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

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

PPT-SpUNet-F.T.	0.332 3	0.556 2	0.270 1	0.123 5	0.816 2	0.682 2	0.946 1	0.549 3	0.657 3	0.756 1	0.459 3	0.376 4	0.550 3	0.001 7	0.807 1	0.616 1	0.727 3	0.267 2	0.691 2	0.942 4	0.530 4	0.872 3	0.874 2	0.330 3	0.542 5	0.374 3	0.792 2	0.400 5	0.673 1	0.572 2	0.433 1	0.793 3	0.623 2	0.008 8	0.351 3	0.594 5	0.000 1	0.783 4	0.876 1	0.833 2	0.213 2	0.000 5	0.537 2	0.091 2	0.519 1	0.304 2	0.620 4	0.942 1	0.264 1	0.124 3	0.855 1	0.695 1	0.086 3	0.646 3	0.506 7	0.658 2	0.535 2	0.715 2	0.314 1	0.000 3	0.241 1	0.608 2	0.897 1	0.359 3	0.858 3	0.000 1	0.076 8	0.611 4	0.392 3	0.509 3	0.378 2	0.000 1	0.579 1	0.565 7	0.000 4	0.000 4	0.000 1	0.000 3	0.755 3	0.806 4	0.661 1	0.572 6	0.350 4	0.181 4	0.660 3	0.300 5	0.000 3	0.000 3	0.000 1	0.023 5	0.000 2	0.042 7	0.930 1	0.000 1	0.000 6	0.077 4	0.584 2	0.392 3	0.339 6	0.185 3	0.171 4	0.308 1	0.006 7	0.563 3	0.256 3	0.150 1	0.000 3	0.002 7	0.345 6	0.000 1	0.045 5	0.197 1	0.063 3	0.323 5	0.453 1	0.600 3	0.163 5	0.037 6	0.349 2	0.000 1	0.672 1	0.679 1	0.753 1	0.000 1	0.000 5	0.000 3	0.117 1	0.000 4	0.000 3	0.291 5	0.000 3	0.000 2	0.039 1	0.000 1	0.000 1	0.899 2	0.000 1	0.374 5	0.000 1	0.000 4	0.545 2	0.000 1	0.634 1	0.000 1	0.000 1	0.074 5	0.223 3	0.914 3	0.000 4	0.021 2	0.000 1	0.000 4	0.000 1	0.112 3	0.498 5	0.649 1	0.383 5	0.095 1	0.135 7	0.449 5	0.432 4	0.008 3	0.000 1	0.518 2	0.000 1	0.000 2	0.000 4	0.796 1	0.000 2	0.000 1	0.000 1	0.138 5	0.000 2	0.000 2	0.000 3	0.000 1
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.
OctFormer ScanNet200	0.326 4	0.539 4	0.265 3	0.131 3	0.806 3	0.670 4	0.943 3	0.535 4	0.662 1	0.705 7	0.423 4	0.407 3	0.505 5	0.003 6	0.765 4	0.582 2	0.686 6	0.227 7	0.680 3	0.943 3	0.601 1	0.854 5	0.892 1	0.335 1	0.417 8	0.357 4	0.724 5	0.453 3	0.632 3	0.596 1	0.432 2	0.783 4	0.512 8	0.021 6	0.244 6	0.637 1	0.000 1	0.787 3	0.873 3	0.743 6	0.000 8	0.000 5	0.534 3	0.110 1	0.499 2	0.289 3	0.626 3	0.620 6	0.168 8	0.204 1	0.849 2	0.679 2	0.117 1	0.633 4	0.684 1	0.650 3	0.552 1	0.684 5	0.312 2	0.000 3	0.175 3	0.429 4	0.865 2	0.413 1	0.837 4	0.000 1	0.145 3	0.626 3	0.451 2	0.487 4	0.513 1	0.000 1	0.529 3	0.613 4	0.000 4	0.033 2	0.000 1	0.000 3	0.828 1	0.871 1	0.622 2	0.587 4	0.411 2	0.137 6	0.645 5	0.343 3	0.000 3	0.000 3	0.000 1	0.022 6	0.000 2	0.026 8	0.829 5	0.000 1	0.022 4	0.089 3	0.842 1	0.253 7	0.318 8	0.296 1	0.178 3	0.291 2	0.224 1	0.584 2	0.200 6	0.132 4	0.000 3	0.128 2	0.227 7	0.000 1	0.230 4	0.047 5	0.149 2	0.331 4	0.412 3	0.618 2	0.164 4	0.102 4	0.522 1	0.000 1	0.655 2	0.378 4	0.469 6	0.000 1	0.000 5	0.000 3	0.105 3	0.000 4	0.000 3	0.483 2	0.000 3	0.000 2	0.028 2	0.000 1	0.000 1	0.906 1	0.000 1	0.339 6	0.000 1	0.000 4	0.457 4	0.000 1	0.612 3	0.000 1	0.000 1	0.408 1	0.000 7	0.900 4	0.000 4	0.000 3	0.000 1	0.029 3	0.000 1	0.074 8	0.455 6	0.479 2	0.427 4	0.079 6	0.140 6	0.496 3	0.414 5	0.022 1	0.000 1	0.471 5	0.000 1	0.000 2	0.000 4	0.722 2	0.000 2	0.000 1	0.000 1	0.138 5	0.000 2	0.000 2	0.000 3	0.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CeCo	0.340 1	0.551 3	0.247 4	0.181 1	0.784 4	0.661 5	0.939 4	0.564 2	0.624 4	0.721 3	0.484 2	0.429 2	0.575 1	0.027 4	0.774 3	0.503 5	0.753 1	0.242 4	0.656 4	0.945 2	0.534 2	0.865 4	0.860 3	0.177 8	0.616 3	0.400 2	0.818 1	0.579 1	0.615 4	0.367 5	0.408 3	0.726 6	0.633 1	0.162 1	0.360 2	0.619 2	0.000 1	0.828 1	0.873 3	0.924 1	0.109 5	0.083 2	0.564 1	0.057 8	0.475 4	0.266 4	0.781 1	0.767 3	0.257 3	0.100 5	0.825 3	0.663 4	0.048 8	0.620 6	0.551 4	0.595 6	0.532 3	0.692 4	0.246 3	0.000 3	0.213 2	0.615 1	0.861 4	0.376 2	0.900 1	0.000 1	0.102 7	0.660 2	0.321 6	0.547 1	0.226 4	0.000 1	0.311 4	0.742 1	0.011 3	0.006 3	0.000 1	0.000 3	0.546 8	0.824 3	0.345 5	0.665 1	0.450 1	0.435 1	0.683 1	0.411 1	0.338 1	0.000 3	0.000 1	0.030 4	0.000 2	0.068 4	0.892 3	0.000 1	0.063 2	0.000 7	0.257 4	0.304 6	0.387 3	0.079 6	0.228 1	0.190 3	0.000 8	0.586 1	0.347 1	0.133 3	0.000 3	0.037 4	0.377 4	0.000 1	0.384 2	0.006 7	0.003 5	0.421 1	0.410 4	0.643 1	0.171 3	0.121 2	0.142 7	0.000 1	0.510 6	0.447 3	0.474 5	0.000 1	0.000 5	0.286 1	0.083 4	0.000 4	0.000 3	0.603 1	0.096 1	0.063 1	0.000 4	0.000 1	0.000 1	0.898 3	0.000 1	0.429 2	0.000 1	0.400 1	0.550 1	0.000 1	0.633 2	0.000 1	0.000 1	0.377 2	0.000 7	0.916 1	0.000 4	0.000 3	0.000 1	0.000 4	0.000 1	0.102 7	0.499 4	0.296 5	0.463 3	0.089 4	0.304 1	0.740 1	0.401 7	0.010 2	0.000 1	0.560 1	0.000 1	0.000 2	0.709 1	0.652 4	0.000 2	0.000 1	0.000 1	0.143 3	0.000 2	0.000 2	0.609 1	0.000 1
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
AWCS	0.305 5	0.508 5	0.225 5	0.142 2	0.782 5	0.634 8	0.937 5	0.489 6	0.578 5	0.721 3	0.364 6	0.355 5	0.515 4	0.023 5	0.764 5	0.523 4	0.707 5	0.264 3	0.633 5	0.922 5	0.507 6	0.886 1	0.804 6	0.179 6	0.436 7	0.300 5	0.656 7	0.529 2	0.501 6	0.394 4	0.296 7	0.820 1	0.603 3	0.131 2	0.179 8	0.619 2	0.000 1	0.707 7	0.865 5	0.773 3	0.171 3	0.010 4	0.484 5	0.063 6	0.463 5	0.254 5	0.332 7	0.649 5	0.220 5	0.100 5	0.729 6	0.613 6	0.071 6	0.582 7	0.628 2	0.702 1	0.424 6	0.749 1	0.137 6	0.000 3	0.142 5	0.360 5	0.863 3	0.305 5	0.877 2	0.000 1	0.173 1	0.606 5	0.337 5	0.478 5	0.154 6	0.000 1	0.253 5	0.664 2	0.000 4	0.000 4	0.000 1	0.000 3	0.626 6	0.782 5	0.302 7	0.602 2	0.185 7	0.282 3	0.651 4	0.317 4	0.000 3	0.000 3	0.000 1	0.022 6	0.000 2	0.154 1	0.876 4	0.000 1	0.014 5	0.063 6	0.029 8	0.553 1	0.467 2	0.084 5	0.124 5	0.157 7	0.049 6	0.373 5	0.252 4	0.097 6	0.000 3	0.219 1	0.542 1	0.000 1	0.392 1	0.172 3	0.000 7	0.339 3	0.417 2	0.533 6	0.093 6	0.115 3	0.195 5	0.000 1	0.516 5	0.288 7	0.741 2	0.000 1	0.001 4	0.233 2	0.056 5	0.000 4	0.159 1	0.334 4	0.077 2	0.000 2	0.000 4	0.000 1	0.000 1	0.749 5	0.000 1	0.411 3	0.000 1	0.008 3	0.452 5	0.000 1	0.595 4	0.000 1	0.000 1	0.220 4	0.006 5	0.894 6	0.006 3	0.000 3	0.000 1	0.000 4	0.000 1	0.112 3	0.504 3	0.404 3	0.551 1	0.093 3	0.129 8	0.484 4	0.381 8	0.000 6	0.000 1	0.396 6	0.000 1	0.000 2	0.620 2	0.402 8	0.000 2	0.000 1	0.000 1	0.142 4	0.000 2	0.000 2	0.512 2	0.000 1

