.673 60	0.789 44	0.748 67	0.763 63	0.635 65	0.814 66	0.407 88	0.747 66	0.581 63	0.573 59	0.950 55	0.484 57	0.271 56	0.607 60	0.754 60	0.649 71	0.774 44	0.596 60	0.883 62	0.823 55	0.606 70

SALANet	0.670 61	0.816 38	0.770 53	0.768 60	0.652 57	0.807 71	0.451 66	0.747 66	0.659 28	0.545 65	0.924 100	0.473 62	0.149 108	0.571 71	0.811 51	0.635 81	0.746 65	0.623 48	0.892 56	0.794 75	0.570 83

O3DSeg	0.668 62	0.822 36	0.771 52	0.496 112	0.651 58	0.833 44	0.541 23	0.761 61	0.555 75	0.611 43	0.966 15	0.489 55	0.370 6	0.388 105	0.580 88	0.776 17	0.751 62	0.570 71	0.956 7	0.817 60	0.646 57

PointConv	0.666 63	0.781 48	0.759 58	0.699 76	0.644 62	0.822 56	0.475 55	0.779 56	0.564 72	0.504 83	0.953 44	0.428 83	0.203 87	0.586 66	0.754 60	0.661 67	0.753 61	0.588 66	0.902 48	0.813 63	0.642 58
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 63	0.703 86	0.781 46	0.751 67	0.655 55	0.830 47	0.471 57	0.769 59	0.474 96	0.537 68	0.951 51	0.475 61	0.279 49	0.635 51	0.698 74	0.675 61	0.751 62	0.553 82	0.816 95	0.806 65	0.703 40
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++	0.663 65	0.746 65	0.708 81	0.722 69	0.638 64	0.820 59	0.451 66	0.566 102	0.599 54	0.541 66	0.950 55	0.510 49	0.313 29	0.648 47	0.819 49	0.616 86	0.682 89	0.590 64	0.869 80	0.810 64	0.656 53
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN	0.658 66	0.558 108	0.751 65	0.655 91	0.690 44	0.722 101	0.453 65	0.867 23	0.579 64	0.576 58	0.893 112	0.523 43	0.293 39	0.733 35	0.571 90	0.692 53	0.659 96	0.606 55	0.875 70	0.804 67	0.668 49

DCM-Net	0.658 66	0.778 49	0.702 84	0.806 45	0.619 68	0.813 69	0.468 59	0.693 82	0.494 89	0.524 74	0.941 82	0.449 75	0.298 36	0.510 88	0.821 47	0.675 61	0.727 73	0.568 74	0.826 93	0.803 68	0.637 60
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 68	0.698 88	0.743 72	0.650 93	0.564 85	0.820 59	0.505 40	0.758 62	0.631 38	0.479 87	0.945 70	0.480 59	0.226 73	0.572 70	0.774 58	0.690 56	0.735 69	0.614 51	0.853 87	0.776 90	0.597 76
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 69	0.752 63	0.734 76	0.664 89	0.583 80	0.815 65	0.399 90	0.754 64	0.639 35	0.535 70	0.942 80	0.470 63	0.309 31	0.665 43	0.539 92	0.650 70	0.708 79	0.635 42	0.857 86	0.793 77	0.642 58
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 70	0.778 49	0.731 77	0.699 76	0.577 81	0.829 48	0.446 71	0.736 70	0.477 95	0.523 76	0.945 70	0.454 70	0.269 58	0.484 95	0.749 63	0.618 84	0.738 67	0.599 59	0.827 92	0.792 80	0.621 65

PointConv-SFPN	0.641 71	0.776 51	0.703 83	0.721 70	0.557 88	0.826 51	0.451 66	0.672 87	0.563 73	0.483 86	0.943 79	0.425 86	0.162 103	0.644 48	0.726 65	0.659 68	0.709 78	0.572 70	0.875 70	0.786 85	0.559 89

MVPNet	0.641 71	0.831 32	0.715 79	0.671 86	0.590 76	0.781 85	0.394 92	0.679 84	0.642 33	0.553 63	0.937 87	0.462 66	0.256 64	0.649 46	0.406 105	0.626 82	0.691 86	0.666 33	0.877 68	0.792 80	0.608 69
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 73	0.717 82	0.701 85	0.692 79	0.576 82	0.801 75	0.467 61	0.716 75	0.563 73	0.459 93	0.953 44	0.429 82	0.169 100	0.581 67	0.854 36	0.605 87	0.710 76	0.550 84	0.894 55	0.793 77	0.575 81

FPConv	0.639 74	0.785 46	0.760 57	0.713 74	0.603 71	0.798 77	0.392 94	0.534 107	0.603 52	0.524 74	0.948 62	0.457 68	0.250 66	0.538 82	0.723 67	0.598 91	0.696 84	0.614 51	0.872 76	0.799 70	0.567 86
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net	0.638 75	0.797 42	0.769 54	0.641 98	0.590 76	0.820 59	0.461 63	0.537 106	0.637 36	0.536 69	0.947 64	0.388 96	0.206 84	0.656 44	0.668 79	0.647 74	0.732 71	0.585 68	0.868 81	0.793 77	0.473 109

PointSPNet	0.637 76	0.734 71	0.692 92	0.714 73	0.576 82	0.797 78	0.446 71	0.743 68	0.598 55	0.437 98	0.942 80	0.403 92	0.150 107	0.626 55	0.800 55	0.649 71	0.697 83	0.557 80	0.846 89	0.777 89	0.563 87

SConv	0.636 77	0.830 33	0.697 88	0.752 66	0.572 84	0.780 87	0.445 73	0.716 75	0.529 79	0.530 71	0.951 51	0.446 77	0.170 99	0.507 90	0.666 80	0.636 80	0.682 89	0.541 90	0.886 60	0.799 70	0.594 77

Supervoxel-CNN	0.635 78	0.656 94	0.711 80	0.719 71	0.613 69	0.757 96	0.444 76	0.765 60	0.534 78	0.566 60	0.928 98	0.478 60	0.272 54	0.636 50	0.531 94	0.664 65	0.645 100	0.508 98	0.864 83	0.792 80	0.611 66

joint point-based	0.634 79	0.614 102	0.778 47	0.667 88	0.633 66	0.825 52	0.420 84	0.804 50	0.467 98	0.561 61	0.951 51	0.494 52	0.291 41	0.566 72	0.458 100	0.579 97	0.764 52	0.559 79	0.838 90	0.814 61	0.598 75
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL	0.632 80	0.731 73	0.688 95	0.675 83	0.591 75	0.784 84	0.444 76	0.565 103	0.610 46	0.492 84	0.949 59	0.456 69	0.254 65	0.587 64	0.706 70	0.599 90	0.665 95	0.612 54	0.868 81	0.791 83	0.579 80

PointNet2-SFPN	0.631 81	0.771 55	0.692 92	0.672 84	0.524 94	0.837 39	0.440 78	0.706 80	0.538 77	0.446 95	0.944 76	0.421 88	0.219 78	0.552 78	0.751 62	0.591 93	0.737 68	0.543 89	0.901 50	0.768 92	0.557 90

