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

OctFormer ScanNet200	0.326 4	0.539 4	0.265 3	0.131 3	0.499 2	0.110 1	0.522 1	0.000 1	0.000 1	0.000 1	0.318 8	0.427 4	0.455 6	0.743 6	0.765 4	0.175 3	0.842 1	0.000 3	0.828 1	0.204 1	0.033 2	0.429 4	0.335 1	0.601 1	0.312 2	0.000 4	0.357 4	0.000 1	0.000 3	0.047 5	0.423 4	0.000 3	0.000 1	0.105 3	0.000 2	0.873 3	0.079 6	0.670 4	0.000 3	0.117 1	0.471 5	0.432 2	0.829 5	0.000 1	0.000 1	0.584 2	0.417 8	0.089 3	0.684 5	0.837 4	0.705 7	0.021 6	0.178 3	0.892 1	0.000 1	0.028 2	0.505 5	0.000 5	0.457 4	0.200 6	0.662 1	0.412 3	0.244 6	0.496 3	0.000 8	0.451 2	0.626 3	0.000 2	0.102 4	0.943 3	0.138 5	0.000 1	0.000 4	0.149 2	0.291 2	0.534 3	0.722 2	0.632 3	0.331 4	0.253 7	0.453 3	0.487 4	0.000 2	0.000 1	0.479 2	0.000 5	0.022 6	0.000 3	0.000 3	0.900 4	0.128 2	0.684 1	0.164 4	0.413 1	0.854 5	0.000 3	0.000 1	0.512 8	0.074 8	0.003 6	0.000 4	0.000 1	0.000 3	0.469 6	0.613 4	0.132 4	0.529 3	0.871 1	0.227 7	0.582 2	0.026 8	0.787 3	0.000 3	0.339 6	0.000 1	0.000 2	0.000 2	0.626 3	0.000 1	0.029 3	0.000 1	0.587 4	0.612 3	0.411 2	0.724 5	0.000 3	0.000 1	0.407 3	0.552 1	0.513 1	0.849 2	0.655 2	0.408 1	0.000 4	0.296 1	0.000 1	0.686 6	0.645 5	0.145 3	0.022 4	0.414 5	0.633 4	0.000 1	0.637 1	0.224 1	0.000 4	0.000 1	0.650 3	0.000 2	0.000 1	0.622 2	0.535 4	0.343 3	0.483 2	0.230 4	0.943 3	0.289 3	0.618 2	0.596 1	0.140 6	0.679 2	0.000 1	0.022 1	0.783 4	0.620 6	0.906 1	0.000 1	0.806 3	0.137 6	0.865 2	0.000 1	0.378 4	0.000 7	0.168 8	0.680 3	0.227 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-F.T.	0.332 3	0.556 2	0.270 1	0.123 5	0.519 1	0.091 2	0.349 2	0.000 1	0.000 1	0.000 1	0.339 6	0.383 5	0.498 5	0.833 2	0.807 1	0.241 1	0.584 2	0.000 3	0.755 3	0.124 3	0.000 4	0.608 2	0.330 3	0.530 4	0.314 1	0.000 4	0.374 3	0.000 1	0.000 3	0.197 1	0.459 3	0.000 3	0.000 1	0.117 1	0.000 2	0.876 1	0.095 1	0.682 2	0.000 3	0.086 3	0.518 2	0.433 1	0.930 1	0.000 1	0.000 1	0.563 3	0.542 5	0.077 4	0.715 2	0.858 3	0.756 1	0.008 8	0.171 4	0.874 2	0.000 1	0.039 1	0.550 3	0.000 5	0.545 2	0.256 3	0.657 3	0.453 1	0.351 3	0.449 5	0.213 2	0.392 3	0.611 4	0.000 2	0.037 6	0.946 1	0.138 5	0.000 1	0.000 4	0.063 3	0.308 1	0.537 2	0.796 1	0.673 1	0.323 5	0.392 3	0.400 5	0.509 3	0.000 2	0.000 1	0.649 1	0.000 5	0.023 5	0.000 3	0.000 3	0.914 3	0.002 7	0.506 7	0.163 5	0.359 3	0.872 3	0.000 3	0.000 1	0.623 2	0.112 3	0.001 7	0.000 4	0.000 1	0.021 2	0.753 1	0.565 7	0.150 1	0.579 1	0.806 4	0.267 2	0.616 1	0.042 7	0.783 4	0.000 3	0.374 5	0.000 1	0.000 2	0.000 2	0.620 4	0.000 1	0.000 4	0.000 1	0.572 6	0.634 1	0.350 4	0.792 2	0.000 3	0.000 1	0.376 4	0.535 2	0.378 2	0.855 1	0.672 1	0.074 5	0.000 4	0.185 3	0.000 1	0.727 3	0.660 3	0.076 8	0.000 6	0.432 4	0.646 3	0.000 1	0.594 5	0.006 7	0.000 4	0.000 1	0.658 2	0.000 2	0.000 1	0.661 1	0.549 3	0.300 5	0.291 5	0.045 5	0.942 4	0.304 2	0.600 3	0.572 2	0.135 7	0.695 1	0.000 1	0.008 3	0.793 3	0.942 1	0.899 2	0.000 1	0.816 2	0.181 4	0.897 1	0.000 1	0.679 1	0.223 3	0.264 1	0.691 2	0.345 6