LGround	0.272 6	0.485 6	0.184 6	0.106 6	0.778 6	0.676 3	0.932 6	0.479 8	0.572 6	0.718 5	0.399 5	0.265 6	0.453 7	0.085 2	0.745 6	0.446 6	0.726 4	0.232 6	0.622 6	0.901 6	0.512 5	0.826 6	0.786 7	0.178 7	0.549 4	0.277 6	0.659 6	0.381 6	0.518 5	0.295 8	0.323 5	0.777 5	0.599 4	0.028 4	0.321 4	0.363 7	0.000 1	0.708 6	0.858 6	0.746 5	0.063 6	0.022 3	0.457 6	0.077 5	0.476 3	0.243 6	0.402 5	0.397 8	0.233 4	0.077 8	0.720 8	0.610 7	0.103 2	0.629 5	0.437 8	0.626 5	0.446 5	0.702 3	0.190 5	0.005 1	0.058 7	0.322 6	0.702 7	0.244 6	0.768 6	0.000 1	0.134 5	0.552 6	0.279 7	0.395 6	0.147 7	0.000 1	0.207 6	0.612 5	0.000 4	0.000 4	0.000 1	0.000 3	0.658 5	0.566 6	0.323 6	0.525 8	0.229 6	0.179 5	0.467 8	0.154 7	0.000 3	0.002 1	0.000 1	0.051 1	0.000 2	0.127 2	0.703 6	0.000 1	0.000 6	0.216 1	0.112 7	0.358 4	0.547 1	0.187 2	0.092 7	0.156 8	0.055 5	0.296 6	0.252 4	0.143 2	0.000 3	0.014 5	0.398 3	0.000 1	0.028 7	0.173 2	0.000 7	0.265 7	0.348 6	0.415 8	0.179 2	0.019 7	0.218 4	0.000 1	0.597 4	0.274 8	0.565 4	0.000 1	0.012 3	0.000 3	0.039 7	0.022 2	0.000 3	0.117 6	0.000 3	0.000 2	0.000 4	0.000 1	0.000 1	0.324 7	0.000 1	0.384 4	0.000 1	0.000 4	0.251 8	0.000 1	0.566 5	0.000 1	0.000 1	0.066 6	0.404 1	0.886 7	0.199 1	0.000 3	0.000 1	0.059 2	0.000 1	0.136 1	0.540 1	0.127 8	0.295 6	0.085 5	0.143 5	0.514 2	0.413 6	0.000 6	0.000 1	0.498 3	0.000 1	0.000 2	0.000 4	0.623 5	0.000 2	0.000 1	0.000 1	0.132 7	0.000 2	0.000 2	0.000 3	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrain	0.249 8	0.455 8	0.171 7	0.079 8	0.766 8	0.659 6	0.930 8	0.494 5	0.542 8	0.700 8	0.314 8	0.215 8	0.430 8	0.121 1	0.697 8	0.441 7	0.683 7	0.235 5	0.609 8	0.895 7	0.476 8	0.816 7	0.770 8	0.186 5	0.634 2	0.216 8	0.734 4	0.340 7	0.471 7	0.307 7	0.293 8	0.591 8	0.542 7	0.076 3	0.205 7	0.464 6	0.000 1	0.484 8	0.832 8	0.766 4	0.052 7	0.000 5	0.413 7	0.059 7	0.418 7	0.222 7	0.318 8	0.609 7	0.206 7	0.112 4	0.743 5	0.625 5	0.076 4	0.579 8	0.548 5	0.590 7	0.371 7	0.552 8	0.081 7	0.003 2	0.142 5	0.201 8	0.638 8	0.233 7	0.686 8	0.000 1	0.142 4	0.444 8	0.375 4	0.247 8	0.198 5	0.000 1	0.128 8	0.454 8	0.019 2	0.097 1	0.000 1	0.000 3	0.553 7	0.557 7	0.373 4	0.545 7	0.164 8	0.014 8	0.547 7	0.174 6	0.000 3	0.002 1	0.000 1	0.037 2	0.000 2	0.063 5	0.664 8	0.000 1	0.000 6	0.130 2	0.170 5	0.152 8	0.335 7	0.079 6	0.110 6	0.175 5	0.098 4	0.175 8	0.166 7	0.045 8	0.207 1	0.014 5	0.465 2	0.000 1	0.001 8	0.001 8	0.046 4	0.299 6	0.327 7	0.537 5	0.033 7	0.012 8	0.186 6	0.000 1	0.205 7	0.377 5	0.463 7	0.000 1	0.058 2	0.000 3	0.055 6	0.041 1	0.000 3	0.105 7	0.000 3	0.000 2	0.000 4	0.000 1	0.000 1	0.398 6	0.000 1	0.308 8	0.000 1	0.000 4	0.319 6	0.000 1	0.543 6	0.000 1	0.000 1	0.062 7	0.004 6	0.862 8	0.000 4	0.000 3	0.000 1	0.000 4	0.000 1	0.123 2	0.316 7	0.225 6	0.250 7	0.094 2	0.180 4	0.332 7	0.441 3	0.000 6	0.000 1	0.310 8	0.000 1	0.000 2	0.000 4	0.592 6	0.000 2	0.000 1	0.000 1	0.203 1	0.000 2	0.000 2	0.000 3	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34D	0.253 7	0.463 7	0.154 8	0.102 7	0.771 7	0.650 7	0.932 6	0.483 7	0.571 7	0.710 6	0.331 7	0.250 7	0.492 6	0.044 3	0.703 7	0.419 8	0.606 8	0.227 7	0.621 7	0.865 8	0.531 3	0.771 8	0.813 5	0.291 4	0.484 6	0.242 7	0.612 8	0.282 8	0.440 8	0.351 6	0.299 6	0.622 7	0.593 5	0.027 5	0.293 5	0.310 8	0.000 1	0.757 5	0.858 6	0.737 7	0.150 4	0.164 1	0.368 8	0.084 3	0.381 8	0.142 8	0.357 6	0.720 4	0.214 6	0.092 7	0.724 7	0.596 8	0.056 7	0.655 2	0.525 6	0.581 8	0.352 8	0.594 7	0.056 8	0.000 3	0.014 8	0.224 7	0.772 6	0.205 8	0.720 7	0.000 1	0.159 2	0.531 7	0.163 8	0.294 7	0.136 8	0.000 1	0.169 7	0.589 6	0.000 4	0.000 4	0.000 1	0.002 1	0.663 4	0.466 8	0.265 8	0.582 5	0.337 5	0.016 7	0.559 6	0.084 8	0.000 3	0.000 3	0.000 1	0.036 3	0.000 2	0.125 3	0.670 7	0.000 1	0.102 1	0.071 5	0.164 6	0.406 2	0.386 4	0.046 8	0.068 8	0.159 6	0.117 2	0.284 7	0.111 8	0.094 7	0.000 3	0.000 8	0.197 8	0.000 1	0.044 6	0.013 6	0.002 6	0.228 8	0.307 8	0.588 4	0.025 8	0.545 1	0.134 8	0.000 1	0.655 2	0.302 6	0.282 8	0.000 1	0.060 1	0.000 3	0.035 8	0.000 4	0.000 3	0.097 8	0.000 3	0.000 2	0.005 3	0.000 1	0.000 1	0.096 8	0.000 1	0.334 7	0.000 1	0.000 4	0.274 7	0.000 1	0.513 8	0.000 1	0.000 1	0.280 3	0.194 4	0.897 5	0.000 4	0.000 3	0.000 1	0.000 4	0.000 1	0.108 5	0.279 8	0.189 7	0.141 8	0.059 7	0.272 2	0.307 8	0.445 2	0.003 4	0.000 1	0.353 7	0.000 1	0.026 1	0.000 4	0.581 7	0.001 1	0.000 1	0.000 1	0.093 8	0.002 1	0.000 2	0.000 3	0.000 1
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 ap 25%	head ap 25%	common ap 25%	tail ap 25%	chair	table	door	couch	cabinet	shelf	desk	office chair	bed	pillow	sink	picture	window	toilet	bookshelf	monitor	curtain	book	coffee table	box	refrigerator	lamp	kitchen cabinet	towel	clothes	tv	nightstand	counter	dresser	stool	cushion	plant	ceiling	bathtub	end table	dining table	keyboard	bag	backpack	toilet paper	printer	tv stand	whiteboard	blanket	shower curtain	trash can	closet	stairs	microwave	stove	shoe	computer tower	bottle	bin	bench	board	washing machine	mirror	copier	basket	sofa chair	file cabinet	fan	laptop	shower	paper	person	paper towel dispenser	oven	blinds	rack	plate	blackboard	piano	suitcase	radiator	recycling bin	wardrobe	soap dispenser	telephone	bucket	clock	stand	light	laundry basket	pipe	clothes dryer	seat	speaker	column	bicycle	ladder	bathroom stall	shower wall	cup	jacket	storage bin	coffee maker	dishwasher	paper towel roll	machine	mat	windowsill	bar	toaster	bulletin board	ironing board	kitchen counter	doorframe	toilet paper dispenser	mini fridge	fire extinguisher	ball	hat	shower curtain rod	water cooler	paper cutter	tray	ledge	mouse	cart	storage container	scale	tissue box	light switch	power outlet	decoration	sign	projector	vacuum cleaner	candle	plunger	stuffed animal	headphones	dish rack	broom	range hood	water bottle	vent	shower floor	water pitcher	mailbox	bowl	paper bag	music stand	projector screen	laundry detergent	object	bathroom vanity	laundry hamper	bathroom stall door	ceiling light	trash bin	dumbbell	stair rail	tube	bathroom cabinet	closet rod	coffee kettle	structure	shower head	keyboard piano	case of water bottles	coat rack	storage organizer	folded chair	fire alarm	power strip	calendar	poster

TD3D Scannet200	0.379 2	0.603 2	0.306 2	0.190 2	0.885 1	0.755 1	0.800 2	0.958 1	0.390 2	0.260 2	0.866 2	0.232 1	0.979 2	0.523 3	0.869 3	0.559 5	0.689 2	1.000 1	0.795 1	0.905 2	0.748 1	0.173 5	0.825 1	0.173 2	0.970 1	0.457 1	0.615 2	0.456 2	0.200 1	0.621 4	0.906 2	0.553 1	0.517 1	0.510 1	0.220 2	0.715 1	0.706 2	1.000 1	0.113 2	0.792 1	0.717 2	0.073 2	0.635 2	0.557 1	0.638 1	1.000 1	0.205 5	0.146 3	1.000 1	0.769 5	0.186 2	1.000 1	0.710 5	0.778 1	0.415 1	0.834 4	0.226 2	0.021 2	0.590 2	0.356 2	0.817 1	0.477 5	1.000 1	0.000 1	0.635 1	0.843 2	0.427 1	0.270 4	0.125 2	0.000 2	0.102 3	1.000 1	0.125 2	0.000 2	0.000 1	0.000 2	0.000 3	0.125 4	0.370 3	0.622 5	0.221 1	0.196 2	0.836 1	0.288 2	0.000 2	0.093 2	0.020 2	0.294 2	0.000 1	0.075 2	0.667 1	0.038 1	0.111 1	0.250 4	0.000 4	0.526 2	0.495 3	0.908 1	0.111 3	0.259 1	0.003 3	0.667 2	0.045 5	0.000 2	0.000 1	0.400 1	0.274 3	0.000 1	0.274 2	0.226 2	0.000 1	0.520 2	0.302 5	0.731 2	0.103 3	0.458 1	0.500 1	0.000 1	1.000 1	0.472 1	0.792 3	0.000 1	0.088 2	0.061 2	0.250 1	0.009 2	0.250 2	0.333 3	0.181 2	0.396 2	0.051 2	0.012 1	0.000 1	0.458 4	0.000 1	0.424 5	0.000 1	0.101 2	0.390 5	0.000 1	0.833 2	0.000 1	0.000 1	0.857 2	0.222 3	1.000 1	0.000 1	0.003 2	0.000 1	0.000 2	0.000 1	0.102 2	0.275 5	0.400 2	0.735 2	0.061 3	0.433 3	0.533 3	0.625 1	0.000 2	0.000 1	0.259 4	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	1.000 1	0.600 1	0.000 2	0.250 1	0.000 2	0.000 1