APCF-Net	0.631 81	0.742 68	0.687 97	0.672 84	0.557 88	0.792 82	0.408 86	0.665 89	0.545 76	0.508 80	0.952 49	0.428 83	0.186 94	0.634 52	0.702 72	0.620 83	0.706 80	0.555 81	0.873 74	0.798 72	0.581 79
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF	0.631 81	0.626 99	0.745 70	0.801 48	0.607 70	0.751 97	0.506 39	0.729 73	0.565 71	0.491 85	0.866 115	0.434 78	0.197 91	0.595 62	0.630 84	0.709 46	0.705 81	0.560 77	0.875 70	0.740 100	0.491 104

FusionAwareConv	0.630 84	0.604 104	0.741 74	0.766 62	0.590 76	0.747 98	0.501 42	0.734 71	0.503 88	0.527 72	0.919 104	0.454 70	0.323 26	0.550 80	0.420 104	0.678 60	0.688 87	0.544 87	0.896 53	0.795 74	0.627 64
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 85	0.800 41	0.625 107	0.719 71	0.545 91	0.806 72	0.445 73	0.597 97	0.448 103	0.519 78	0.938 86	0.481 58	0.328 24	0.489 94	0.499 99	0.657 69	0.759 57	0.592 63	0.881 64	0.797 73	0.634 61

SegGroup_sem	0.627 86	0.818 37	0.747 69	0.701 75	0.602 72	0.764 93	0.385 98	0.629 94	0.490 91	0.508 80	0.931 97	0.409 91	0.201 88	0.564 73	0.725 66	0.618 84	0.692 85	0.539 91	0.873 74	0.794 75	0.548 93
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv	0.625 87	0.830 33	0.694 90	0.757 64	0.563 86	0.772 91	0.448 70	0.647 92	0.520 82	0.509 79	0.949 59	0.431 81	0.191 92	0.496 92	0.614 86	0.647 74	0.672 93	0.535 94	0.876 69	0.783 86	0.571 82

dtc_net	0.625 87	0.703 86	0.751 65	0.794 51	0.535 92	0.848 27	0.480 54	0.676 86	0.528 80	0.469 90	0.944 76	0.454 70	0.004 120	0.464 97	0.636 83	0.704 49	0.758 58	0.548 86	0.924 30	0.787 84	0.492 103

Weakly-Openseg v3	0.625 87	0.924 8	0.787 42	0.620 100	0.555 90	0.811 70	0.393 93	0.666 88	0.382 111	0.520 77	0.953 44	0.250 115	0.208 82	0.604 61	0.670 77	0.644 78	0.742 66	0.538 92	0.919 36	0.803 68	0.513 101

HPEIN	0.618 90	0.729 74	0.668 98	0.647 95	0.597 74	0.766 92	0.414 85	0.680 83	0.520 82	0.525 73	0.946 67	0.432 79	0.215 80	0.493 93	0.599 87	0.638 79	0.617 105	0.570 71	0.897 52	0.806 65	0.605 72
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCN	0.610 91	0.858 26	0.772 50	0.489 113	0.532 93	0.792 82	0.404 89	0.643 93	0.570 70	0.507 82	0.935 90	0.414 90	0.046 117	0.510 88	0.702 72	0.602 89	0.705 81	0.549 85	0.859 85	0.773 91	0.534 96
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 92	0.760 60	0.667 99	0.649 94	0.521 95	0.793 80	0.457 64	0.648 91	0.528 80	0.434 100	0.947 64	0.401 93	0.153 106	0.454 98	0.721 68	0.648 73	0.717 75	0.536 93	0.904 45	0.765 93	0.485 105
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer	0.600 93	0.634 98	0.743 72	0.697 78	0.601 73	0.781 85	0.437 80	0.585 100	0.493 90	0.446 95	0.933 95	0.394 94	0.011 119	0.654 45	0.661 82	0.603 88	0.733 70	0.526 95	0.832 91	0.761 95	0.480 106

LAP-D	0.594 94	0.720 80	0.692 92	0.637 99	0.456 104	0.773 90	0.391 96	0.730 72	0.587 59	0.445 97	0.940 84	0.381 97	0.288 42	0.434 101	0.453 102	0.591 93	0.649 98	0.581 69	0.777 99	0.749 99	0.610 68

DPC	0.592 95	0.720 80	0.700 86	0.602 104	0.480 100	0.762 95	0.380 99	0.713 78	0.585 62	0.437 98	0.940 84	0.369 99	0.288 42	0.434 101	0.509 98	0.590 95	0.639 103	0.567 75	0.772 100	0.755 97	0.592 78
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet	0.589 96	0.766 59	0.659 102	0.683 81	0.470 103	0.740 100	0.387 97	0.620 96	0.490 91	0.476 88	0.922 102	0.355 102	0.245 69	0.511 87	0.511 97	0.571 98	0.643 101	0.493 102	0.872 76	0.762 94	0.600 74

ROSMRF	0.580 97	0.772 54	0.707 82	0.681 82	0.563 86	0.764 93	0.362 101	0.515 108	0.465 99	0.465 92	0.936 89	0.427 85	0.207 83	0.438 99	0.577 89	0.536 101	0.675 92	0.486 103	0.723 106	0.779 87	0.524 98

SD-DETR	0.576 98	0.746 65	0.609 111	0.445 117	0.517 96	0.643 112	0.366 100	0.714 77	0.456 101	0.468 91	0.870 114	0.432 79	0.264 61	0.558 76	0.674 76	0.586 96	0.688 87	0.482 104	0.739 104	0.733 102	0.537 95

SQN_0.1%	0.569 99	0.676 90	0.696 89	0.657 90	0.497 97	0.779 88	0.424 82	0.548 104	0.515 84	0.376 105	0.902 111	0.422 87	0.357 10	0.379 106	0.456 101	0.596 92	0.659 96	0.544 87	0.685 109	0.665 113	0.556 91

TextureNet	0.566 100	0.672 92	0.664 100	0.671 86	0.494 98	0.719 102	0.445 73	0.678 85	0.411 109	0.396 103	0.935 90	0.356 101	0.225 75	0.412 103	0.535 93	0.565 99	0.636 104	0.464 106	0.794 98	0.680 110	0.568 85
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet	0.562 101	0.648 95	0.700 86	0.770 59	0.586 79	0.687 106	0.333 105	0.650 90	0.514 85	0.475 89	0.906 108	0.359 100	0.223 77	0.340 108	0.442 103	0.422 112	0.668 94	0.501 99	0.708 107	0.779 87	0.534 96

Pointnet++ & Feature	0.557 102	0.735 70	0.661 101	0.686 80	0.491 99	0.744 99	0.392 94	0.539 105	0.451 102	0.375 106	0.946 67	0.376 98	0.205 85	0.403 104	0.356 108	0.553 100	0.643 101	0.497 100	0.824 94	0.756 96	0.515 99

GMLPs	0.538 103	0.495 113	0.693 91	0.647 95	0.471 102	0.793 80	0.300 108	0.477 109	0.505 87	0.358 107	0.903 110	0.327 105	0.081 114	0.472 96	0.529 95	0.448 110	0.710 76	0.509 96	0.746 102	0.737 101	0.554 92