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

CeCo	0.340 1	0.551 3	0.247 4	0.181 1	0.475 4	0.057 8	0.142 7	0.000 1	0.000 1	0.000 1	0.387 3	0.463 3	0.499 4	0.924 1	0.774 3	0.213 2	0.257 4	0.000 3	0.546 8	0.100 5	0.006 3	0.615 1	0.177 8	0.534 2	0.246 3	0.000 4	0.400 2	0.000 1	0.338 1	0.006 7	0.484 2	0.609 1	0.000 1	0.083 4	0.000 2	0.873 3	0.089 4	0.661 5	0.000 3	0.048 8	0.560 1	0.408 3	0.892 3	0.000 1	0.000 1	0.586 1	0.616 3	0.000 7	0.692 4	0.900 1	0.721 3	0.162 1	0.228 1	0.860 3	0.000 1	0.000 4	0.575 1	0.083 2	0.550 1	0.347 1	0.624 4	0.410 4	0.360 2	0.740 1	0.109 5	0.321 6	0.660 2	0.000 2	0.121 2	0.939 4	0.143 3	0.000 1	0.400 1	0.003 5	0.190 3	0.564 1	0.652 4	0.615 4	0.421 1	0.304 6	0.579 1	0.547 1	0.000 2	0.000 1	0.296 5	0.000 5	0.030 4	0.096 1	0.000 3	0.916 1	0.037 4	0.551 4	0.171 3	0.376 2	0.865 4	0.286 1	0.000 1	0.633 1	0.102 7	0.027 4	0.011 3	0.000 1	0.000 3	0.474 5	0.742 1	0.133 3	0.311 4	0.824 3	0.242 4	0.503 5	0.068 4	0.828 1	0.000 3	0.429 2	0.000 1	0.063 1	0.000 2	0.781 1	0.000 1	0.000 4	0.000 1	0.665 1	0.633 2	0.450 1	0.818 1	0.000 3	0.000 1	0.429 2	0.532 3	0.226 4	0.825 3	0.510 6	0.377 2	0.709 1	0.079 6	0.000 1	0.753 1	0.683 1	0.102 7	0.063 2	0.401 7	0.620 6	0.000 1	0.619 2	0.000 8	0.000 4	0.000 1	0.595 6	0.000 2	0.000 1	0.345 5	0.564 2	0.411 1	0.603 1	0.384 2	0.945 2	0.266 4	0.643 1	0.367 5	0.304 1	0.663 4	0.000 1	0.010 2	0.726 6	0.767 3	0.898 3	0.000 1	0.784 4	0.435 1	0.861 4	0.000 1	0.447 3	0.000 7	0.257 3	0.656 4	0.377 4
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
CSC-Pretrain	0.249 8	0.455 8	0.171 7	0.079 8	0.418 7	0.059 7	0.186 6	0.000 1	0.000 1	0.000 1	0.335 7	0.250 7	0.316 7	0.766 4	0.697 8	0.142 5	0.170 5	0.003 2	0.553 7	0.112 4	0.097 1	0.201 8	0.186 5	0.476 8	0.081 7	0.000 4	0.216 8	0.000 1	0.000 3	0.001 8	0.314 8	0.000 3	0.000 1	0.055 6	0.000 2	0.832 8	0.094 2	0.659 6	0.002 1	0.076 4	0.310 8	0.293 8	0.664 8	0.000 1	0.000 1	0.175 8	0.634 2	0.130 2	0.552 8	0.686 8	0.700 8	0.076 3	0.110 6	0.770 8	0.000 1	0.000 4	0.430 8	0.000 5	0.319 6	0.166 7	0.542 8	0.327 7	0.205 7	0.332 7	0.052 7	0.375 4	0.444 8	0.000 2	0.012 8	0.930 8	0.203 1	0.000 1	0.000 4	0.046 4	0.175 5	0.413 7	0.592 6	0.471 7	0.299 6	0.152 8	0.340 7	0.247 8	0.000 2	0.000 1	0.225 6	0.058 2	0.037 2	0.000 3	0.207 1	0.862 8	0.014 5	0.548 