Mask3D Scannet200	0.445 1	0.653 1	0.392 1	0.254 1	0.844 2	0.746 2	0.818 1	0.888 4	0.556 1	0.262 1	0.890 1	0.025 2	1.000 1	0.608 1	0.930 1	0.694 3	0.721 1	0.930 5	0.686 3	0.966 1	0.615 4	0.440 1	0.725 4	0.201 1	0.890 3	0.414 4	0.827 1	0.552 1	0.158 5	0.806 1	0.924 1	0.042 3	0.512 2	0.412 5	0.226 1	0.604 3	0.830 1	1.000 1	0.125 1	0.792 1	0.815 1	0.097 1	0.648 1	0.551 2	0.354 4	1.000 1	0.630 1	0.241 2	1.000 1	0.853 1	0.204 1	0.974 4	0.841 1	0.778 1	0.358 2	0.927 1	0.300 1	0.045 1	0.640 1	0.363 1	0.745 2	0.710 1	1.000 1	0.000 1	0.330 2	0.943 1	0.315 2	0.600 1	1.000 1	0.027 1	0.080 5	0.556 5	0.500 1	0.409 1	0.000 1	0.194 1	1.000 1	0.500 1	0.493 2	0.761 2	0.053 4	0.042 3	0.780 2	0.454 1	0.009 1	0.333 1	0.050 1	0.321 1	0.000 1	0.084 1	0.552 2	0.008 2	0.027 2	0.750 1	0.500 1	0.442 3	0.657 1	0.765 2	0.120 2	0.183 3	0.021 2	1.000 1	0.510 2	0.016 1	0.000 1	0.400 1	0.619 1	0.000 1	0.396 1	0.290 1	0.000 1	0.741 1	0.699 1	1.000 1	0.260 1	0.017 3	0.125 5	0.000 1	0.792 4	0.399 4	1.000 1	0.000 1	0.049 3	0.265 1	0.063 3	0.000 3	1.000 1	0.335 2	0.381 1	0.500 1	0.250 1	0.004 2	0.000 1	0.727 2	0.000 1	0.538 3	0.000 1	0.188 1	0.677 2	0.000 1	0.930 1	0.000 1	0.000 1	0.966 1	0.391 1	0.908 2	0.000 1	0.028 1	0.000 1	1.000 1	0.000 1	0.152 1	0.451 2	0.458 1	0.971 1	0.573 1	0.606 1	0.167 5	0.625 1	0.004 1	0.000 1	0.058 5	0.000 1	0.000 1	1.000 1	1.000 1	0.000 1	0.056 1	0.000 2	0.200 3	0.309 1	0.000 2	1.000 1	0.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
Minkowski 34D Inst.	0.280 4	0.488 4	0.192 5	0.124 4	0.804 4	0.518 4	0.772 5	0.904 3	0.337 5	0.191 4	0.443 4	0.000 3	0.861 4	0.502 4	0.868 4	0.669 4	0.587 4	0.997 3	0.467 5	0.828 5	0.732 2	0.342 3	0.745 3	0.119 5	0.918 2	0.404 5	0.419 4	0.398 3	0.172 3	0.618 5	0.743 4	0.167 2	0.077 5	0.500 2	0.000 3	0.568 4	0.506 5	1.000 1	0.044 4	0.000 3	0.502 4	0.010 4	0.593 4	0.284 5	0.305 5	0.903 5	0.213 4	0.142 4	0.981 3	0.790 4	0.000 4	1.000 1	0.715 4	0.538 5	0.346 4	0.830 5	0.067 3	0.000 3	0.400 3	0.074 4	0.333 4	0.551 2	1.000 1	0.000 1	0.292 3	0.777 4	0.118 5	0.317 3	0.100 4	0.000 2	0.191 2	0.648 3	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.500 1	0.213 5	0.825 1	0.021 5	0.333 1	0.648 5	0.098 4	0.000 2	0.000 3	0.000 3	0.077 3	0.000 1	0.000 5	0.150 5	0.000 3	0.000 3	0.000 5	0.225 2	0.281 4	0.447 4	0.000 5	0.090 4	0.148 4	0.000 4	0.479 5	0.542 1	0.000 2	0.000 1	0.200 3	0.131 5	0.000 1	0.250 3	0.000 4	0.000 1	0.159 5	0.396 4	0.677 3	0.021 4	0.000 4	0.500 1	0.000 1	1.000 1	0.442 3	0.125 5	0.000 1	0.000 4	0.000 3	0.000 4	0.333 1	0.000 3	0.528 1	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.200 5	0.000 1	0.516 4	0.000 1	0.000 3	0.500 3	0.000 1	0.833 2	0.000 1	0.000 1	0.286 4	0.083 4	0.750 3	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.059 5	0.445 3	0.200 3	0.535 4	0.070 2	0.167 4	0.385 4	0.375 3	0.000 2	0.000 1	0.333 3	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.200 3	0.000 2	0.000 2	0.000 2	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.	0.275 5	0.466 5	0.218 4	0.110 5	0.783 5	0.383 5	0.783 4	0.829 5	0.367 4	0.168 5	0.305 5	0.000 3	0.661 5	0.413 5	0.869 2	0.719 1	0.546 5	0.997 3	0.685 4	0.841 4	0.555 5	0.277 4	0.768 2	0.132 3	0.779 5	0.448 3	0.364 5	0.212 5	0.161 4	0.768 2	0.692 5	0.000 4	0.395 3	0.500 2	0.000 3	0.450 5	0.591 3	1.000 1	0.020 5	0.000 3	0.423 5	0.007 5	0.625 3	0.420 3	0.505 3	1.000 1	0.353 2	0.119 5	0.571 4	0.819 2	0.014 3	1.000 1	0.774 2	0.689 4	0.311 5	0.866 2	0.067 3	0.000 3	0.400 3	0.000 5	0.278 5	0.501 3	1.000 1	0.000 1	0.162 5	0.584 5	0.286 3	0.206 5	0.125 2	0.000 2	0.084 4	0.649 2	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.125 4	0.312 4	0.727 3	0.221 2	0.000 4	0.667 4	0.114 3	0.000 2	0.000 3	0.000 3	0.065 5	0.000 1	0.004 4	0.278 3	0.000 3	0.000 3	0.500 2	0.000 4	0.571 1	0.000 5	0.250 4	0.019 5	0.145 5	0.000 4	0.667 2	0.200 4	0.000 2	0.000 1	0.200 3	0.258 4	0.000 1	0.000 4	0.000 4	0.000 1	0.369 4	0.429 3	0.613 4	0.000 5	0.000 4	0.500 1	0.000 1	0.500 5	0.333 5	0.500 4	0.000 1	0.106 1	0.000 3	0.000 4	0.000 3	0.000 3	0.333 3	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.918 1	0.000 1	0.638 1	0.000 1	0.000 3	0.750 1	0.000 1	0.833 2	0.000 1	0.000 1	0.143 5	0.000 5	0.750 3	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.063 4	0.377 4	0.200 3	0.222 5	0.055 4	0.500 2	0.677 2	0.250 4	0.000 2	0.000 1	0.500 2	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.115 5	0.000 2	0.000 2	0.000 2	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.	0.314 3	0.529 3	0.225 3	0.155 3	0.810 3	0.625 3	0.798 3	0.940 2	0.372 3	0.217 3	0.484 3	0.000 3	0.927 3	0.528 2	0.826 5	0.694 2	0.605 3	1.000 1	0.731 2	0.846 3	0.716 3	0.350 2	0.589 5	0.123 4	0.857 4	0.457 2	0.578 3	0.376 4	0.183 2	0.765 3	0.800 3	0.000 4	0.278 4	0.500 2	0.000 3	0.659 2	0.569 4	1.000 1	0.093 3	0.000 3	0.539 3	0.010 3	0.578 5	0.378 4	0.571 2	1.000 1	0.337 3	0.252 1	0.530 5	0.814 3	0.000 4	0.744 5	0.743 3	0.746 3	0.346 3	0.863 3	0.067 3	0.000 3	0.400 3	0.167 3	0.667 3	0.488 4	1.000 1	0.000 1	0.208 4	0.783 3	0.166 4	0.375 2	0.071 5	0.000 2	0.200 1	0.607 4	0.000 3	0.000 2	0.000 1	0.000 2	1.000 1	0.500 1	0.517 1	0.716 4	0.221 2	0.000 4	0.706 3	0.085 5	0.000 2	0.000 3	0.000 3	0.077 4	0.000 1	0.063 3	0.278 3	0.000 3	0.000 3	0.500 2	0.083 3	0.181 5	0.515 2	0.286 3	0.144 1	0.219 2	0.042 1	0.582 4	0.400 3	0.000 2	0.000 1	0.000 5	0.305 2	0.000 1	0.000 4	0.036 3	0.000 1	0.413 3	0.500 2	0.533 5	0.250 2	0.200 2	0.500 1	0.000 1	1.000 1	0.472 1	1.000 1	0.000 1	0.000 4	0.000 3	0.250 1	0.000 3	0.000 3	0.333 3	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.600 3	0.000 1	0.594 2	0.000 1	0.000 3	0.500 3	0.000 1	0.647 5	0.000 1	0.000 1	0.429 3	0.333 2	0.500 5	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.069 3	0.696 1	0.050 5	0.556 3	0.031 5	0.042 5	0.750 1	0.250 4	0.000 2	0.000 1	0.630 1	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.400 2	0.000 2	0.000 2	0.000 2	0.000 1
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

Mix3D	0.781 1	0.964 1	0.855 1	0.843 13	0.781 3	0.858 9	0.575 4	0.831 25	0.685 9	0.714 2	0.979 1	0.594 4	0.310 20	0.801 1	0.892 12	0.841 2	0.819 3	0.723 3	0.940 8	0.887 2	0.725 17
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 2	0.861 16	0.818 10	0.836 16	0.790 1	0.875 2	0.576 3	0.905 3	0.704 3	0.739 1	0.969 6	0.611 1	0.349 6	0.756 16	0.958 1	0.702 35	0.805 12	0.708 6	0.916 24	0.898 1	0.801 1

OctFormer	0.766 3	0.925 3	0.808 16	0.849 7	0.786 2	0.846 19	0.566 6	0.876 10	0.690 7	0.674 8	0.960 11	0.576 11	0.226 58	0.753 18	0.904 6	0.777 7	0.815 5	0.722 4	0.923 20	0.877 8	0.776 4
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint	0.766 3	0.932 2	0.794 25	0.829 19	0.751 15	0.854 11	0.540 14	0.903 4	0.630 26	0.672 9	0.963 9	0.565 15	0.357 4	0.788 2	0.900 8	0.737 19	0.802 13	0.685 12	0.950 2	0.887 2	0.780 2
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.
CU-Hybrid Net	0.764 5	0.924 4	0.819 8	0.840 14	0.757 10	0.853 13	0.580 1	0.848 19	0.709 2	0.643 16	0.958 14	0.587 8	0.295 26	0.753 18	0.884 16	0.758 13	0.815 5	0.725 2	0.927 18	0.867 14	0.743 9

OccuSeg+Semantic	0.764 5	0.758 52	0.796 23	0.839 15	0.746 17	0.907 1	0.562 7	0.850 18	0.680 11	0.672 9	0.978 2	0.610 2	0.335 11	0.777 5	0.819 37	0.847 1	0.830 1	0.691 10	0.972 1	0.885 4	0.727 15

O-CNN	0.762 7	0.924 4	0.823 5	0.844 12	0.770 5	0.852 14	0.577 2	0.847 21	0.711 1	0.640 20	0.958 14	0.592 5	0.217 64	0.762 13	0.888 13	0.758 13	0.813 8	0.726 1	0.932 16	0.868 13	0.744 8
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
OA-CNN-L_ScanNet20	0.756 8	0.783 38	0.826 4	0.858 4	0.776 4	0.837 25	0.548 11	0.896 7	0.649 19	0.675 7	0.962 10	0.586 9	0.335 11	0.771 8	0.802 41	0.770 9	0.787 27	0.691 10	0.936 11	0.880 7	0.761 6