PanopticFusion-label	0.529 104	0.491 114	0.688 95	0.604 103	0.386 109	0.632 113	0.225 119	0.705 81	0.434 106	0.293 113	0.815 117	0.348 103	0.241 70	0.499 91	0.669 78	0.507 103	0.649 98	0.442 112	0.796 97	0.602 117	0.561 88
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak	0.516 105	0.676 90	0.591 114	0.609 101	0.442 105	0.774 89	0.335 104	0.597 97	0.422 108	0.357 108	0.932 96	0.341 104	0.094 113	0.298 110	0.528 96	0.473 108	0.676 91	0.495 101	0.602 115	0.721 105	0.349 117

Online SegFusion	0.515 106	0.607 103	0.644 105	0.579 106	0.434 106	0.630 114	0.353 102	0.628 95	0.440 104	0.410 101	0.762 120	0.307 107	0.167 101	0.520 85	0.403 106	0.516 102	0.565 108	0.447 110	0.678 110	0.701 107	0.514 100
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF	0.501 107	0.558 108	0.608 112	0.424 119	0.478 101	0.690 105	0.246 115	0.586 99	0.468 97	0.450 94	0.911 106	0.394 94	0.160 104	0.438 99	0.212 115	0.432 111	0.541 113	0.475 105	0.742 103	0.727 103	0.477 107

PCNN	0.498 108	0.559 107	0.644 105	0.560 108	0.420 108	0.711 104	0.229 117	0.414 110	0.436 105	0.352 109	0.941 82	0.324 106	0.155 105	0.238 115	0.387 107	0.493 104	0.529 114	0.509 96	0.813 96	0.751 98	0.504 102

3DMV	0.484 109	0.484 115	0.538 117	0.643 97	0.424 107	0.606 117	0.310 106	0.574 101	0.433 107	0.378 104	0.796 118	0.301 108	0.214 81	0.537 83	0.208 116	0.472 109	0.507 117	0.413 115	0.693 108	0.602 117	0.539 94
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 110	0.577 106	0.611 110	0.356 121	0.321 117	0.715 103	0.299 110	0.376 114	0.328 117	0.319 111	0.944 76	0.285 110	0.164 102	0.216 118	0.229 113	0.484 106	0.545 112	0.456 108	0.755 101	0.709 106	0.475 108
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 111	0.679 89	0.604 113	0.578 107	0.380 110	0.682 107	0.291 111	0.106 121	0.483 94	0.258 119	0.920 103	0.258 114	0.025 118	0.231 117	0.325 109	0.480 107	0.560 110	0.463 107	0.725 105	0.666 112	0.231 121
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 112	0.474 116	0.623 108	0.463 115	0.366 112	0.651 110	0.310 106	0.389 113	0.349 115	0.330 110	0.937 87	0.271 112	0.126 110	0.285 111	0.224 114	0.350 117	0.577 107	0.445 111	0.625 113	0.723 104	0.394 113
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET2	0.442 113	0.548 110	0.548 116	0.597 105	0.363 113	0.628 115	0.300 108	0.292 116	0.374 112	0.307 112	0.881 113	0.268 113	0.186 94	0.238 115	0.204 117	0.407 113	0.506 118	0.449 109	0.667 111	0.620 116	0.462 111

SurfaceConvPF	0.442 113	0.505 112	0.622 109	0.380 120	0.342 115	0.654 109	0.227 118	0.397 112	0.367 113	0.276 115	0.924 100	0.240 116	0.198 90	0.359 107	0.262 111	0.366 114	0.581 106	0.435 113	0.640 112	0.668 111	0.398 112
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 115	0.437 118	0.646 104	0.474 114	0.369 111	0.645 111	0.353 102	0.258 118	0.282 120	0.279 114	0.918 105	0.298 109	0.147 109	0.283 112	0.294 110	0.487 105	0.562 109	0.427 114	0.619 114	0.633 115	0.352 116
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 116	0.525 111	0.647 103	0.522 109	0.324 116	0.488 121	0.077 122	0.712 79	0.353 114	0.401 102	0.636 122	0.281 111	0.176 97	0.340 108	0.565 91	0.175 121	0.551 111	0.398 116	0.370 122	0.602 117	0.361 115

SPLAT Net	0.393 117	0.472 117	0.511 118	0.606 102	0.311 118	0.656 108	0.245 116	0.405 111	0.328 117	0.197 120	0.927 99	0.227 118	0.000 122	0.001 123	0.249 112	0.271 120	0.510 115	0.383 118	0.593 116	0.699 108	0.267 119
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF	0.383 118	0.297 120	0.491 119	0.432 118	0.358 114	0.612 116	0.274 113	0.116 120	0.411 109	0.265 116	0.904 109	0.229 117	0.079 115	0.250 113	0.185 118	0.320 118	0.510 115	0.385 117	0.548 117	0.597 120	0.394 113

PointNet++	0.339 119	0.584 105	0.478 120	0.458 116	0.256 120	0.360 122	0.250 114	0.247 119	0.278 121	0.261 118	0.677 121	0.183 119	0.117 111	0.212 119	0.145 120	0.364 115	0.346 122	0.232 122	0.548 117	0.523 121	0.252 120
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
GrowSP++	0.323 120	0.114 122	0.589 115	0.499 111	0.147 122	0.555 118	0.290 112	0.336 115	0.290 119	0.262 117	0.865 116	0.102 122	0.000 122	0.037 121	0.000 123	0.000 123	0.462 119	0.381 119	0.389 121	0.664 114	0.473 109

SSC-UNet	0.308 121	0.353 119	0.290 122	0.278 122	0.166 121	0.553 119	0.169 121	0.286 117	0.147 122	0.148 122	0.908 107	0.182 120	0.064 116	0.023 122	0.018 122	0.354 116	0.363 120	0.345 120	0.546 119	0.685 109	0.278 118

ScanNet	0.306 122	0.203 121	0.366 121	0.501 110	0.311 118	0.524 120	0.211 120	0.002 123	0.342 116	0.189 121	0.786 119	0.145 121	0.102 112	0.245 114	0.152 119	0.318 119	0.348 121	0.300 121	0.460 120	0.437 122	0.182 122
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 123	0.000 123	0.041 123	0.172 123	0.030 123	0.062 123	0.001 123	0.035 122	0.004 123	0.051 123	0.143 123	0.019 123	0.003 121	0.041 120	0.050 121	0.003 122	0.054 123	0.018 123	0.005 123	0.264 123	0.082 123

This table lists the benchmark results for the 3D semantic instance scenario.