5	0.033 7	0.233 7	0.816 7	0.000 3	0.000 1	0.542 7	0.123 2	0.121 1	0.019 2	0.000 1	0.000 3	0.463 7	0.454 8	0.045 8	0.128 8	0.557 7	0.235 5	0.441 7	0.063 5	0.484 8	0.000 3	0.308 8	0.000 1	0.000 2	0.000 2	0.318 8	0.000 1	0.000 4	0.000 1	0.545 7	0.543 6	0.164 8	0.734 4	0.000 3	0.000 1	0.215 8	0.371 7	0.198 5	0.743 5	0.205 7	0.062 7	0.000 4	0.079 6	0.000 1	0.683 7	0.547 7	0.142 4	0.000 6	0.441 3	0.579 8	0.000 1	0.464 6	0.098 4	0.041 1	0.000 1	0.590 7	0.000 2	0.000 1	0.373 4	0.494 5	0.174 6	0.105 7	0.001 8	0.895 7	0.222 7	0.537 5	0.307 7	0.180 4	0.625 5	0.000 1	0.000 6	0.591 8	0.609 7	0.398 6	0.000 1	0.766 8	0.014 8	0.638 8	0.000 1	0.377 5	0.004 6	0.206 7	0.609 8	0.465 2
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
AWCS	0.305 5	0.508 5	0.225 5	0.142 2	0.463 5	0.063 6	0.195 5	0.000 1	0.000 1	0.000 1	0.467 2	0.551 1	0.504 3	0.773 3	0.764 5	0.142 5	0.029 8	0.000 3	0.626 6	0.100 5	0.000 4	0.360 5	0.179 6	0.507 6	0.137 6	0.006 3	0.300 5	0.000 1	0.000 3	0.172 3	0.364 6	0.512 2	0.000 1	0.056 5	0.000 2	0.865 5	0.093 3	0.634 8	0.000 3	0.071 6	0.396 6	0.296 7	0.876 4	0.000 1	0.000 1	0.373 5	0.436 7	0.063 6	0.749 1	0.877 2	0.721 3	0.131 2	0.124 5	0.804 6	0.000 1	0.000 4	0.515 4	0.010 4	0.452 5	0.252 4	0.578 5	0.417 2	0.179 8	0.484 4	0.171 3	0.337 5	0.606 5	0.000 2	0.115 3	0.937 5	0.142 4	0.000 1	0.008 3	0.000 7	0.157 7	0.484 5	0.402 8	0.501 6	0.339 3	0.553 1	0.529 2	0.478 5	0.000 2	0.000 1	0.404 3	0.001 4	0.022 6	0.077 2	0.000 3	0.894 6	0.219 1	0.628 2	0.093 6	0.305 5	0.886 1	0.233 2	0.000 1	0.603 3	0.112 3	0.023 5	0.000 4	0.000 1	0.000 3	0.741 2	0.664 2	0.097 6	0.253 5	0.782 5	0.264 3	0.523 4	0.154 1	0.707 7	0.000 3	0.411 3	0.000 1	0.000 2	0.000 2	0.332 7	0.000 1	0.000 4	0.000 1	0.602 2	0.595 4	0.185 7	0.656 7	0.159 1	0.000 1	0.355 5	0.424 6	0.154 6	0.729 6	0.516 5	0.220 4	0.620 2	0.084 5	0.000 1	0.707 5	0.651 4	0.173 1	0.014 5	0.381 8	0.582 7	0.000 1	0.619 2	0.049 6	0.000 4	0.000 1	0.702 1	0.000 2	0.000 1	0.302 7	0.489 6	0.317 4	0.334 4	0.392 1	0.922 5	0.254 5	0.533 6	0.394 4	0.129 8	0.613 6	0.000 1	0.000 6	0.820 1	0.649 5	0.749 5	0.000 1	0.782 5	0.282 3	0.863 3	0.000 1	0.288 7	0.006 5	0.220 5	0.633 5	0.542 1

LGround	0.272 6	0.485 6	0.184 6	0.106 6	0.476 3	0.077 5	0.218 4	0.000 1	0.000 1	0.000 1	0.547 1	0.295 6	0.540 1	0.746 5	0.745 6	0.058 7	0.112 7	0.005 1	0.658 5	0.077 8	0.000 4	0.322 6	0.178 7	0.512 5	0.190 5	0.199 1	0.277 6	0.000 1	0.000 3	0.173 2	0.399 5	0.000 3	0.000 1	0.039 7	0.000 2	0.858 6	0.085 5	0.676 3	0.002 1	0.103 2	0.498 3	0.323 5	0.703 6	0.000 1	0.000 1	0.296 6	0.549 4	0.216 1	0.702 3	0.768 6	0.718 5	0.028 4	0.092 7	0.786 7	0.000 1	0.000 4	0.453 7	0.022 