PointTransformerV2	0.752 9	0.742 59	0.809 15	0.872 1	0.758 9	0.860 8	0.552 9	0.891 8	0.610 34	0.687 3	0.960 11	0.559 18	0.304 23	0.766 10	0.926 3	0.767 10	0.797 18	0.644 25	0.942 6	0.876 11	0.722 19
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 9	0.906 8	0.793 27	0.802 34	0.689 32	0.825 36	0.556 8	0.867 12	0.681 10	0.602 36	0.960 11	0.555 20	0.365 3	0.779 4	0.859 22	0.747 16	0.795 22	0.717 5	0.917 23	0.856 22	0.764 5
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
BPNet	0.749 11	0.909 6	0.818 10	0.811 27	0.752 13	0.839 24	0.485 39	0.842 22	0.673 12	0.644 15	0.957 17	0.528 29	0.305 22	0.773 7	0.859 22	0.788 4	0.818 4	0.693 9	0.916 24	0.856 22	0.723 18
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer	0.749 11	0.793 35	0.790 28	0.807 30	0.750 16	0.856 10	0.524 20	0.881 9	0.588 46	0.642 19	0.977 4	0.591 6	0.274 37	0.781 3	0.929 2	0.804 3	0.796 19	0.642 26	0.947 4	0.885 4	0.715 22
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP	0.748 13	0.623 85	0.804 18	0.859 3	0.745 18	0.824 38	0.501 29	0.912 2	0.690 7	0.685 5	0.956 19	0.567 14	0.320 17	0.768 9	0.918 4	0.720 25	0.802 13	0.676 15	0.921 21	0.881 6	0.779 3

StratifiedFormer	0.747 14	0.901 9	0.803 19	0.845 11	0.757 10	0.846 19	0.512 24	0.825 28	0.696 6	0.645 14	0.956 19	0.576 11	0.262 49	0.744 23	0.861 21	0.742 17	0.770 36	0.705 7	0.899 37	0.860 19	0.734 10
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
VMNet	0.746 15	0.870 14	0.838 2	0.858 4	0.729 23	0.850 16	0.501 29	0.874 11	0.587 47	0.658 12	0.956 19	0.564 16	0.299 24	0.765 11	0.900 8	0.716 28	0.812 9	0.631 31	0.939 9	0.858 20	0.709 23
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)
Virtual MVFusion	0.746 15	0.771 46	0.819 8	0.848 9	0.702 29	0.865 7	0.397 76	0.899 5	0.699 4	0.664 11	0.948 47	0.588 7	0.330 13	0.746 22	0.851 29	0.764 11	0.796 19	0.704 8	0.935 12	0.866 15	0.728 13
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
Retro-FPN	0.744 17	0.842 22	0.800 20	0.767 47	0.740 19	0.836 27	0.541 13	0.914 1	0.672 13	0.626 25	0.958 14	0.552 21	0.272 39	0.777 5	0.886 15	0.696 36	0.801 16	0.674 17	0.941 7	0.858 20	0.717 20
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 18	0.620 86	0.799 22	0.849 7	0.730 22	0.822 41	0.493 36	0.897 6	0.664 14	0.681 6	0.955 22	0.562 17	0.378 1	0.760 14	0.903 7	0.738 18	0.801 16	0.673 18	0.907 29	0.877 8	0.745 7
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet	0.742 19	0.816 30	0.806 17	0.807 30	0.752 13	0.828 34	0.575 4	0.839 24	0.699 4	0.637 21	0.954 28	0.520 31	0.320 17	0.755 17	0.834 33	0.760 12	0.772 33	0.676 15	0.915 26	0.862 17	0.717 20

SAT	0.742 19	0.860 17	0.765 41	0.819 22	0.769 6	0.848 17	0.533 16	0.829 26	0.663 15	0.631 23	0.955 22	0.586 9	0.274 37	0.753 18	0.896 10	0.729 20	0.760 43	0.666 20	0.921 21	0.855 24	0.733 11

LargeKernel3D	0.739 21	0.909 6	0.820 7	0.806 32	0.740 19	0.852 14	0.545 12	0.826 27	0.594 45	0.643 16	0.955 22	0.541 23	0.263 48	0.723 26	0.858 24	0.775 8	0.767 37	0.678 14	0.933 14	0.848 30	0.694 29
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 22	0.776 42	0.790 28	0.851 6	0.754 12	0.854 11	0.491 38	0.866 13	0.596 44	0.686 4	0.955 22	0.536 24	0.342 8	0.624 42	0.869 18	0.787 5	0.802 13	0.628 32	0.927 18	0.875 12	0.704 26

MinkowskiNet	0.736 22	0.859 18	0.818 10	0.832 18	0.709 27	0.840 23	0.521 22	0.853 17	0.660 17	0.643 16	0.951 37	0.544 22	0.286 31	0.731 24	0.893 11	0.675 44	0.772 33	0.683 13	0.874 57	0.852 28	0.727 15
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 24	0.890 10	0.837 3	0.864 2	0.726 24	0.873 3	0.530 19	0.824 29	0.489 78	0.647 13	0.978 2	0.609 3	0.336 10	0.624 42	0.733 51	0.758 13	0.776 31	0.570 57	0.949 3	0.877 8	0.728 13

PointTransformer++	0.725 25	0.727 67	0.811 14	0.819 22	0.765 7	0.841 22	0.502 28	0.814 34	0.621 29	0.623 27	0.955 22	0.556 19	0.284 32	0.620 44	0.866 19	0.781 6	0.757 46	0.648 23	0.932 16	0.862 17	0.709 23

SparseConvNet	0.725 25	0.647 82	0.821 6	0.846 10	0.721 25	0.869 4	0.533 16	0.754 49	0.603 40	0.614 29	0.955 22	0.572 13	0.325 15	0.710 27	0.870 17	0.724 23	0.823 2	0.628 32	0.934 13	0.865 16	0.683 32

MatchingNet	0.724 27	0.812 32	0.812 13	0.810 28	0.735 21	0.834 29	0.495 35	0.860 16	0.572 53	0.602 36	0.954 28	0.512 33	0.280 34	0.757 15	0.845 31	0.725 22	0.780 29	0.606 43	0.937 10	0.851 29	0.700 28

PNE	0.721 28	0.840 23	0.789 30	0.833 17	0.690 30	0.823 40	0.509 25	0.864 15	0.618 30	0.629 24	0.957 17	0.500 36	0.266 46	0.763 12	0.797 43	0.674 48	0.791 25	0.621 37	0.892 42	0.855 24	0.708 25

INS-Conv-semantic	0.717 29	0.751 55	0.759 44	0.812 26	0.704 28	0.868 5	0.537 15	0.842 22	0.609 36	0.608 32	0.953 31	0.534 26	0.293 27	0.616 45	0.864 20	0.719 27	0.793 23	0.640 27	0.933 14	0.845 34	0.663 37

PointMetaBase	0.714 30	0.835 24	0.785 31	0.821 20	0.684 34	0.846 19	0.531 18	0.865 14	0.614 31	0.596 40	0.953 31	0.500 36	0.246 54	0.674 28	0.888 13	0.692 37	0.764 39	0.624 34	0.849 72	0.844 35	0.675 34

contrastBoundary	0.705 31	0.769 49	0.775 36	0.809 29	0.687 33	0.820 44	0.439 64	0.812 35	0.661 16	0.591 42	0.945 55	0.515 32	0.171 82	0.633 39	0.856 25	0.720 25	0.796 19	0.668 19	0.889 45	0.847 31	0.689 30
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 32	0.774 44	0.800 20	0.793 38	0.760 8	0.847 18	0.471 42	0.802 38	0.463 85	0.634 22	0.968 8	0.491 40	0.271 41	0.726 25	0.910 5	0.706 32	0.815 5	0.551 68	0.878 52	0.833 36	0.570 68

RFCR	0.702 33	0.889 11	0.745 53	0.813 25	0.672 37	0.818 48	0.493 36	0.815 33	0.623 27	0.610 30	0.947 49	0.470 48	0.249 53	0.594 48	0.848 30	0.705 33	0.779 30	0.646 24	0.892 42	0.823 42	0.611 51
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 34	0.825 28	0.796 23	0.723 54	0.716 26	0.832 30	0.433 66	0.816 31	0.634 24	0.609 31	0.969 6	0.418 73	0.344 7	0.559 60	0.833 34	0.715 29	0.808 11	0.560 62	0.902 34	0.847 31	0.680 33

JSENet	0.699 35	0.881 13	0.762 42	0.821 20	0.667 38	0.800 61	0.522 21	0.792 41	0.613 32	0.607 33	0.935 75	0.492 39	0.205 69	0.576 53	0.853 27	0.691 38	0.758 45	0.652 22	0.872 60	0.828 39	0.649 41
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 36	0.743 58	0.794 25	0.655 78	0.684 34	0.822 41	0.497 34	0.719 59	0.622 28	0.617 28	0.977 4	0.447 60	0.339 9	0.750 21	0.664 67	0.703 34	0.790 26	0.596 47	0.946 5	0.855 24	0.647 42
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 37	0.732 63	0.772 37	0.786 39	0.677 36	0.866 6	0.517 23	0.848 19	0.509 70	0.626 25	0.952 35	0.536 24	0.225 60	0.545 66	0.704 58	0.689 41	0.810 10	0.564 61	0.903 33	0.854 27	0.729 12
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 38	0.884 12	0.754 48	0.795 37	0.647 44	0.818 48	0.422 68	0.802 38	0.612 33	0.604 34	0.945 55	0.462 51	0.189 77	0.563 59	0.853 27	0.726 21	0.765 38	0.632 30	0.904 31	0.821 45	0.606 55
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 39	0.704 72	0.741 57	0.754 51	0.656 40	0.829 32	0.501 29	0.741 54	0.609 36	0.548 49	0.950 41	0.522 30	0.371 2	0.633 39	0.756 46	0.715 29	0.771 35	0.623 35	0.861 68	0.814 47	0.658 38
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 40	0.866 15	0.748 50	0.819 22	0.645 46	0.794 64	0.450 53	0.802 38	0.587 47	0.604 34	0.945 55	0.464 50	0.201 72	0.554 62	0.840 32	0.723 24	0.732 55	0.602 45	0.907 29	0.822 44	0.603 58

DGNet	0.684 41	0.712 71	0.784 32	0.782 43	0.658 39	0.835 28	0.499 33	0.823 30	0.641 21	0.597 39	0.950 41	0.487 41	0.281 33	0.575 54	0.619 70	0.647 58	0.764 39	0.620 38	0.871 63	0.846 33	0.688 31

KP-FCNN	0.684 41	0.847 21	0.758 46	0.784 41	0.647 44	0.814 51	0.473 41	0.772 44	0.605 38	0.594 41	0.935 75	0.450 58	0.181 80	0.587 49	0.805 40	0.690 39	0.785 28	0.614 39	0.882 49	0.819 46	0.632 47
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++	0.684 41	0.728 66	0.757 47	0.776 44	0.690 30	0.804 58	0.464 47	0.816 31	0.577 52	0.587 43	0.945 55	0.508 35	0.276 36	0.671 29	0.710 56	0.663 50	0.750 49	0.589 52	0.881 50	0.832 38	0.653 40

Superpoint Network	0.683 44	0.851 20	0.728 61	0.800 36	0.653 42	0.806 56	0.468 44	0.804 36	0.572 53	0.602 36	0.946 52	0.453 57	0.239 57	0.519 71	0.822 35	0.689 41	0.762 42	0.595 49	0.895 40	0.827 40	0.630 48