Method	avg ap 50%	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

Competitor-MAFT	0.816 1	1.000 1	0.983 4	0.872 11	0.718 6	0.941 2	0.588 5	0.652 41	0.819 3	0.776 3	0.720 6	0.780 6	0.769 12	1.000 1	0.797 11	0.813 31	0.798 9	1.000 1	0.659 5

PointRel	0.816 1	1.000 1	0.971 9	0.908 6	0.743 2	0.923 9	0.573 9	0.714 22	0.695 20	0.734 11	0.747 2	0.725 13	0.809 1	1.000 1	0.814 9	0.899 5	0.820 4	1.000 1	0.610 19
: Relation3D: Enhancing Relation Modeling for Point Cloud Instance Segmentation. CVPR 2025
Spherical Mask(CtoF)	0.812 3	1.000 1	0.973 8	0.852 16	0.718 7	0.917 11	0.574 7	0.677 31	0.748 13	0.729 15	0.715 9	0.795 3	0.809 1	1.000 1	0.831 4	0.854 11	0.787 13	1.000 1	0.638 8

PointComp	0.811 4	0.850 60	0.969 10	0.864 14	0.739 3	0.946 1	0.539 15	0.671 34	0.835 1	0.700 19	0.742 3	0.817 1	0.766 13	1.000 1	0.755 22	0.909 1	0.808 7	1.000 1	0.687 2

EV3D	0.811 4	1.000 1	0.968 11	0.852 16	0.717 8	0.921 10	0.574 8	0.677 31	0.748 13	0.730 14	0.703 15	0.795 3	0.809 1	1.000 1	0.831 4	0.854 11	0.778 17	1.000 1	0.638 9

VDG-Uni3DSeg	0.804 6	1.000 1	0.990 1	0.886 9	0.688 21	0.912 13	0.602 2	0.703 26	0.786 8	0.771 4	0.708 12	0.700 18	0.669 27	0.981 41	0.789 17	0.903 2	0.772 20	1.000 1	0.609 20

SIM3D	0.803 7	1.000 1	0.967 12	0.863 15	0.692 20	0.924 8	0.552 13	0.732 21	0.667 25	0.732 13	0.662 19	0.796 2	0.789 9	1.000 1	0.803 10	0.864 8	0.766 23	1.000 1	0.643 7

OneFormer3D	0.801 8	1.000 1	0.973 7	0.909 5	0.698 16	0.928 6	0.582 6	0.668 37	0.685 21	0.780 2	0.687 17	0.698 22	0.702 16	1.000 1	0.794 13	0.900 4	0.784 15	0.986 55	0.635 10
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
Competitor-SPFormer	0.800 9	1.000 1	0.986 3	0.845 18	0.705 14	0.915 12	0.532 16	0.733 20	0.757 12	0.733 12	0.708 11	0.698 21	0.648 39	0.981 41	0.890 1	0.830 21	0.796 10	0.997 42	0.644 6

UniPerception	0.800 9	1.000 1	0.930 14	0.872 11	0.727 5	0.862 27	0.454 22	0.764 13	0.820 2	0.746 8	0.706 13	0.750 8	0.772 10	0.926 49	0.764 20	0.818 29	0.826 2	0.997 42	0.660 4

InsSSM	0.799 11	1.000 1	0.915 16	0.710 44	0.729 4	0.925 7	0.664 1	0.670 35	0.770 9	0.766 5	0.739 4	0.737 9	0.700 17	1.000 1	0.792 14	0.829 23	0.815 5	0.997 42	0.625 12
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
DCD	0.798 12	1.000 1	0.878 23	0.792 30	0.693 19	0.936 3	0.596 3	0.685 30	0.663 27	0.736 9	0.717 7	0.788 5	0.693 22	1.000 1	0.825 7	0.840 17	0.837 1	1.000 1	0.689 1

TST3D	0.795 13	1.000 1	0.929 15	0.918 4	0.709 11	0.884 22	0.596 4	0.704 25	0.769 10	0.734 10	0.644 24	0.699 20	0.751 14	1.000 1	0.794 12	0.876 7	0.757 26	0.997 42	0.550 36
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
MG-Former	0.791 14	1.000 1	0.980 6	0.837 21	0.626 29	0.897 15	0.543 14	0.759 15	0.800 7	0.766 6	0.659 20	0.769 7	0.697 20	1.000 1	0.791 15	0.707 52	0.791 12	1.000 1	0.610 18

ExtMask3D	0.789 15	1.000 1	0.988 2	0.756 37	0.706 13	0.912 14	0.429 23	0.647 43	0.806 6	0.755 7	0.673 18	0.689 23	0.772 11	1.000 1	0.789 16	0.852 13	0.811 6	1.000 1	0.617 15

Queryformer	0.787 16	1.000 1	0.933 13	0.601 54	0.754 1	0.886 20	0.558 12	0.661 39	0.767 11	0.665 22	0.716 8	0.639 29	0.808 5	1.000 1	0.844 3	0.897 6	0.804 8	1.000 1	0.624 13

MAFT	0.786 17	1.000 1	0.894 21	0.807 25	0.694 18	0.893 18	0.486 18	0.674 33	0.740 15	0.786 1	0.704 14	0.727 12	0.739 15	1.000 1	0.707 28	0.849 15	0.756 27	1.000 1	0.685 3

KmaxOneFormerNet	0.783 18	0.903 58	0.981 5	0.794 29	0.706 12	0.931 5	0.561 11	0.701 27	0.706 18	0.727 16	0.697 16	0.731 11	0.689 24	1.000 1	0.856 2	0.750 43	0.761 25	1.000 1	0.599 24

Mask3D	0.780 19	1.000 1	0.786 47	0.716 42	0.696 17	0.885 21	0.500 17	0.714 22	0.810 5	0.672 21	0.715 9	0.679 24	0.809 1	1.000 1	0.831 4	0.833 20	0.787 13	1.000 1	0.602 22
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormer	0.770 20	0.903 58	0.903 18	0.806 26	0.609 36	0.886 19	0.568 10	0.815 6	0.705 19	0.711 17	0.655 21	0.652 28	0.685 25	1.000 1	0.789 18	0.809 32	0.776 19	1.000 1	0.583 28
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++	0.769 21	1.000 1	0.803 40	0.937 1	0.684 22	0.865 24	0.213 39	0.870 2	0.664 26	0.571 29	0.758 1	0.702 17	0.807 6	1.000 1	0.653 35	0.902 3	0.792 11	1.000 1	0.626 11

SoftGroup	0.761 22	1.000 1	0.808 36	0.845 18	0.716 9	0.862 26	0.243 36	0.824 4	0.655 29	0.620 23	0.734 5	0.699 19	0.791 8	0.981 41	0.716 25	0.844 16	0.769 21	1.000 1	0.594 26
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNet	0.757 23	1.000 1	0.904 17	0.731 40	0.678 23	0.895 16	0.458 20	0.644 45	0.670 24	0.710 18	0.620 29	0.732 10	0.650 29	1.000 1	0.756 21	0.778 35	0.779 16	1.000 1	0.614 16
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
TD3D	0.751 24	1.000 1	0.774 48	0.867 13	0.621 31	0.934 4	0.404 24	0.706 24	0.812 4	0.605 26	0.633 27	0.626 30	0.690 23	1.000 1	0.640 37	0.820 26	0.777 18	1.000 1	0.612 17
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNet	0.747 25	1.000 1	0.818 32	0.837 22	0.713 10	0.844 29	0.457 21	0.647 43	0.711 17	0.614 24	0.617 31	0.657 27	0.650 29	1.000 1	0.692 29	0.822 25	0.765 24	1.000 1	0.595 25
Weiguang Zhao, Yuyao Yan, Chaolong Yang, Jianan Ye, Xi Yang, Kaizhu Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut	0.732 26	1.000 1	0.788 45	0.724 41	0.642 28	0.859 28	0.248 35	0.787 11	0.618 32	0.596 27	0.653 23	0.722 15	0.583 51	1.000 1	0.766 19	0.861 9	0.825 3	1.000 1	0.504 42