3	0.251 8	0.252 4	0.572 6	0.348 6	0.321 4	0.514 2	0.063 6	0.279 7	0.552 6	0.000 2	0.019 7	0.932 6	0.132 7	0.000 1	0.000 4	0.000 7	0.156 8	0.457 6	0.623 5	0.518 5	0.265 7	0.358 4	0.381 6	0.395 6	0.000 2	0.000 1	0.127 8	0.012 3	0.051 1	0.000 3	0.000 3	0.886 7	0.014 5	0.437 8	0.179 2	0.244 6	0.826 6	0.000 3	0.000 1	0.599 4	0.136 1	0.085 2	0.000 4	0.000 1	0.000 3	0.565 4	0.612 5	0.143 2	0.207 6	0.566 6	0.232 6	0.446 6	0.127 2	0.708 6	0.000 3	0.384 4	0.000 1	0.000 2	0.000 2	0.402 5	0.000 1	0.059 2	0.000 1	0.525 8	0.566 5	0.229 6	0.659 6	0.000 3	0.000 1	0.265 6	0.446 5	0.147 7	0.720 8	0.597 4	0.066 6	0.000 4	0.187 2	0.000 1	0.726 4	0.467 8	0.134 5	0.000 6	0.413 6	0.629 5	0.000 1	0.363 7	0.055 5	0.022 2	0.000 1	0.626 5	0.000 2	0.000 1	0.323 6	0.479 8	0.154 7	0.117 6	0.028 7	0.901 6	0.243 6	0.415 8	0.295 8	0.143 5	0.610 7	0.000 1	0.000 6	0.777 5	0.397 8	0.324 7	0.000 1	0.778 6	0.179 5	0.702 7	0.000 1	0.274 8	0.404 1	0.233 4	0.622 6	0.398 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34D	0.253 7	0.463 7	0.154 8	0.102 7	0.381 8	0.084 3	0.134 8	0.000 1	0.000 1	0.000 1	0.386 4	0.141 8	0.279 8	0.737 7	0.703 7	0.014 8	0.164 6	0.000 3	0.663 4	0.092 7	0.000 4	0.224 7	0.291 4	0.531 3	0.056 8	0.000 4	0.242 7	0.000 1	0.000 3	0.013 6	0.331 7	0.000 3	0.000 1	0.035 8	0.001 1	0.858 6	0.059 7	0.650 7	0.000 3	0.056 7	0.353 7	0.299 6	0.670 7	0.000 1	0.000 1	0.284 7	0.484 6	0.071 5	0.594 7	0.720 7	0.710 6	0.027 5	0.068 8	0.813 5	0.000 1	0.005 3	0.492 6	0.164 1	0.274 7	0.111 8	0.571 7	0.307 8	0.293 5	0.307 8	0.150 4	0.163 8	0.531 7	0.002 1	0.545 1	0.932 6	0.093 8	0.000 1	0.000 4	0.002 6	0.159 6	0.368 8	0.581 7	0.440 8	0.228 8	0.406 2	0.282 8	0.294 7	0.000 2	0.000 1	0.189 7	0.060 1	0.036 3	0.000 3	0.000 3	0.897 5	0.000 8	0.525 6	0.025 8	0.205 8	0.771 8	0.000 3	0.000 1	0.593 5	0.108 5	0.044 3	0.000 4	0.000 1	0.000 3	0.282 8	0.589 6	0.094 7	0.169 7	0.466 8	0.227 7	0.419 8	0.125 3	0.757 5	0.002 1	0.334 7	0.000 1	0.000 2	0.000 2	0.357 6	0.000 1	0.000 4	0.000 1	0.582 5	0.513 8	0.337 5	0.612 8	0.000 3	0.000 1	0.250 7	0.352 8	0.136 8	0.724 7	0.655 2	0.280 3	0.000 4	0.046 8	0.000 1	0.606 8	0.559 6	0.159 2	0.102 1	0.445 2	0.655 2	0.000 1	0.310 8	0.117 2	0.000 4	0.000 1	0.581 8	0.026 1	0.000 1	0.265 8	0.483 7	0.084 8	0.097 8	0.044 6	0.865 8	0.142 8	0.588 4	0.351 6	0.272 2	0.596 8	0.000 1	0.003 4	0.622 7	0.720 4	0.096 8	0.000 1	0.771 7	0.016 7	0.772 6	0.000 1	0.302 6	0.194 4	0.214 6	0.621 7	0.197 8
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%	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	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	wardrobe	washing machine	water bottle	water cooler	water pitcher	whiteboard	window	windowsill