PointContrast_LA_SEM	0.683 44	0.757 53	0.784 32	0.786 39	0.639 48	0.824 38	0.408 71	0.775 43	0.604 39	0.541 51	0.934 79	0.532 27	0.269 43	0.552 63	0.777 44	0.645 61	0.793 23	0.640 27	0.913 27	0.824 41	0.671 35

VI-PointConv	0.676 46	0.770 48	0.754 48	0.783 42	0.621 52	0.814 51	0.552 9	0.758 47	0.571 55	0.557 47	0.954 28	0.529 28	0.268 45	0.530 69	0.682 62	0.675 44	0.719 58	0.603 44	0.888 46	0.833 36	0.665 36
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 47	0.789 36	0.748 50	0.763 49	0.635 50	0.814 51	0.407 73	0.747 51	0.581 51	0.573 44	0.950 41	0.484 42	0.271 41	0.607 46	0.754 47	0.649 55	0.774 32	0.596 47	0.883 48	0.823 42	0.606 55

SALANet	0.670 48	0.816 30	0.770 39	0.768 46	0.652 43	0.807 55	0.451 50	0.747 51	0.659 18	0.545 50	0.924 85	0.473 47	0.149 92	0.571 56	0.811 39	0.635 64	0.746 50	0.623 35	0.892 42	0.794 59	0.570 68

PointASNL	0.666 49	0.703 73	0.781 34	0.751 53	0.655 41	0.830 31	0.471 42	0.769 45	0.474 81	0.537 53	0.951 37	0.475 46	0.279 35	0.635 37	0.698 61	0.675 44	0.751 48	0.553 67	0.816 79	0.806 51	0.703 27
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConv	0.666 49	0.781 39	0.759 44	0.699 63	0.644 47	0.822 41	0.475 40	0.779 42	0.564 58	0.504 67	0.953 31	0.428 67	0.203 71	0.586 51	0.754 47	0.661 51	0.753 47	0.588 53	0.902 34	0.813 49	0.642 43
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++	0.663 51	0.746 56	0.708 65	0.722 55	0.638 49	0.820 44	0.451 50	0.566 86	0.599 42	0.541 51	0.950 41	0.510 34	0.313 19	0.648 34	0.819 37	0.616 69	0.682 74	0.590 51	0.869 64	0.810 50	0.656 39
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net	0.658 52	0.778 40	0.702 68	0.806 32	0.619 53	0.813 54	0.468 44	0.693 67	0.494 73	0.524 59	0.941 66	0.449 59	0.298 25	0.510 73	0.821 36	0.675 44	0.727 57	0.568 59	0.826 77	0.803 53	0.637 45
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 53	0.698 74	0.743 55	0.650 79	0.564 70	0.820 44	0.505 27	0.758 47	0.631 25	0.479 72	0.945 55	0.480 44	0.226 58	0.572 55	0.774 45	0.690 39	0.735 53	0.614 39	0.853 71	0.776 74	0.597 61
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 54	0.752 54	0.734 59	0.664 76	0.583 65	0.815 50	0.399 75	0.754 49	0.639 22	0.535 55	0.942 64	0.470 48	0.309 21	0.665 30	0.539 76	0.650 54	0.708 63	0.635 29	0.857 70	0.793 61	0.642 43
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 55	0.778 40	0.731 60	0.699 63	0.577 66	0.829 32	0.446 55	0.736 55	0.477 80	0.523 61	0.945 55	0.454 55	0.269 43	0.484 80	0.749 50	0.618 67	0.738 51	0.599 46	0.827 76	0.792 64	0.621 50

PointConv-SFPN	0.641 56	0.776 42	0.703 67	0.721 56	0.557 73	0.826 35	0.451 50	0.672 71	0.563 59	0.483 71	0.943 63	0.425 70	0.162 87	0.644 35	0.726 52	0.659 52	0.709 62	0.572 56	0.875 55	0.786 69	0.559 74

MVPNet	0.641 56	0.831 25	0.715 63	0.671 73	0.590 61	0.781 70	0.394 77	0.679 69	0.642 20	0.553 48	0.937 72	0.462 51	0.256 50	0.649 33	0.406 89	0.626 65	0.691 71	0.666 20	0.877 53	0.792 64	0.608 54
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 58	0.717 70	0.701 69	0.692 66	0.576 67	0.801 60	0.467 46	0.716 60	0.563 59	0.459 77	0.953 31	0.429 66	0.169 84	0.581 52	0.854 26	0.605 70	0.710 60	0.550 69	0.894 41	0.793 61	0.575 66

FPConv	0.639 59	0.785 37	0.760 43	0.713 61	0.603 56	0.798 62	0.392 78	0.534 91	0.603 40	0.524 59	0.948 47	0.457 53	0.250 52	0.538 67	0.723 54	0.598 74	0.696 69	0.614 39	0.872 60	0.799 54	0.567 71
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 60	0.797 34	0.769 40	0.641 84	0.590 61	0.820 44	0.461 48	0.537 90	0.637 23	0.536 54	0.947 49	0.388 81	0.206 68	0.656 31	0.668 65	0.647 58	0.732 55	0.585 54	0.868 65	0.793 61	0.473 93

PointSPNet	0.637 61	0.734 62	0.692 76	0.714 60	0.576 67	0.797 63	0.446 55	0.743 53	0.598 43	0.437 82	0.942 64	0.403 77	0.150 91	0.626 41	0.800 42	0.649 55	0.697 68	0.557 65	0.846 73	0.777 73	0.563 72

SConv	0.636 62	0.830 26	0.697 72	0.752 52	0.572 69	0.780 72	0.445 57	0.716 60	0.529 64	0.530 56	0.951 37	0.446 61	0.170 83	0.507 75	0.666 66	0.636 63	0.682 74	0.541 74	0.886 47	0.799 54	0.594 62

Supervoxel-CNN	0.635 63	0.656 80	0.711 64	0.719 57	0.613 54	0.757 81	0.444 60	0.765 46	0.534 63	0.566 45	0.928 83	0.478 45	0.272 39	0.636 36	0.531 78	0.664 49	0.645 84	0.508 82	0.864 67	0.792 64	0.611 51

joint point-based	0.634 64	0.614 87	0.778 35	0.667 75	0.633 51	0.825 36	0.420 69	0.804 36	0.467 83	0.561 46	0.951 37	0.494 38	0.291 28	0.566 57	0.458 84	0.579 81	0.764 39	0.559 64	0.838 74	0.814 47	0.598 60
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 65	0.731 64	0.688 79	0.675 70	0.591 60	0.784 69	0.444 60	0.565 87	0.610 34	0.492 69	0.949 45	0.456 54	0.254 51	0.587 49	0.706 57	0.599 73	0.665 80	0.612 42	0.868 65	0.791 68	0.579 65

PointNet2-SFPN	0.631 66	0.771 46	0.692 76	0.672 71	0.524 78	0.837 25	0.440 63	0.706 65	0.538 62	0.446 79	0.944 61	0.421 72	0.219 63	0.552 63	0.751 49	0.591 77	0.737 52	0.543 73	0.901 36	0.768 76	0.557 75

APCF-Net	0.631 66	0.742 59	0.687 81	0.672 71	0.557 73	0.792 67	0.408 71	0.665 72	0.545 61	0.508 64	0.952 35	0.428 67	0.186 78	0.634 38	0.702 59	0.620 66	0.706 64	0.555 66	0.873 58	0.798 56	0.581 64
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF	0.631 66	0.626 84	0.745 53	0.801 35	0.607 55	0.751 82	0.506 26	0.729 58	0.565 57	0.491 70	0.866 99	0.434 62	0.197 75	0.595 47	0.630 69	0.709 31	0.705 65	0.560 62	0.875 55	0.740 84	0.491 88

FusionAwareConv	0.630 69	0.604 89	0.741 57	0.766 48	0.590 61	0.747 83	0.501 29	0.734 56	0.503 72	0.527 57	0.919 89	0.454 55	0.323 16	0.550 65	0.420 88	0.678 43	0.688 72	0.544 71	0.896 39	0.795 58	0.627 49
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 70	0.800 33	0.625 91	0.719 57	0.545 76	0.806 56	0.445 57	0.597 81	0.448 88	0.519 62	0.938 71	0.481 43	0.328 14	0.489 79	0.499 83	0.657 53	0.759 44	0.592 50	0.881 50	0.797 57	0.634 46

SegGroup_sem	0.627 71	0.818 29	0.747 52	0.701 62	0.602 57	0.764 78	0.385 82	0.629 78	0.490 76	0.508 64	0.931 82	0.409 75	0.201 72	0.564 58	0.725 53	0.618 67	0.692 70	0.539 75	0.873 58	0.794 59	0.548 78
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 72	0.830 26	0.694 74	0.757 50	0.563 71	0.772 76	0.448 54	0.647 75	0.520 66	0.509 63	0.949 45	0.431 65	0.191 76	0.496 77	0.614 71	0.647 58	0.672 78	0.535 77	0.876 54	0.783 70	0.571 67

HPEIN	0.618 73	0.729 65	0.668 82	0.647 81	0.597 59	0.766 77	0.414 70	0.680 68	0.520 66	0.525 58	0.946 52	0.432 63	0.215 65	0.493 78	0.599 72	0.638 62	0.617 89	0.570 57	0.897 38	0.806 51	0.605 57
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 74	0.858 19	0.772 37	0.489 96	0.532 77	0.792 67	0.404 74	0.643 77	0.570 56	0.507 66	0.935 75	0.414 74	0.046 101	0.510 73	0.702 59	0.602 72	0.705 65	0.549 70	0.859 69	0.773 75	0.534 81
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 75	0.760 51	0.667 83	0.649 80	0.521 79	0.793 65	0.457 49	0.648 74	0.528 65	0.434 84	0.947 49	0.401 78	0.153 90	0.454 82	0.721 55	0.648 57	0.717 59	0.536 76	0.904 31	0.765 77	0.485 89
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 76	0.634 83	0.743 55	0.697 65	0.601 58	0.781 70	0.437 65	0.585 84	0.493 74	0.446 79	0.933 80	0.394 79	0.011 103	0.654 32	0.661 68	0.603 71	0.733 54	0.526 78	0.832 75	0.761 79	0.480 90

dtc_net	0.596 77	0.683 75	0.725 62	0.715 59	0.549 75	0.803 59	0.444 60	0.647 75	0.493 74	0.495 68	0.941 66	0.409 75	0.000 105	0.424 87	0.544 75	0.598 74	0.703 67	0.522 79	0.912 28	0.792 64	0.520 84

LAP-D	0.594 78	0.720 68	0.692 76	0.637 85	0.456 88	0.773 75	0.391 80	0.730 57	0.587 47	0.445 81	0.940 69	0.381 82	0.288 29	0.434 85	0.453 86	0.591 77	0.649 82	0.581 55	0.777 83	0.749 83	0.610 53

DPC	0.592 79	0.720 68	0.700 70	0.602 89	0.480 84	0.762 80	0.380 83	0.713 63	0.585 50	0.437 82	0.940 69	0.369 84	0.288 29	0.434 85	0.509 82	0.590 79	0.639 87	0.567 60	0.772 84	0.755 81	0.592 63
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 80	0.766 50	0.659 86	0.683 68	0.470 87	0.740 85	0.387 81	0.620 80	0.490 76	0.476 73	0.922 87	0.355 87	0.245 55	0.511 72	0.511 81	0.571 82	0.643 85	0.493 86	0.872 60	0.762 78	0.600 59

ROSMRF	0.580 81	0.772 45	0.707 66	0.681 69	0.563 71	0.764 78	0.362 85	0.515 92	0.465 84	0.465 76	0.936 74	0.427 69	0.207 67	0.438 83	0.577 73	0.536 85	0.675 77	0.486 87	0.723 90	0.779 71	0.524 83