IPCA-Inst	0.731 27	1.000 1	0.788 46	0.884 10	0.698 15	0.788 45	0.252 34	0.760 14	0.646 30	0.511 37	0.637 26	0.665 26	0.804 7	1.000 1	0.644 36	0.778 36	0.747 29	1.000 1	0.561 32

TopoSeg	0.725 28	1.000 1	0.806 39	0.933 2	0.668 25	0.758 50	0.272 33	0.734 19	0.630 31	0.549 33	0.654 22	0.606 31	0.697 21	0.966 46	0.612 41	0.839 18	0.754 28	1.000 1	0.573 29

DKNet	0.718 29	1.000 1	0.814 33	0.782 31	0.619 33	0.872 23	0.224 37	0.751 17	0.569 36	0.677 20	0.585 36	0.724 14	0.633 41	0.981 41	0.515 51	0.819 27	0.736 30	1.000 1	0.617 14
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC	0.707 30	1.000 1	0.850 25	0.924 3	0.648 26	0.747 53	0.162 41	0.862 3	0.572 35	0.520 35	0.624 28	0.549 34	0.649 38	1.000 1	0.560 46	0.706 53	0.768 22	1.000 1	0.591 27

HAIS	0.699 31	1.000 1	0.849 26	0.820 23	0.675 24	0.808 39	0.279 31	0.757 16	0.465 42	0.517 36	0.596 33	0.559 33	0.600 45	1.000 1	0.654 34	0.767 38	0.676 34	0.994 51	0.560 33
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet	0.698 32	1.000 1	0.697 64	0.888 8	0.556 43	0.803 40	0.387 25	0.626 47	0.417 47	0.556 32	0.585 37	0.702 16	0.600 45	1.000 1	0.824 8	0.720 51	0.692 32	1.000 1	0.509 41
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup	0.694 33	1.000 1	0.799 42	0.811 24	0.622 30	0.817 34	0.376 26	0.805 9	0.590 34	0.487 41	0.568 40	0.525 38	0.650 29	0.835 59	0.600 42	0.829 22	0.655 37	1.000 1	0.526 38

ODIN - Ins	0.693 34	1.000 1	0.880 22	0.647 49	0.620 32	0.779 47	0.336 28	0.501 62	0.681 22	0.577 28	0.595 34	0.679 25	0.683 26	1.000 1	0.709 27	0.816 30	0.637 41	0.770 71	0.557 34
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024
DANCENET	0.680 35	1.000 1	0.807 37	0.733 39	0.600 37	0.768 49	0.375 27	0.543 55	0.538 37	0.610 25	0.599 32	0.498 39	0.632 43	0.981 41	0.739 24	0.856 10	0.633 44	0.882 66	0.454 51

SphereSeg	0.680 35	1.000 1	0.856 24	0.744 38	0.618 34	0.893 17	0.151 42	0.651 42	0.713 16	0.537 34	0.579 39	0.430 48	0.651 28	1.000 1	0.389 62	0.744 46	0.697 31	0.991 53	0.601 23

Box2Mask	0.677 37	1.000 1	0.847 27	0.771 33	0.509 52	0.816 35	0.277 32	0.558 54	0.482 39	0.562 31	0.640 25	0.448 44	0.700 17	1.000 1	0.666 30	0.852 14	0.578 51	0.997 42	0.488 46
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance	0.672 38	1.000 1	0.758 56	0.682 46	0.576 41	0.842 30	0.477 19	0.504 61	0.524 38	0.567 30	0.585 38	0.451 43	0.557 53	1.000 1	0.751 23	0.797 33	0.563 54	1.000 1	0.467 50
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group	0.664 39	1.000 1	0.822 31	0.764 36	0.616 35	0.815 36	0.139 46	0.694 29	0.597 33	0.459 45	0.566 41	0.599 32	0.600 45	0.516 69	0.715 26	0.819 28	0.635 42	1.000 1	0.603 21
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance	0.657 40	1.000 1	0.760 54	0.667 48	0.581 39	0.863 25	0.323 29	0.655 40	0.477 40	0.473 43	0.549 43	0.432 47	0.650 29	1.000 1	0.655 33	0.738 47	0.585 50	0.944 58	0.472 49

CSC-Pretrained	0.648 41	1.000 1	0.810 34	0.768 34	0.523 50	0.813 37	0.143 45	0.819 5	0.389 50	0.422 54	0.511 47	0.443 45	0.650 29	1.000 1	0.624 39	0.732 48	0.634 43	1.000 1	0.375 58

PE	0.645 42	1.000 1	0.773 50	0.798 28	0.538 45	0.786 46	0.088 54	0.799 10	0.350 54	0.435 52	0.547 44	0.545 35	0.646 40	0.933 48	0.562 45	0.761 41	0.556 59	0.997 42	0.501 44
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 43	1.000 1	0.758 55	0.582 60	0.539 44	0.826 33	0.046 59	0.765 12	0.372 52	0.436 51	0.588 35	0.539 37	0.650 29	1.000 1	0.577 43	0.750 44	0.653 39	0.997 42	0.495 45
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3D	0.641 44	1.000 1	0.841 28	0.893 7	0.531 47	0.802 41	0.115 51	0.588 52	0.448 44	0.438 49	0.537 46	0.430 49	0.550 54	0.857 51	0.534 49	0.764 40	0.657 36	0.987 54	0.568 30
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN	0.638 45	1.000 1	0.895 20	0.800 27	0.480 56	0.676 58	0.144 44	0.737 18	0.354 53	0.447 46	0.400 60	0.365 55	0.700 17	1.000 1	0.569 44	0.836 19	0.599 46	1.000 1	0.473 48

PointGroup	0.636 46	1.000 1	0.765 51	0.624 51	0.505 54	0.797 42	0.116 50	0.696 28	0.384 51	0.441 47	0.559 42	0.476 41	0.596 48	1.000 1	0.666 30	0.756 42	0.556 58	0.997 42	0.513 40
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group	0.635 47	0.667 62	0.797 44	0.714 43	0.562 42	0.774 48	0.146 43	0.810 8	0.429 46	0.476 42	0.546 45	0.399 51	0.633 41	1.000 1	0.632 38	0.722 50	0.609 45	1.000 1	0.514 39
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
Mask3D_evaluation	0.631 48	1.000 1	0.829 30	0.606 53	0.646 27	0.836 31	0.068 55	0.511 59	0.462 43	0.507 38	0.619 30	0.389 53	0.610 44	1.000 1	0.432 57	0.828 24	0.673 35	0.788 70	0.552 35

DENet	0.629 49	1.000 1	0.797 43	0.608 52	0.589 38	0.627 62	0.219 38	0.882 1	0.310 56	0.402 59	0.383 62	0.396 52	0.650 29	1.000 1	0.663 32	0.543 70	0.691 33	1.000 1	0.568 31