Mask3D Scannet200	0.445 1	0.653 1	0.392 1	0.254 1	0.648 1	0.097 1	0.125 5	0.000 1	0.000 1	0.000 1	0.657 1	0.971 1	0.451 2	1.000 1	1.000 1	0.640 1	0.500 1	0.045 1	1.000 1	0.241 2	0.409 1	0.363 1	0.440 1	0.686 3	0.300 1	0.000 1	0.201 1	0.000 1	0.009 1	0.290 1	0.556 1	1.000 1	0.000 1	0.063 3	0.000 1	0.830 1	0.573 1	0.844 2	0.333 1	0.204 1	0.058 5	0.158 5	0.552 2	0.056 1	0.000 1	1.000 1	0.725 4	0.750 1	0.927 1	1.000 1	0.888 4	0.042 3	0.120 2	0.615 4	0.226 1	0.250 1	0.890 1	0.792 1	0.677 2	0.510 2	0.818 1	0.699 1	0.512 2	0.167 5	0.125 1	0.315 2	0.943 1	0.309 1	0.017 3	0.200 3	0.000 1	0.188 1	0.000 1	0.183 3	0.815 1	1.000 1	0.827 1	0.741 1	0.442 3	0.414 4	0.600 1	0.000 1	0.000 1	0.458 1	0.049 3	0.321 1	0.381 1	0.000 1	0.908 2	0.400 1	0.841 1	0.260 1	0.710 1	0.966 1	0.265 1	0.000 1	0.924 1	0.152 1	0.025 2	0.500 1	0.027 1	0.028 1	1.000 1	0.556 5	0.016 1	0.080 5	0.500 1	0.694 3	0.608 1	0.084 1	0.604 3	0.194 1	0.538 3	0.000 1	0.500 1	0.000 2	0.354 4	0.000 1	1.000 1	0.000 1	0.761 2	0.930 1	0.053 4	0.890 3	1.000 1	0.008 2	0.262 1	0.358 2	1.000 1	1.000 1	0.792 4	0.966 1	1.000 1	0.765 2	0.004 2	0.930 1	0.780 2	0.330 2	0.027 2	0.625 1	0.974 4	0.050 1	0.412 5	0.021 2	0.000 3	0.000 2	0.778 1	0.000 1	0.000 1	0.493 2	0.746 2	0.454 1	0.335 2	0.396 1	0.930 5	0.551 2	1.000 1	0.552 1	0.606 1	0.853 1	0.000 1	0.004 1	0.806 1	1.000 1	0.727 2	0.000 1	0.042 3	0.745 2	0.000 1	0.399 4	0.391 1	0.630 1	0.721 1	0.619 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
TD3D Scannet200	0.379 2	0.603 2	0.306 2	0.190 2	0.635 2	0.073 2	0.500 1	0.000 1	0.000 1	0.000 1	0.495 3	0.735 2	0.275 5	1.000 1	0.979 2	0.590 2	0.000 4	0.021 2	0.000 3	0.146 3	0.000 2	0.356 2	0.173 5	0.795 1	0.226 2	0.000 1	0.173 2	0.000 1	0.000 2	0.226 2	0.390 2	0.000 2	0.000 1	0.250 1	0.000 1	0.706 2	0.061 3	0.885 1	0.093 2	0.186 2	0.259 4	0.200 1	0.667 1	0.000 2	0.000 1	0.667 2	0.825 1	0.250 4	0.834 4	1.000 1	0.958 1	0.553 1	0.111 3	0.748 1	0.220 2	0.051 2	0.866 2	0.792 1	0.390 5	0.045 5	0.800 2	0.302 5	0.517 1	0.533 3	0.113 2	0.427 1	0.843 2	0.000 2	0.458 1	0.600 1	0.000 1	0.101 2	0.000 1	0.259 1	0.717 2	0.500 2	0.615 2	0.520 2	0.526 2	0.457 1	0.270 4	0.000 1	0.000 1	0.400 2	0.088 2	0.294 2	0.181 2	0.000 1	1.000 1	0.400 1	0.710 5	0.103 3	0.477 5	0.905 2	0.061 2	0.000 1	0.906 2	0.102 2	0.232 1	0.125 2	0.000 2	0.003 2	0.792 3	1.000 1	0.000 2	0.102 3	0.125 4	0.559 5	0.523 3	0.075 2	0.715 1	0.000 2	0.424 5	0.000 1	0.396 2	0.250 1	0.638 1	0.000 1	0.000 2	0.000 1	0.622 5	0.833 2	0.221 1	0.970 1	0.250 2	0.038 1	0.260 2	0.415 1	0.125 2	1.000 1	1.000 1	0.857 2	0.000 2	0.908 1	0.012 1	0.869 3	0.836 1	0.635 1	0.111 1	0.625 1	1.000 1	0.020 2	0.510 1	0.003 3	0.009 2	1.000 1	0.778 1	0.000 1	0.000 1	0.370 3	0.755 1	0.288 2	0.333 3	0.274 2	1.000 1	0.557 1	0.731 2	0.456 2	0.433 3	0.769 5	0.000 1	0.000 2	0.621 4	1.000 1	0.458 4	0.000 1	0.196 2	0.817 1	0.000 1	0.472 1	0.222 3	0.205 5	0.689 2	0.274 3