SD-DETR	0.576 82	0.746 56	0.609 95	0.445 100	0.517 80	0.643 96	0.366 84	0.714 62	0.456 86	0.468 75	0.870 98	0.432 63	0.264 47	0.558 61	0.674 63	0.586 80	0.688 72	0.482 88	0.739 88	0.733 86	0.537 80

SQN_0.1%	0.569 83	0.676 77	0.696 73	0.657 77	0.497 81	0.779 73	0.424 67	0.548 88	0.515 68	0.376 89	0.902 96	0.422 71	0.357 4	0.379 90	0.456 85	0.596 76	0.659 81	0.544 71	0.685 93	0.665 97	0.556 76

TextureNet	0.566 84	0.672 79	0.664 84	0.671 73	0.494 82	0.719 86	0.445 57	0.678 70	0.411 94	0.396 87	0.935 75	0.356 86	0.225 60	0.412 88	0.535 77	0.565 83	0.636 88	0.464 90	0.794 82	0.680 94	0.568 70
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 85	0.648 81	0.700 70	0.770 45	0.586 64	0.687 90	0.333 89	0.650 73	0.514 69	0.475 74	0.906 93	0.359 85	0.223 62	0.340 92	0.442 87	0.422 96	0.668 79	0.501 83	0.708 91	0.779 71	0.534 81

Pointnet++ & Feature	0.557 86	0.735 61	0.661 85	0.686 67	0.491 83	0.744 84	0.392 78	0.539 89	0.451 87	0.375 90	0.946 52	0.376 83	0.205 69	0.403 89	0.356 92	0.553 84	0.643 85	0.497 84	0.824 78	0.756 80	0.515 85

GMLPs	0.538 87	0.495 97	0.693 75	0.647 81	0.471 86	0.793 65	0.300 92	0.477 93	0.505 71	0.358 91	0.903 95	0.327 90	0.081 98	0.472 81	0.529 79	0.448 94	0.710 60	0.509 80	0.746 86	0.737 85	0.554 77

PanopticFusion-label	0.529 88	0.491 98	0.688 79	0.604 88	0.386 93	0.632 97	0.225 102	0.705 66	0.434 91	0.293 97	0.815 100	0.348 88	0.241 56	0.499 76	0.669 64	0.507 87	0.649 82	0.442 96	0.796 81	0.602 100	0.561 73
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 89	0.676 77	0.591 98	0.609 86	0.442 89	0.774 74	0.335 88	0.597 81	0.422 93	0.357 92	0.932 81	0.341 89	0.094 97	0.298 94	0.528 80	0.473 92	0.676 76	0.495 85	0.602 99	0.721 89	0.349 100

Online SegFusion	0.515 90	0.607 88	0.644 89	0.579 91	0.434 90	0.630 98	0.353 86	0.628 79	0.440 89	0.410 85	0.762 103	0.307 92	0.167 85	0.520 70	0.403 90	0.516 86	0.565 92	0.447 94	0.678 94	0.701 91	0.514 86
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 91	0.558 93	0.608 96	0.424 102	0.478 85	0.690 89	0.246 98	0.586 83	0.468 82	0.450 78	0.911 91	0.394 79	0.160 88	0.438 83	0.212 99	0.432 95	0.541 97	0.475 89	0.742 87	0.727 87	0.477 91

PCNN	0.498 92	0.559 92	0.644 89	0.560 93	0.420 92	0.711 88	0.229 100	0.414 94	0.436 90	0.352 93	0.941 66	0.324 91	0.155 89	0.238 99	0.387 91	0.493 88	0.529 98	0.509 80	0.813 80	0.751 82	0.504 87

3DMV	0.484 93	0.484 99	0.538 100	0.643 83	0.424 91	0.606 101	0.310 90	0.574 85	0.433 92	0.378 88	0.796 101	0.301 93	0.214 66	0.537 68	0.208 100	0.472 93	0.507 101	0.413 99	0.693 92	0.602 100	0.539 79
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 94	0.577 91	0.611 94	0.356 104	0.321 101	0.715 87	0.299 94	0.376 98	0.328 101	0.319 95	0.944 61	0.285 95	0.164 86	0.216 102	0.229 97	0.484 90	0.545 96	0.456 92	0.755 85	0.709 90	0.475 92
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 95	0.679 76	0.604 97	0.578 92	0.380 94	0.682 91	0.291 95	0.106 104	0.483 79	0.258 102	0.920 88	0.258 99	0.025 102	0.231 101	0.325 93	0.480 91	0.560 94	0.463 91	0.725 89	0.666 96	0.231 104
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 96	0.474 100	0.623 92	0.463 98	0.366 96	0.651 94	0.310 90	0.389 97	0.349 99	0.330 94	0.937 72	0.271 97	0.126 94	0.285 95	0.224 98	0.350 101	0.577 91	0.445 95	0.625 97	0.723 88	0.394 96
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 97	0.548 94	0.548 99	0.597 90	0.363 97	0.628 99	0.300 92	0.292 99	0.374 96	0.307 96	0.881 97	0.268 98	0.186 78	0.238 99	0.204 101	0.407 97	0.506 102	0.449 93	0.667 95	0.620 99	0.462 94

SurfaceConvPF	0.442 97	0.505 96	0.622 93	0.380 103	0.342 99	0.654 93	0.227 101	0.397 96	0.367 97	0.276 99	0.924 85	0.240 100	0.198 74	0.359 91	0.262 95	0.366 98	0.581 90	0.435 97	0.640 96	0.668 95	0.398 95
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 99	0.437 102	0.646 88	0.474 97	0.369 95	0.645 95	0.353 86	0.258 101	0.282 103	0.279 98	0.918 90	0.298 94	0.147 93	0.283 96	0.294 94	0.487 89	0.562 93	0.427 98	0.619 98	0.633 98	0.352 99
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 100	0.525 95	0.647 87	0.522 94	0.324 100	0.488 104	0.077 105	0.712 64	0.353 98	0.401 86	0.636 105	0.281 96	0.176 81	0.340 92	0.565 74	0.175 105	0.551 95	0.398 100	0.370 105	0.602 100	0.361 98

SPLAT Net	0.393 101	0.472 101	0.511 101	0.606 87	0.311 102	0.656 92	0.245 99	0.405 95	0.328 101	0.197 103	0.927 84	0.227 102	0.000 105	0.001 106	0.249 96	0.271 104	0.510 99	0.383 102	0.593 100	0.699 92	0.267 102
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	0.383 102	0.297 104	0.491 102	0.432 101	0.358 98	0.612 100	0.274 96	0.116 103	0.411 94	0.265 100	0.904 94	0.229 101	0.079 99	0.250 97	0.185 102	0.320 102	0.510 99	0.385 101	0.548 101	0.597 103	0.394 96

PointNet++	0.339 103	0.584 90	0.478 103	0.458 99	0.256 104	0.360 105	0.250 97	0.247 102	0.278 104	0.261 101	0.677 104	0.183 103	0.117 95	0.212 103	0.145 104	0.364 99	0.346 105	0.232 105	0.548 101	0.523 104	0.252 103
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNet	0.308 104	0.353 103	0.290 105	0.278 105	0.166 105	0.553 102	0.169 104	0.286 100	0.147 105	0.148 105	0.908 92	0.182 104	0.064 100	0.023 105	0.018 106	0.354 100	0.363 103	0.345 103	0.546 103	0.685 93	0.278 101

ScanNet	0.306 105	0.203 105	0.366 104	0.501 95	0.311 102	0.524 103	0.211 103	0.002 106	0.342 100	0.189 104	0.786 102	0.145 105	0.102 96	0.245 98	0.152 103	0.318 103	0.348 104	0.300 104	0.460 104	0.437 105	0.182 105
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 106	0.000 106	0.041 106	0.172 106	0.030 106	0.062 106	0.001 106	0.035 105	0.004 106	0.051 106	0.143 106	0.019 106	0.003 104	0.041 104	0.050 105	0.003 106	0.054 106	0.018 106	0.005 106	0.264 106	0.082 106

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

Method	avg ap 25%	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

TD3D	0.875 1	1.000 1	0.976 13	0.877 9	0.783 15	0.970 1	0.889 1	0.828 12	0.945 3	0.803 7	0.713 11	0.720 11	0.709 9	1.000 1	0.936 6	0.934 3	0.873 7	1.000 1	0.791 6

Queryformer	0.874 2	1.000 1	0.978 11	0.809 25	0.876 1	0.936 6	0.702 9	0.716 26	0.920 5	0.875 4	0.766 4	0.772 3	0.818 4	1.000 1	0.995 1	0.916 4	0.892 1	1.000 1	0.767 9

SoftGroup++	0.874 2	1.000 1	0.972 14	0.947 1	0.839 5	0.898 13	0.556 25	0.913 2	0.881 11	0.756 9	0.828 2	0.748 7	0.821 2	1.000 1	0.937 5	0.937 1	0.887 2	1.000 1	0.821 3

Mask3D	0.870 4	1.000 1	0.985 7	0.782 33	0.818 9	0.938 5	0.760 5	0.749 23	0.923 4	0.877 3	0.760 5	0.785 2	0.820 3	1.000 1	0.912 9	0.864 23	0.878 5	0.983 40	0.825 2
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SoftGroup	0.865 5	1.000 1	0.969 15	0.860 12	0.860 2	0.913 9	0.558 22	0.899 3	0.911 6	0.760 8	0.828 1	0.736 8	0.802 6	0.981 30	0.919 8	0.875 14	0.877 6	1.000 1	0.820 4
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
MAFT	0.860 6	1.000 1	0.990 6	0.810 24	0.829 6	0.949 3	0.809 4	0.688 32	0.836 16	0.904 1	0.751 7	0.796 1	0.741 7	1.000 1	0.864 24	0.848 30	0.837 13	1.000 1	0.828 1

IPCA-Inst	0.851 7	1.000 1	0.968 16	0.884 8	0.842 4	0.862 25	0.693 11	0.812 17	0.888 9	0.677 21	0.783 3	0.698 12	0.807 5	1.000 1	0.911 13	0.865 22	0.865 9	1.000 1	0.757 11

SPFormer	0.851 7	1.000 1	0.994 2	0.806 26	0.774 17	0.942 4	0.637 14	0.849 10	0.859 14	0.889 2	0.720 9	0.730 9	0.665 14	1.000 1	0.911 13	0.868 21	0.873 8	1.000 1	0.796 5
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
ISBNet	0.845 9	1.000 1	0.976 12	0.798 27	0.794 12	0.916 7	0.757 6	0.667 34	0.882 10	0.842 5	0.715 10	0.757 5	0.832 1	1.000 1	0.905 16	0.803 45	0.843 12	1.000 1	0.715 18
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
SphereSeg	0.835 10	1.000 1	0.963 19	0.891 6	0.794 11	0.954 2	0.822 3	0.710 27	0.961 2	0.721 13	0.693 17	0.530 32	0.653 16	1.000 1	0.867 23	0.857 26	0.859 10	0.991 37	0.771 8

TopoSeg	0.832 11	1.000 1	0.981 9	0.933 2	0.819 8	0.826 33	0.524 31	0.841 11	0.811 20	0.681 20	0.759 6	0.687 13	0.727 8	0.981 30	0.911 13	0.883 10	0.853 11	1.000 1	0.756 12

GraphCut	0.832 11	1.000 1	0.922 33	0.724 42	0.798 10	0.902 12	0.701 10	0.856 8	0.859 13	0.715 14	0.706 12	0.748 6	0.640 27	1.000 1	0.934 7	0.862 24	0.880 3	1.000 1	0.729 14