3D-MPA	0.611 50	1.000 1	0.833 29	0.765 35	0.526 49	0.756 51	0.136 48	0.588 52	0.470 41	0.438 50	0.432 56	0.358 57	0.650 29	0.857 51	0.429 58	0.765 39	0.557 57	1.000 1	0.430 53
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS	0.605 51	1.000 1	0.801 41	0.599 55	0.535 46	0.728 55	0.286 30	0.436 66	0.679 23	0.491 39	0.433 54	0.256 59	0.404 66	0.857 51	0.620 40	0.724 49	0.510 64	1.000 1	0.539 37

AOIA	0.601 52	1.000 1	0.761 53	0.687 45	0.485 55	0.828 32	0.008 66	0.663 38	0.405 49	0.405 58	0.425 57	0.490 40	0.596 48	0.714 62	0.553 48	0.779 34	0.597 47	0.992 52	0.424 55

PCJC	0.578 53	1.000 1	0.810 35	0.583 59	0.449 59	0.813 38	0.042 60	0.603 50	0.341 55	0.490 40	0.465 51	0.410 50	0.650 29	0.835 59	0.264 68	0.694 57	0.561 55	0.889 63	0.504 43

SSEN	0.575 54	1.000 1	0.761 52	0.473 62	0.477 57	0.795 43	0.066 56	0.529 57	0.658 28	0.460 44	0.461 52	0.380 54	0.331 68	0.859 50	0.401 61	0.692 59	0.653 38	1.000 1	0.348 60
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg	0.567 55	0.528 72	0.708 63	0.626 50	0.580 40	0.745 54	0.063 57	0.627 46	0.240 60	0.400 60	0.497 48	0.464 42	0.515 55	1.000 1	0.475 53	0.745 45	0.571 52	1.000 1	0.429 54

NeuralBF	0.555 56	0.667 62	0.896 19	0.843 20	0.517 51	0.751 52	0.029 61	0.519 58	0.414 48	0.439 48	0.465 50	0.000 78	0.484 57	0.857 51	0.287 66	0.693 58	0.651 40	1.000 1	0.485 47
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML	0.549 57	1.000 1	0.807 38	0.588 58	0.327 64	0.647 60	0.004 68	0.815 7	0.180 63	0.418 55	0.364 64	0.182 62	0.445 60	1.000 1	0.442 56	0.688 60	0.571 53	1.000 1	0.396 56
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance	0.539 58	1.000 1	0.621 67	0.300 65	0.530 48	0.698 56	0.127 49	0.533 56	0.222 61	0.430 53	0.400 59	0.365 55	0.574 52	0.938 47	0.472 54	0.659 62	0.543 60	0.944 58	0.347 61

One_Thing_One_Click	0.529 59	0.667 62	0.718 59	0.777 32	0.399 60	0.683 57	0.000 71	0.669 36	0.138 66	0.391 61	0.374 63	0.539 36	0.360 67	0.641 66	0.556 47	0.774 37	0.593 48	0.997 42	0.251 66
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN	0.515 60	1.000 1	0.538 72	0.282 66	0.468 58	0.790 44	0.173 40	0.345 68	0.429 45	0.413 57	0.484 49	0.176 63	0.595 50	0.591 67	0.522 50	0.668 61	0.476 65	0.986 56	0.327 62

Occipital-SCS	0.512 61	1.000 1	0.716 60	0.509 61	0.506 53	0.611 63	0.092 53	0.602 51	0.177 64	0.346 64	0.383 61	0.165 64	0.442 61	0.850 58	0.386 63	0.618 66	0.543 61	0.889 63	0.389 57

3D-BoNet	0.488 62	1.000 1	0.672 66	0.590 57	0.301 66	0.484 73	0.098 52	0.620 48	0.306 57	0.341 65	0.259 68	0.125 66	0.434 63	0.796 61	0.402 60	0.499 72	0.513 63	0.909 62	0.439 52
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst	0.478 63	0.667 62	0.712 62	0.595 56	0.259 69	0.550 69	0.000 71	0.613 49	0.175 65	0.250 70	0.434 53	0.437 46	0.411 65	0.857 51	0.485 52	0.591 69	0.267 75	0.944 58	0.359 59
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS	0.470 64	0.667 62	0.685 65	0.677 47	0.372 62	0.562 67	0.000 71	0.482 63	0.244 59	0.316 67	0.298 65	0.052 73	0.442 62	0.857 51	0.267 67	0.702 54	0.559 56	1.000 1	0.287 64

SALoss-ResNet	0.459 65	1.000 1	0.737 58	0.159 76	0.259 68	0.587 65	0.138 47	0.475 64	0.217 62	0.416 56	0.408 58	0.128 65	0.315 69	0.714 62	0.411 59	0.536 71	0.590 49	0.873 67	0.304 63
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.447 66	0.528 72	0.555 70	0.381 63	0.382 61	0.633 61	0.002 69	0.509 60	0.260 58	0.361 63	0.432 55	0.327 58	0.451 59	0.571 68	0.367 64	0.639 64	0.386 66	0.980 57	0.276 65
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_ins	0.445 67	0.667 62	0.773 49	0.185 73	0.317 65	0.656 59	0.000 71	0.407 67	0.134 67	0.381 62	0.267 67	0.217 61	0.476 58	0.714 62	0.452 55	0.629 65	0.514 62	1.000 1	0.222 69
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SIS	0.382 68	1.000 1	0.432 75	0.245 68	0.190 70	0.577 66	0.013 65	0.263 70	0.033 73	0.320 66	0.240 69	0.075 69	0.422 64	0.857 51	0.117 73	0.699 55	0.271 74	0.883 65	0.235 68
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.323 69	0.667 62	0.542 71	0.264 67	0.157 73	0.550 68	0.000 71	0.205 73	0.009 75	0.270 69	0.218 70	0.075 69	0.500 56	0.688 65	0.007 79	0.698 56	0.301 71	0.459 76	0.200 70
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone	0.319 70	0.667 62	0.715 61	0.233 69	0.189 71	0.479 74	0.008 66	0.218 71	0.067 72	0.201 72	0.173 71	0.107 67	0.123 74	0.438 70	0.150 70	0.615 67	0.355 67	0.916 61	0.093 78

R-PointNet	0.306 71	0.500 74	0.405 76	0.311 64	0.348 63	0.589 64	0.054 58	0.068 76	0.126 68	0.283 68	0.290 66	0.028 74	0.219 72	0.214 73	0.331 65	0.396 76	0.275 72	0.821 69	0.245 67

Region-18class	0.284 72	0.250 78	0.751 57	0.228 71	0.270 67	0.521 70	0.000 71	0.468 65	0.008 77	0.205 71	0.127 72	0.000 78	0.068 76	0.070 77	0.262 69	0.652 63	0.323 69	0.740 72	0.173 71

SemRegionNet-20cls	0.250 73	0.333 75	0.613 68	0.229 70	0.163 72	0.493 71	0.000 71	0.304 69	0.107 69	0.147 75	0.100 74	0.052 72	0.231 70	0.119 75	0.039 75	0.445 74	0.325 68	0.654 73	0.141 74

tmp	0.248 74	0.667 62	0.437 74	0.188 72	0.153 74	0.491 72	0.000 71	0.208 72	0.094 71	0.153 74	0.099 75	0.057 71	0.217 73	0.119 75	0.039 75	0.466 73	0.302 70	0.640 74	0.140 75