CSC-Pretrain Inst.	0.275 5	0.466 5	0.218 4	0.110 5	0.625 3	0.007 5	0.500 1	0.000 1	0.000 1	0.000 1	0.000 5	0.222 5	0.377 4	1.000 1	0.661 5	0.400 3	0.000 4	0.000 3	0.000 3	0.119 5	0.000 2	0.000 5	0.277 4	0.685 4	0.067 3	0.000 1	0.132 3	0.000 1	0.000 2	0.000 4	0.367 4	0.000 2	0.000 1	0.000 4	0.000 1	0.591 3	0.055 4	0.783 5	0.000 3	0.014 3	0.500 2	0.161 4	0.278 3	0.000 2	0.000 1	0.667 2	0.768 2	0.500 2	0.866 2	1.000 1	0.829 5	0.000 4	0.019 5	0.555 5	0.000 3	0.000 3	0.305 5	0.000 3	0.750 1	0.200 4	0.783 4	0.429 3	0.395 3	0.677 2	0.020 5	0.286 3	0.584 5	0.000 2	0.000 4	0.115 5	0.000 1	0.000 3	0.000 1	0.145 5	0.423 5	0.500 2	0.364 5	0.369 4	0.571 1	0.448 3	0.206 5	0.000 1	0.000 1	0.200 3	0.106 1	0.065 5	0.000 3	0.000 1	0.750 3	0.200 3	0.774 2	0.000 5	0.501 3	0.841 4	0.000 3	0.000 1	0.692 5	0.063 4	0.000 3	0.000 3	0.000 2	0.000 3	0.500 4	0.649 2	0.000 2	0.084 4	0.125 4	0.719 1	0.413 5	0.004 4	0.450 5	0.000 2	0.638 1	0.000 1	0.000 3	0.000 2	0.505 3	0.000 1	0.000 2	0.000 1	0.727 3	0.833 2	0.221 2	0.779 5	0.000 3	0.000 3	0.168 5	0.311 5	0.125 2	0.571 4	0.500 5	0.143 5	0.000 2	0.250 4	0.000 3	0.869 2	0.667 4	0.162 5	0.000 3	0.250 4	1.000 1	0.000 3	0.500 2	0.000 4	0.000 3	0.000 2	0.689 4	0.000 1	0.000 1	0.312 4	0.383 5	0.114 3	0.333 3	0.000 4	0.997 3	0.420 3	0.613 4	0.212 5	0.500 2	0.819 2	0.000 1	0.000 2	0.768 2	1.000 1	0.918 1	0.000 1	0.000 4	0.278 5	0.000 1	0.333 5	0.000 5	0.353 2	0.546 5	0.258 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34D Inst.	0.280 4	0.488 4	0.192 5	0.124 4	0.593 4	0.010 4	0.500 1	0.000 1	0.000 1	0.000 1	0.447 4	0.535 4	0.445 3	1.000 1	0.861 4	0.400 3	0.225 2	0.000 3	0.000 3	0.142 4	0.000 2	0.074 4	0.342 3	0.467 5	0.067 3	0.000 1	0.119 5	0.000 1	0.000 2	0.000 4	0.337 5	0.000 2	0.000 1	0.000 4	0.000 1	0.506 5	0.070 2	0.804 4	0.000 3	0.000 4	0.333 3	0.172 3	0.150 5	0.000 2	0.000 1	0.479 5	0.745 3	0.000 5	0.830 5	1.000 1	0.904 3	0.167 2	0.090 4	0.732 2	0.000 3	0.000 3	0.443 4	0.000 3	0.500 3	0.542 1	0.772 5	0.396 4	0.077 5	0.385 4	0.044 4	0.118 5	0.777 4	0.000 2	0.000 4	0.200 3	0.000 1	0.000 3	0.000 1	0.148 4	0.502 4	0.500 2	0.419 4	0.159 5	0.281 4	0.404 5	0.317 3	0.000 1	0.000 1	0.200 3	0.000 4	0.077 3	0.000 3	0.000 1	0.750 3	0.200 3	0.715 4	0.021 4	0.551 2	0.828 5	0.000 3	0.000 1	0.743 4	0.059 5	0.000 3	0.000 3	0.000 2	0.000 3	