PBNet	0.825 13	1.000 1	0.963 18	0.837 16	0.843 3	0.865 20	0.822 2	0.647 36	0.878 12	0.733 11	0.639 25	0.683 14	0.650 17	1.000 1	0.853 25	0.870 18	0.820 14	1.000 1	0.744 13
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
SSEC	0.820 14	1.000 1	0.983 8	0.924 3	0.826 7	0.817 36	0.415 40	0.899 4	0.793 24	0.673 22	0.731 8	0.636 19	0.653 15	1.000 1	0.939 4	0.804 43	0.878 4	1.000 1	0.780 7

DKNet	0.815 15	1.000 1	0.930 25	0.844 14	0.765 21	0.915 8	0.534 29	0.805 19	0.805 22	0.807 6	0.654 19	0.763 4	0.650 17	1.000 1	0.794 37	0.881 11	0.766 18	1.000 1	0.758 10
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN	0.806 16	1.000 1	0.992 4	0.789 29	0.723 33	0.891 14	0.650 13	0.810 18	0.832 17	0.665 24	0.699 15	0.658 15	0.700 10	1.000 1	0.881 18	0.832 34	0.774 16	0.997 30	0.613 35
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
HAIS	0.803 17	1.000 1	0.994 2	0.820 20	0.759 22	0.855 26	0.554 26	0.882 5	0.827 19	0.615 30	0.676 18	0.638 18	0.646 25	1.000 1	0.912 9	0.797 47	0.767 17	0.994 35	0.726 15
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Box2Mask	0.803 17	1.000 1	0.962 20	0.874 10	0.707 37	0.887 17	0.686 12	0.598 40	0.961 1	0.715 15	0.694 16	0.469 37	0.700 10	1.000 1	0.912 9	0.902 5	0.753 23	0.997 30	0.637 29
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
Mask-Group	0.792 19	1.000 1	0.968 17	0.812 21	0.766 20	0.864 21	0.460 34	0.815 16	0.888 8	0.598 34	0.651 22	0.639 17	0.600 32	0.918 35	0.941 2	0.896 6	0.721 30	1.000 1	0.723 16
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained	0.791 20	1.000 1	0.996 1	0.829 19	0.767 19	0.889 16	0.600 17	0.819 15	0.770 29	0.594 35	0.620 28	0.541 29	0.700 10	1.000 1	0.941 2	0.889 8	0.763 19	1.000 1	0.526 44

SSTNet	0.789 21	1.000 1	0.840 47	0.888 7	0.717 34	0.835 29	0.717 8	0.684 33	0.627 43	0.724 12	0.652 21	0.727 10	0.600 32	1.000 1	0.912 9	0.822 37	0.757 22	1.000 1	0.691 23
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN	0.788 22	1.000 1	0.978 10	0.867 11	0.781 16	0.833 30	0.527 30	0.824 13	0.806 21	0.549 43	0.596 31	0.551 25	0.700 10	1.000 1	0.853 25	0.935 2	0.733 27	1.000 1	0.651 26

DENet	0.786 23	1.000 1	0.929 26	0.736 40	0.750 28	0.720 49	0.755 7	0.934 1	0.794 23	0.590 36	0.561 37	0.537 30	0.650 17	1.000 1	0.882 17	0.804 44	0.789 15	1.000 1	0.719 17

DualGroup	0.782 24	1.000 1	0.927 27	0.811 22	0.772 18	0.853 27	0.631 16	0.805 19	0.773 26	0.613 31	0.611 29	0.610 21	0.650 17	0.835 46	0.881 18	0.879 13	0.750 25	1.000 1	0.675 24

PointGroup	0.778 25	1.000 1	0.900 37	0.798 28	0.715 35	0.863 22	0.493 32	0.706 28	0.895 7	0.569 41	0.701 13	0.576 23	0.639 28	1.000 1	0.880 20	0.851 28	0.719 31	0.997 30	0.709 20
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]
PE	0.776 26	1.000 1	0.900 38	0.860 12	0.728 32	0.869 18	0.400 41	0.857 7	0.774 25	0.568 42	0.701 14	0.602 22	0.646 25	0.933 34	0.843 28	0.890 7	0.691 38	0.997 30	0.709 19
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
AOIA	0.767 27	1.000 1	0.937 22	0.810 23	0.740 30	0.906 10	0.550 27	0.800 21	0.706 35	0.577 40	0.624 27	0.544 28	0.596 37	0.857 38	0.879 22	0.880 12	0.750 24	0.992 36	0.658 25

DD-UNet+Group	0.764 28	1.000 1	0.897 40	0.837 15	0.753 25	0.830 32	0.459 36	0.824 13	0.699 37	0.629 28	0.653 20	0.438 40	0.650 17	1.000 1	0.880 20	0.858 25	0.690 39	1.000 1	0.650 27
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
INS-Conv-instance	0.762 29	1.000 1	0.923 30	0.765 36	0.785 14	0.905 11	0.600 17	0.655 35	0.646 42	0.683 19	0.647 23	0.530 31	0.650 17	1.000 1	0.824 30	0.830 35	0.693 37	0.944 44	0.644 28

Dyco3D	0.761 30	1.000 1	0.935 23	0.893 5	0.752 27	0.863 23	0.600 17	0.588 41	0.742 32	0.641 26	0.633 26	0.546 27	0.550 39	0.857 38	0.789 39	0.853 27	0.762 20	0.987 38	0.699 21
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance	0.742 31	1.000 1	0.923 30	0.785 30	0.745 29	0.867 19	0.557 23	0.578 44	0.729 33	0.670 23	0.644 24	0.488 35	0.577 38	1.000 1	0.794 37	0.830 35	0.620 47	1.000 1	0.550 40
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg	0.739 32	1.000 1	0.899 39	0.759 38	0.753 26	0.823 34	0.282 45	0.691 31	0.658 40	0.582 39	0.594 32	0.547 26	0.628 30	1.000 1	0.795 36	0.868 20	0.728 29	1.000 1	0.692 22

3D-MPA	0.737 33	1.000 1	0.933 24	0.785 30	0.794 13	0.831 31	0.279 47	0.588 41	0.695 38	0.616 29	0.559 38	0.556 24	0.650 17	1.000 1	0.809 34	0.875 15	0.696 35	1.000 1	0.608 37
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML	0.731 34	1.000 1	0.992 4	0.779 35	0.609 46	0.746 44	0.308 44	0.867 6	0.601 46	0.607 32	0.539 41	0.519 33	0.550 39	1.000 1	0.824 30	0.869 19	0.729 28	1.000 1	0.616 33
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
OSIS	0.725 35	1.000 1	0.885 43	0.653 48	0.657 43	0.801 37	0.576 21	0.695 30	0.828 18	0.698 17	0.534 42	0.457 39	0.500 46	0.857 38	0.831 29	0.841 32	0.627 45	1.000 1	0.619 32

SSEN	0.724 36	1.000 1	0.926 28	0.781 34	0.661 41	0.845 28	0.596 20	0.529 47	0.764 31	0.653 25	0.489 48	0.461 38	0.500 46	0.859 37	0.765 40	0.872 17	0.761 21	1.000 1	0.577 38
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
NeuralBF	0.718 37	1.000 1	0.945 21	0.901 4	0.754 24	0.817 35	0.460 34	0.700 29	0.772 27	0.688 18	0.568 36	0.000 58	0.500 46	0.981 30	0.606 49	0.872 16	0.740 26	1.000 1	0.614 34
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
Sparse R-CNN	0.714 38	1.000 1	0.926 29	0.694 43	0.699 39	0.890 15	0.636 15	0.516 48	0.693 39	0.743 10	0.588 33	0.369 44	0.601 31	0.594 51	0.800 35	0.886 9	0.676 40	0.986 39	0.546 41

SALoss-ResNet	0.695 39	1.000 1	0.855 45	0.579 53	0.589 48	0.735 47	0.484 33	0.588 41	0.856 15	0.634 27	0.571 35	0.298 45	0.500 46	1.000 1	0.824 30	0.818 38	0.702 34	0.935 49	0.545 42
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)
PanopticFusion-inst	0.693 40	1.000 1	0.852 46	0.655 47	0.616 45	0.788 39	0.334 43	0.763 22	0.771 28	0.457 53	0.555 39	0.652 16	0.518 43	0.857 38	0.765 40	0.732 53	0.631 43	0.944 44	0.577 39
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Occipital-SCS	0.688 41	1.000 1	0.913 34	0.730 41	0.737 31	0.743 46	0.442 37	0.855 9	0.655 41	0.546 44	0.546 40	0.263 47	0.508 45	0.889 36	0.568 50	0.771 50	0.705 33	0.889 52	0.625 31

3D-BoNet	0.687 42	1.000 1	0.887 42	0.836 17	0.587 49	0.643 56	0.550 27	0.620 37	0.724 34	0.522 48	0.501 46	0.243 48	0.512 44	1.000 1	0.751 42	0.807 42	0.661 42	0.909 51	0.612 36
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
ClickSeg_Instance	0.685 43	1.000 1	0.818 49	0.600 51	0.715 36	0.795 38	0.557 23	0.533 46	0.591 48	0.601 33	0.519 44	0.429 42	0.638 29	0.938 33	0.706 44	0.817 40	0.624 46	0.944 44	0.502 46

PCJC	0.684 44	1.000 1	0.895 41	0.757 39	0.659 42	0.862 24	0.189 54	0.739 24	0.606 45	0.712 16	0.581 34	0.515 34	0.650 17	0.857 38	0.357 55	0.785 48	0.631 44	0.889 52	0.635 30

SPG_WSIS	0.678 45	1.000 1	0.880 44	0.836 17	0.701 38	0.727 48	0.273 49	0.607 39	0.706 36	0.541 46	0.515 45	0.174 50	0.600 32	0.857 38	0.716 43	0.846 31	0.711 32	1.000 1	0.506 45

One_Thing_One_Click	0.675 46	1.000 1	0.823 48	0.782 32	0.621 44	0.766 41	0.211 51	0.736 25	0.560 50	0.586 37	0.522 43	0.636 20	0.453 50	0.641 50	0.853 25	0.850 29	0.694 36	0.997 30	0.411 50
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SegGroup_ins	0.637 47	1.000 1	0.923 32	0.593 52	0.561 50	0.746 45	0.143 56	0.504 49	0.766 30	0.485 51	0.442 49	0.372 43	0.530 42	0.714 47	0.815 33	0.775 49	0.673 41	1.000 1	0.431 49
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MASC	0.615 48	0.711 54	0.802 50	0.540 54	0.757 23	0.777 40	0.029 57	0.577 45	0.588 49	0.521 49	0.600 30	0.436 41	0.534 41	0.697 48	0.616 48	0.838 33	0.526 49	0.980 41	0.534 43
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone	0.605 49	1.000 1	0.909 35	0.764 37	0.603 47	0.704 50	0.415 39	0.301 54	0.548 51	0.461 52	0.394 50	0.267 46	0.386 52	0.857 38	0.649 47	0.817 39	0.504 50	0.959 42	0.356 53

3D-SIS	0.558 50	1.000 1	0.773 51	0.614 50	0.503 52	0.691 52	0.200 52	0.412 50	0.498 54	0.546 45	0.311 55	0.103 54	0.600 32	0.857 38	0.382 52	0.799 46	0.445 56	0.938 48	0.371 51
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet	0.544 51	0.500 57	0.655 57	0.661 46	0.663 40	0.765 42	0.432 38	0.214 56	0.612 44	0.584 38	0.499 47	0.204 49	0.286 56	0.429 54	0.655 46	0.650 58	0.539 48	0.950 43	0.499 47