3D-BEVIS	0.248 74	0.667 62	0.566 69	0.076 77	0.035 79	0.394 77	0.027 63	0.035 78	0.098 70	0.099 77	0.030 78	0.025 75	0.098 75	0.375 72	0.126 72	0.604 68	0.181 77	0.854 68	0.171 72
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sem_Recon_ins	0.227 76	0.764 61	0.486 73	0.069 78	0.098 76	0.426 76	0.017 64	0.067 77	0.015 74	0.172 73	0.100 73	0.096 68	0.054 78	0.183 74	0.135 71	0.366 77	0.260 76	0.614 75	0.168 73

ASIS	0.199 77	0.333 75	0.253 78	0.167 75	0.140 75	0.438 75	0.000 71	0.177 74	0.008 76	0.121 76	0.069 76	0.004 77	0.231 71	0.429 71	0.036 77	0.445 75	0.273 73	0.333 78	0.119 77

Sgpn_scannet	0.143 78	0.208 79	0.390 77	0.169 74	0.065 77	0.275 78	0.029 62	0.069 75	0.000 78	0.087 78	0.043 77	0.014 76	0.027 79	0.000 78	0.112 74	0.351 78	0.168 78	0.438 77	0.138 76

MaskRCNN 2d->3d Proj	0.058 79	0.333 75	0.002 79	0.000 79	0.053 78	0.002 79	0.002 70	0.021 79	0.000 78	0.045 79	0.024 79	0.238 60	0.065 77	0.000 78	0.014 78	0.107 79	0.020 79	0.110 79	0.006 79

Method	avg iou	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	floor	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	wall	window

Virtual MVFusion (R)	0.745 1	0.861 1	0.839 1	0.881 1	0.672 2	0.512 1	0.422 19	0.898 1	0.723 1	0.714 1	0.954 2	0.454 1	0.509 1	0.773 1	0.895 1	0.756 1	0.820 1	0.653 1	0.935 1	0.891 1	0.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2D	0.670 2	0.822 3	0.795 3	0.836 2	0.659 3	0.481 2	0.451 15	0.769 5	0.656 3	0.567 4	0.931 3	0.395 6	0.390 6	0.700 4	0.534 4	0.689 11	0.770 2	0.574 3	0.865 11	0.831 3	0.675 6
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MVF-GNN（2D）	0.636 3	0.606 16	0.794 4	0.434 17	0.688 1	0.337 8	0.464 14	0.798 4	0.632 5	0.589 3	0.908 9	0.420 2	0.329 14	0.743 2	0.594 2	0.738 2	0.676 5	0.527 4	0.906 2	0.818 6	0.715 3

CU-Hybrid-2D Net	0.636 3	0.825 2	0.820 2	0.179 25	0.648 4	0.463 3	0.549 2	0.742 9	0.676 2	0.628 2	0.961 1	0.420 2	0.379 7	0.684 8	0.381 20	0.732 3	0.723 3	0.599 2	0.827 18	0.851 2	0.634 9

DVEFormer	0.626 5	0.616 12	0.764 6	0.690 5	0.583 11	0.322 14	0.540 3	0.809 3	0.593 7	0.502 12	0.900 14	0.374 9	0.433 3	0.660 9	0.528 5	0.665 19	0.663 6	0.491 9	0.871 10	0.810 9	0.705 4

CMX	0.613 6	0.681 9	0.725 13	0.502 13	0.634 6	0.297 19	0.478 12	0.830 2	0.651 4	0.537 7	0.924 4	0.375 7	0.315 16	0.686 7	0.451 15	0.714 5	0.543 23	0.504 6	0.894 7	0.823 5	0.688 5

DMMF_3d	0.605 7	0.651 10	0.744 11	0.782 3	0.637 5	0.387 4	0.536 5	0.732 10	0.590 8	0.540 6	0.856 23	0.359 12	0.306 17	0.596 16	0.539 3	0.627 22	0.706 4	0.497 8	0.785 23	0.757 21	0.476 24

EMSANet	0.600 8	0.716 4	0.746 10	0.395 20	0.614 9	0.382 5	0.523 6	0.713 13	0.571 12	0.503 10	0.922 7	0.404 5	0.397 5	0.655 10	0.400 17	0.626 23	0.663 6	0.469 14	0.900 4	0.827 4	0.577 16
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
MCA-Net	0.595 9	0.533 22	0.756 9	0.746 4	0.590 10	0.334 10	0.506 9	0.670 17	0.587 9	0.500 13	0.905 11	0.366 11	0.352 10	0.601 15	0.506 9	0.669 17	0.648 10	0.501 7	0.839 17	0.769 17	0.516 23

RFBNet	0.592 10	0.616 12	0.758 8	0.659 6	0.581 12	0.330 11	0.469 13	0.655 20	0.543 15	0.524 8	0.924 4	0.355 14	0.336 12	0.572 19	0.479 11	0.671 15	0.648 10	0.480 11	0.814 21	0.814 7	0.614 12

FAN_NV_RVC	0.586 11	0.510 23	0.764 6	0.079 28	0.620 8	0.330 11	0.494 10	0.753 7	0.573 10	0.556 5	0.884 18	0.405 4	0.303 18	0.718 3	0.452 14	0.672 14	0.658 8	0.509 5	0.898 5	0.813 8	0.727 2

WSGFormer	0.585 12	0.706 5	0.708 18	0.434 17	0.574 14	0.283 22	0.538 4	0.759 6	0.542 17	0.482 17	0.924 4	0.351 16	0.333 13	0.614 12	0.393 18	0.692 10	0.551 22	0.461 15	0.874 9	0.809 10	0.673 7

DCRedNet	0.583 13	0.682 8	0.723 14	0.542 12	0.510 22	0.310 16	0.451 15	0.668 18	0.549 14	0.520 9	0.920 8	0.375 7	0.446 2	0.528 22	0.417 16	0.670 16	0.577 19	0.478 12	0.862 12	0.806 11	0.628 11

MIX6D_RVC	0.582 14	0.695 6	0.687 19	0.225 23	0.632 7	0.328 13	0.550 1	0.748 8	0.623 6	0.494 16	0.890 16	0.350 17	0.254 25	0.688 6	0.454 13	0.716 4	0.597 18	0.489 10	0.881 8	0.768 18	0.575 17

SSMA	0.577 15	0.695 6	0.716 16	0.439 15	0.563 16	0.314 15	0.444 17	0.719 11	0.551 13	0.503 10	0.887 17	0.346 18	0.348 11	0.603 14	0.353 22	0.709 6	0.600 16	0.457 16	0.901 3	0.786 13	0.599 15
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
DMMF	0.567 16	0.623 11	0.767 5	0.238 22	0.571 15	0.347 6	0.413 21	0.719 11	0.472 22	0.418 24	0.895 15	0.357 13	0.260 24	0.696 5	0.523 8	0.666 18	0.642 12	0.437 20	0.895 6	0.793 12	0.603 14