0.125 5	0.648 3	0.000 2	0.191 2	0.500 1	0.669 4	0.502 4	0.000 5	0.568 4	0.000 2	0.516 4	0.000 1	0.000 3	0.000 2	0.305 5	0.000 1	0.000 2	0.000 1	0.825 1	0.833 2	0.021 5	0.918 2	0.000 3	0.000 3	0.191 4	0.346 4	0.100 4	0.981 3	1.000 1	0.286 4	0.000 2	0.000 5	0.000 3	0.868 4	0.648 5	0.292 3	0.000 3	0.375 3	1.000 1	0.000 3	0.500 2	0.000 4	0.333 1	0.000 2	0.538 5	0.000 1	0.000 1	0.213 5	0.518 4	0.098 4	0.528 1	0.250 3	0.997 3	0.284 5	0.677 3	0.398 3	0.167 4	0.790 4	0.000 1	0.000 2	0.618 5	0.903 5	0.200 5	0.000 1	0.333 1	0.333 4	0.000 1	0.442 3	0.083 4	0.213 4	0.587 4	0.131 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
LGround Inst.	0.314 3	0.529 3	0.225 3	0.155 3	0.578 5	0.010 3	0.500 1	0.000 1	0.000 1	0.000 1	0.515 2	0.556 3	0.696 1	1.000 1	0.927 3	0.400 3	0.083 3	0.000 3	1.000 1	0.252 1	0.000 2	0.167 3	0.350 2	0.731 2	0.067 3	0.000 1	0.123 4	0.000 1	0.000 2	0.036 3	0.372 3	0.000 2	0.000 1	0.250 1	0.000 1	0.569 4	0.031 5	0.810 3	0.000 3	0.000 4	0.630 1	0.183 2	0.278 3	0.000 2	0.000 1	0.582 4	0.589 5	0.500 2	0.863 3	1.000 1	0.940 2	0.000 4	0.144 1	0.716 3	0.000 3	0.000 3	0.484 3	0.000 3	0.500 3	0.400 3	0.798 3	0.500 2	0.278 4	0.750 1	0.093 3	0.166 4	0.783 3	0.000 2	0.200 2	0.400 2	0.000 1	0.000 3	0.000 1	0.219 2	0.539 3	0.500 2	0.578 3	0.413 3	0.181 5	0.457 2	0.375 2	0.000 1	0.000 1	0.050 5	0.000 4	0.077 4	0.000 3	0.000 1	0.500 5	0.000 5	0.743 3	0.250 2	0.488 4	0.846 3	0.000 3	0.000 1	0.800 3	0.069 3	0.000 3	0.000 3	0.000 2	0.000 3	1.000 1	0.607 4	0.000 2	0.200 1	0.500 1	0.694 2	0.528 2	0.063 3	0.659 2	0.000 2	0.594 2	0.000 1	0.000 3	0.000 2	0.571 2	0.000 1	0.000 2	0.000 1	0.716 4	0.647 5	0.221 2	0.857 4	0.000 3	0.000 3	0.217 3	0.346 3	0.071 5	0.530 5	1.000 1	0.429 3	0.000 2	0.286 3	0.000 3	0.826 5	0.706 3	0.208 4	0.000 3	0.250 4	0.744 5	0.000 3	0.500 2	0.042 1	0.000 3	0.000 2	0.746 3	0.000 1	0.000 1	0.517 1	0.625 3	0.085 5	0.333 3	0.000 4	1.000 1	0.378 4	0.533 5	0.376 4	0.042 5	0.814 3	0.000 1	0.000 2	0.765 3	1.000 1	0.600 3	0.000 1	0.000 4	0.667 3	0.000 1	0.472 1	0.333 2	0.337 3	0.605 3	0.305 2
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 24	0.685 9	0.714 2	0.979 1	0.594 4	0.310 20	0.801 1	0.892 11	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 11	0.836 16	0.790 1	0.875 2	0.576 3	0.905 2	0.704 3	0.739 1	0.969 7	0.611 1	0.349 6	0.756 14	0.958 1	0.702 35	0.805 12	0.708 6	0.916 23	0.898 1	0.801 1

PPT-SpUNet-Joint	0.766 3	0.932 2	0.794 25	0.829 18	0.751 13	0.854 11	0.540 13	0.903 3	0.630 25	0.672 9	0.963 9	0.565 15	0.357 4	0.788 2	0.900 7	0.737 20	0.802 13	0.685 12	0.950 2	0.887 2	0.780 2