Hier3D	0.540 52	1.000 1	0.727 52	0.626 49	0.467 55	0.693 51	0.200 52	0.412 50	0.480 55	0.528 47	0.318 54	0.077 57	0.600 32	0.688 49	0.382 52	0.768 51	0.472 52	0.941 47	0.350 54
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class	0.497 53	0.250 59	0.902 36	0.689 44	0.540 51	0.747 43	0.276 48	0.610 38	0.268 58	0.489 50	0.348 51	0.000 58	0.243 58	0.220 57	0.663 45	0.814 41	0.459 54	0.928 50	0.496 48

tmp	0.474 54	1.000 1	0.727 52	0.433 57	0.481 54	0.673 54	0.022 59	0.380 52	0.517 53	0.436 55	0.338 53	0.128 52	0.343 54	0.429 54	0.291 57	0.728 54	0.473 51	0.833 55	0.300 56

SemRegionNet-20cls	0.470 55	1.000 1	0.727 52	0.447 56	0.481 53	0.678 53	0.024 58	0.380 52	0.518 52	0.440 54	0.339 52	0.128 52	0.350 53	0.429 54	0.212 58	0.711 55	0.465 53	0.833 55	0.290 57

ASIS	0.422 56	0.333 58	0.707 55	0.676 45	0.401 56	0.650 55	0.350 42	0.177 57	0.594 47	0.376 56	0.202 56	0.077 56	0.404 51	0.571 52	0.197 59	0.674 57	0.447 55	0.500 58	0.260 58

3D-BEVIS	0.401 57	0.667 55	0.687 56	0.419 58	0.137 59	0.587 57	0.188 55	0.235 55	0.359 57	0.211 58	0.093 59	0.080 55	0.311 55	0.571 52	0.382 52	0.754 52	0.300 58	0.874 54	0.357 52
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet	0.390 58	0.556 56	0.636 58	0.493 55	0.353 57	0.539 58	0.271 50	0.160 58	0.450 56	0.359 57	0.178 57	0.146 51	0.250 57	0.143 58	0.347 56	0.698 56	0.436 57	0.667 57	0.331 55

MaskRCNN 2d->3d Proj	0.261 59	0.903 53	0.081 59	0.008 59	0.233 58	0.175 59	0.280 46	0.106 59	0.150 59	0.203 59	0.175 58	0.480 36	0.218 59	0.143 58	0.542 51	0.404 59	0.153 59	0.393 59	0.049 59

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 1	0.512 1	0.422 15	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 2	0.481 2	0.451 11	0.769 3	0.656 3	0.567 3	0.931 3	0.395 4	0.390 4	0.700 3	0.534 3	0.689 9	0.770 2	0.574 3	0.865 6	0.831 3	0.675 4
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
CU-Hybrid-2D Net	0.636 3	0.825 2	0.820 2	0.179 20	0.648 3	0.463 3	0.549 2	0.742 6	0.676 2	0.628 2	0.961 1	0.420 2	0.379 5	0.684 6	0.381 15	0.732 2	0.723 3	0.599 2	0.827 13	0.851 2	0.634 6

CMX	0.613 4	0.681 7	0.725 9	0.502 12	0.634 5	0.297 15	0.478 9	0.830 2	0.651 4	0.537 6	0.924 4	0.375 5	0.315 12	0.686 5	0.451 12	0.714 4	0.543 18	0.504 5	0.894 4	0.823 4	0.688 3

DMMF_3d	0.605 5	0.651 8	0.744 7	0.782 3	0.637 4	0.387 4	0.536 3	0.732 7	0.590 6	0.540 5	0.856 18	0.359 9	0.306 13	0.596 11	0.539 2	0.627 18	0.706 4	0.497 7	0.785 18	0.757 16	0.476 19

MCA-Net	0.595 6	0.533 17	0.756 6	0.746 4	0.590 8	0.334 7	0.506 6	0.670 12	0.587 7	0.500 10	0.905 8	0.366 8	0.352 8	0.601 10	0.506 6	0.669 15	0.648 7	0.501 6	0.839 12	0.769 12	0.516 18

RFBNet	0.592 7	0.616 9	0.758 5	0.659 5	0.581 9	0.330 8	0.469 10	0.655 15	0.543 12	0.524 7	0.924 4	0.355 10	0.336 10	0.572 14	0.479 8	0.671 13	0.648 7	0.480 9	0.814 16	0.814 5	0.614 9

FAN_NV_RVC	0.586 8	0.510 18	0.764 4	0.079 23	0.620 7	0.330 8	0.494 7	0.753 4	0.573 8	0.556 4	0.884 13	0.405 3	0.303 14	0.718 2	0.452 11	0.672 12	0.658 5	0.509 4	0.898 3	0.813 6	0.727 2

DCRedNet	0.583 9	0.682 6	0.723 10	0.542 11	0.510 17	0.310 12	0.451 11	0.668 13	0.549 11	0.520 8	0.920 6	0.375 5	0.446 2	0.528 17	0.417 13	0.670 14	0.577 15	0.478 10	0.862 7	0.806 7	0.628 8

MIX6D_RVC	0.582 10	0.695 4	0.687 14	0.225 18	0.632 6	0.328 10	0.550 1	0.748 5	0.623 5	0.494 13	0.890 11	0.350 12	0.254 20	0.688 4	0.454 10	0.716 3	0.597 14	0.489 8	0.881 5	0.768 13	0.575 12

SSMA	0.577 11	0.695 4	0.716 12	0.439 14	0.563 11	0.314 11	0.444 13	0.719 8	0.551 10	0.503 9	0.887 12	0.346 13	0.348 9	0.603 9	0.353 17	0.709 5	0.600 12	0.457 12	0.901 2	0.786 8	0.599 11
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
UNIV_CNP_RVC_UE	0.566 12	0.569 16	0.686 16	0.435 15	0.524 14	0.294 16	0.421 16	0.712 9	0.543 12	0.463 15	0.872 14	0.320 14	0.363 7	0.611 8	0.477 9	0.686 10	0.627 9	0.443 15	0.862 7	0.775 11	0.639 5

EMSAFormer	0.564 13	0.581 13	0.736 8	0.564 10	0.546 13	0.219 20	0.517 4	0.675 11	0.486 17	0.427 19	0.904 9	0.352 11	0.320 11	0.589 12	0.528 4	0.708 6	0.464 21	0.413 19	0.847 11	0.786 8	0.611 10

SN_RN152pyrx8_RVC	0.546 14	0.572 14	0.663 18	0.638 7	0.518 15	0.298 14	0.366 21	0.633 18	0.510 15	0.446 17	0.864 16	0.296 17	0.267 17	0.542 16	0.346 18	0.704 7	0.575 16	0.431 16	0.853 10	0.766 14	0.630 7

UDSSEG_RVC	0.545 15	0.610 11	0.661 19	0.588 8	0.556 12	0.268 18	0.482 8	0.642 17	0.572 9	0.475 14	0.836 20	0.312 15	0.367 6	0.630 7	0.189 20	0.639 17	0.495 20	0.452 13	0.826 14	0.756 17	0.541 14

segfomer with 6d	0.542 16	0.594 12	0.687 14	0.146 21	0.579 10	0.308 13	0.515 5	0.703 10	0.472 18	0.498 11	0.868 15	0.369 7	0.282 15	0.589 12	0.390 14	0.701 8	0.556 17	0.416 18	0.860 9	0.759 15	0.539 16

FuseNet	0.535 17	0.570 15	0.681 17	0.182 19	0.512 16	0.290 17	0.431 14	0.659 14	0.504 16	0.495 12	0.903 10	0.308 16	0.428 3	0.523 18	0.365 16	0.676 11	0.621 11	0.470 11	0.762 19	0.779 10	0.541 14
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++	0.503 18	0.613 10	0.722 11	0.418 16	0.358 23	0.337 6	0.370 20	0.479 21	0.443 19	0.368 21	0.907 7	0.207 20	0.213 22	0.464 21	0.525 5	0.618 19	0.657 6	0.450 14	0.788 17	0.721 20	0.408 22
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 19	0.481 21	0.612 20	0.579 9	0.456 19	0.343 5	0.384 18	0.623 19	0.525 14	0.381 20	0.845 19	0.254 19	0.264 19	0.557 15	0.182 21	0.581 21	0.598 13	0.429 17	0.760 20	0.661 22	0.446 21
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVC	0.485 20	0.505 19	0.709 13	0.092 22	0.427 20	0.241 19	0.411 17	0.654 16	0.385 23	0.457 16	0.861 17	0.053 23	0.279 16	0.503 19	0.481 7	0.645 16	0.626 10	0.365 21	0.748 21	0.725 19	0.529 17
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 21	0.490 20	0.581 21	0.289 17	0.507 18	0.067 23	0.379 19	0.610 20	0.417 21	0.435 18	0.822 22	0.278 18	0.267 17	0.503 19	0.228 19	0.616 20	0.533 19	0.375 20	0.820 15	0.729 18	0.560 13

Enet (reimpl)	0.376 22	0.264 23	0.452 23	0.452 13	0.365 21	0.181 21	0.143 23	0.456 22	0.409 22	0.346 22	0.769 23	0.164 21	0.218 21	0.359 22	0.123 23	0.403 23	0.381 23	0.313 23	0.571 22	0.685 21	0.472 20
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 23	0.293 22	0.521 22	0.657 6	0.361 22	0.161 22	0.250 22	0.004 23	0.440 20	0.183 23	0.836 20	0.125 22	0.060 23	0.319 23	0.132 22	0.417 22	0.412 22	0.344 22	0.541 23	0.427 23	0.109 23
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
DMMF	0.003 24	0.000 24	0.005 24	0.000 24	0.000 24	0.037 24	0.001 24	0.000 24	0.001 24	0.005 24	0.003 24	0.000 24	0.000 24	0.000 24	0.000 24	0.000 24	0.002 24	0.001 24	0.000 24	0.006 24	0.000 24

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

UniDet_RVC	0.205 1	0.381 1	0.323 1	0.037 1	0.226 1	0.177 1	0.063 1	0.277 1	0.120 1	0.067 1	0.131 1	0.074 2	0.317 1	0.080 1	0.235 1	0.289 1	0.141 1	0.678 1	0.080 1

MaskRCNN_ScanNet	0.119 2	0.129 2	0.212 2	0.002 2	0.112 2	0.148 2	0.014 2	0.205 2	0.044 2	0.066 2	0.078 2	0.095 1	0.142 2	0.030 2	0.128 2	0.139 2	0.080 2	0.459 2	0.057 2
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 recall	apartment	bathroom	bedroom / hotel	bookstore / library	conference room	copy/mail room	hallway	kitchen	laundry room	living room / lounge	misc	office	storage / basement / garage

multi-task	0.700 1	0.500 1	1.000 1	0.882 2	0.500 2	1.000 1	1.000 1	0.500 2	1.000 1	1.000 1	0.778 1	0.000 2	0.938 1	0.000 2
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE	0.691 2	0.500 1	0.938 2	0.824 3	1.000 1	1.000 1	0.500 2	1.000 1	0.857 2	0.500 2	0.556 3	0.000 2	0.812 2	0.500 1

SE-ResNeXt-SSMA	0.498 3	0.000 4	0.812 3	0.941 1	0.500 2	0.500 3	0.500 2	0.500 2	0.429 4	0.500 2	0.667 2	0.500 1	0.625 3	0.000 2
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet	0.353 4	0.250 3	0.812 3	0.529 4	0.500 2	0.500 3	0.000 4	0.500 2	0.571 3	0.000 4	0.556 3	0.000 2	0.375 4	0.000 2