UNIV_CNP_RVC_UE	0.566 17	0.569 21	0.686 21	0.435 16	0.524 19	0.294 20	0.421 20	0.712 14	0.543 15	0.463 19	0.872 19	0.320 19	0.363 9	0.611 13	0.477 12	0.686 12	0.627 13	0.443 19	0.862 12	0.775 16	0.639 8

EMSAFormer	0.564 18	0.581 18	0.736 12	0.564 11	0.546 18	0.219 25	0.517 7	0.675 16	0.486 21	0.427 23	0.904 12	0.352 15	0.320 15	0.589 17	0.528 5	0.708 7	0.464 26	0.413 24	0.847 16	0.786 13	0.611 13

SN_RN152pyrx8_RVC	0.546 19	0.572 19	0.663 23	0.638 8	0.518 20	0.298 18	0.366 26	0.633 23	0.510 19	0.446 21	0.864 21	0.296 22	0.267 21	0.542 21	0.346 23	0.704 8	0.575 20	0.431 21	0.853 15	0.766 19	0.630 10

UDSSEG_RVC	0.545 20	0.610 15	0.661 24	0.588 9	0.556 17	0.268 23	0.482 11	0.642 22	0.572 11	0.475 18	0.836 25	0.312 20	0.367 8	0.630 11	0.189 25	0.639 21	0.495 25	0.452 17	0.826 19	0.756 22	0.541 19

segfomer with 6d	0.542 21	0.594 17	0.687 19	0.146 26	0.579 13	0.308 17	0.515 8	0.703 15	0.472 22	0.498 14	0.868 20	0.369 10	0.282 19	0.589 17	0.390 19	0.701 9	0.556 21	0.416 23	0.860 14	0.759 20	0.539 21

FuseNet	0.535 22	0.570 20	0.681 22	0.182 24	0.512 21	0.290 21	0.431 18	0.659 19	0.504 20	0.495 15	0.903 13	0.308 21	0.428 4	0.523 23	0.365 21	0.676 13	0.621 15	0.470 13	0.762 24	0.779 15	0.541 19
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++	0.503 23	0.613 14	0.722 15	0.418 19	0.358 28	0.337 8	0.370 25	0.479 26	0.443 24	0.368 26	0.907 10	0.207 25	0.213 27	0.464 26	0.525 7	0.618 24	0.657 9	0.450 18	0.788 22	0.721 25	0.408 27
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)	0.498 24	0.481 26	0.612 25	0.579 10	0.456 24	0.343 7	0.384 23	0.623 24	0.525 18	0.381 25	0.845 24	0.254 24	0.264 23	0.557 20	0.182 26	0.581 26	0.598 17	0.429 22	0.760 25	0.661 27	0.446 26
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVC	0.485 25	0.505 24	0.709 17	0.092 27	0.427 25	0.241 24	0.411 22	0.654 21	0.385 28	0.457 20	0.861 22	0.053 28	0.279 20	0.503 24	0.481 10	0.645 20	0.626 14	0.365 26	0.748 26	0.725 24	0.529 22
John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet	0.475 26	0.490 25	0.581 26	0.289 21	0.507 23	0.067 28	0.379 24	0.610 25	0.417 26	0.435 22	0.822 27	0.278 23	0.267 21	0.503 24	0.228 24	0.616 25	0.533 24	0.375 25	0.820 20	0.729 23	0.560 18

Enet (reimpl)	0.376 27	0.264 28	0.452 28	0.452 14	0.365 26	0.181 26	0.143 28	0.456 27	0.409 27	0.346 27	0.769 28	0.164 26	0.218 26	0.359 27	0.123 28	0.403 28	0.381 28	0.313 28	0.571 27	0.685 26	0.472 25
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
ScanNet (2d proj)	0.330 28	0.293 27	0.521 27	0.657 7	0.361 27	0.161 27	0.250 27	0.004 28	0.440 25	0.183 28	0.836 25	0.125 27	0.060 28	0.319 28	0.132 27	0.417 27	0.412 27	0.344 27	0.541 28	0.427 28	0.109 28
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17

Method	avg ap	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

EMSANet (Instance)	0.241 1	0.401 1	0.439 1	0.085 1	0.242 1	0.220 1	0.081 1	0.289 2	0.117 2	0.121 1	0.182 1	0.126 1	0.346 1	0.181 2	0.181 2	0.358 1	0.156 1	0.675 2	0.131 1
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
UniDet_RVC	0.205 2	0.381 2	0.323 3	0.037 3	0.226 3	0.177 3	0.063 2	0.277 3	0.120 1	0.067 3	0.131 3	0.074 3	0.317 2	0.080 3	0.235 1	0.289 3	0.141 3	0.678 1	0.080 3

FKNet	0.204 3	0.334 3	0.358 2	0.038 2	0.234 2	0.184 2	0.025 3	0.318 1	0.042 4	0.088 2	0.141 2	0.053 4	0.300 3	0.207 1	0.171 3	0.292 2	0.149 2	0.636 3	0.109 2

MaskRCNN_ScanNet	0.119 4	0.129 4	0.212 4	0.002 4	0.112 4	0.148 4	0.014 4	0.205 4	0.044 3	0.066 4	0.078 4	0.095 2	0.142 4	0.030 4	0.128 4	0.139 4	0.080 4	0.459 4	0.057 4
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17

Presenting the ScanNet200 Benchmark

ScanNet200 Benchmark

ScanNet200 3D Semantic Label Benchmark

ScanNet200 3D Semantic Instance Benchmark

ScanNet Benchmark

3D Semantic Label Benchmark

3D Semantic Instance Benchmark

2D Semantic Label Benchmark

2D Semantic Instance Benchmark

Scene Type Classification Benchmark

Method	avg iou	apartment	bathroom	bedroom / hotel	bookstore / library	conference room	copy/mail room	hallway	kitchen	laundry room	living room / lounge	misc	office	storage / basement / garage

LAST-PCL-type	0.738 1	0.250 3	1.000 1	0.895 1	1.000 1	1.000 1	1.000 1	0.500 1	1.000 1	0.500 2	0.842 1	0.000 2	0.941 1	0.667 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, and Jian Zhang: Language-Assisted 3D Scene Understanding. arxiv23.12
multi-task	0.646 2	0.500 1	1.000 1	0.789 2	0.333 3	0.667 3	1.000 1	0.500 1	1.000 1	1.000 1	0.778 2	0.000 2	0.833 2	0.000 3
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE	0.556 3	0.500 1	0.938 3	0.778 3	0.667 2	1.000 1	0.250 3	0.500 1	0.750 3	0.333 3	0.500 4	0.000 2	0.812 3	0.200 2

SE-ResNeXt-SSMA	0.355 4	0.000 5	0.684 4	0.696 4	0.200 5	0.500 4	0.200 4	0.500 1	0.429 4	0.200 4	0.545 3	0.111 1	0.556 4	0.000 3
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet	0.231 5	0.200 4	0.481 5	0.346 5	0.250 4	0.250 5	0.000 5	0.500 1	0.333 5	0.000 5	0.357 5	0.000 2	0.286 5	0.000 3