OctFormer	0.766 3	0.925 3	0.808 18	0.849 7	0.786 2	0.846 17	0.566 6	0.876 11	0.690 7	0.674 8	0.960 11	0.576 11	0.226 56	0.753 16	0.904 5	0.777 7	0.815 6	0.722 4	0.923 19	0.877 8	0.776 4
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net	0.764 5	0.924 4	0.819 9	0.840 14	0.757 9	0.853 12	0.580 1	0.848 18	0.709 2	0.643 17	0.958 14	0.587 8	0.295 27	0.753 16	0.884 14	0.758 14	0.815 6	0.725 2	0.927 18	0.867 14	0.743 9

OccuSeg+Semantic	0.764 5	0.758 50	0.796 23	0.839 15	0.746 15	0.907 1	0.562 7	0.850 17	0.680 11	0.672 9	0.978 2	0.610 2	0.335 10	0.777 5	0.819 34	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 6	0.844 12	0.770 5	0.852 13	0.577 2	0.847 20	0.711 1	0.640 21	0.958 14	0.592 5	0.217 62	0.762 11	0.888 12	0.758 14	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 5	0.858 5	0.776 4	0.837 24	0.548 11	0.896 6	0.649 18	0.675 6	0.962 10	0.586 9	0.335 10	0.771 7	0.802 38	0.770 10	0.787 25	0.691 10	0.936 11	0.880 7	0.761 6

PointTransformerV2	0.752 9	0.742 57	0.809 16	0.872 1	0.758 8	0.860 8	0.552 9	0.891 8	0.610 33	0.687 3	0.960 11	0.559 18	0.304 23	0.766 9	0.926 3	0.767 11	0.797 16	0.644 25	0.942 7	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 30	0.825 35	0.556 8	0.867 13	0.681 10	0.602 34	0.960 11	0.555 20	0.365 3	0.779 4	0.859 19	0.747 17	0.795 20	0.717 5	0.917 22	0.856 22	0.764 5

PointConvFormer	0.749 11	0.793 35	0.790 28	0.807 29	0.750 14	0.856 10	0.524 19	0.881 10	0.588 44	0.642 20	0.977 4	0.591 6	0.274 38	0.781 3	0.929 2	0.804 3	0.796 17	0.642 27	0.947 4	0.885 4	0.715 22
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNet	0.749 11	0.909 6	0.818 11	0.811 26	0.752 11	0.839 23	0.485 36	0.842 21	0.673 12	0.644 16	0.957 16	0.528 28	0.305 22	0.773 6	0.859 19	0.788 4	0.818 5	0.693 9	0.916 23	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)
MSP	0.748 13	0.623 83	0.804 20	0.859 4	0.745 16	0.824 37	0.501 27	0.912 1	0.690 7	0.685 4	0.956 17	0.567 14	0.320 16	0.768 8	0.918 4	0.720 26	0.802 13	0.676 16	0.921 20	0.881 6	0.779 3

StratifiedFormer	0.747 14	0.901 9	0.803 21	0.845 11	0.757 9	0.846 17	0.512 23	0.825 27	0.696 6	0.645 15	0.956 17	0.576 11	0.262 47	0.744 21	0.861 18	0.742 18	0.770 34	0.705 7	0.899 37	0.860 20	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 5	0.729 20	0.850 15	0.501 27	0.874 12	0.587 45	0.658 13	0.956 17	0.564 16	0.299 24	0.765 10	0.900 7	0.716 29	0.812 9	0.631 32	0.939 9	0.858 21	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 44	0.819 9	0.848 9	0.702 28	0.865 7	0.397 74	0.899 4	0.699 4	0.664 12	0.948 45	0.588 7	0.330 12	0.746 20	0.851 26	0.764 12	0.796 17	0.704 8	0.935 12	0.866 16	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
EQ-Net	0.743 17	0.620 84	0.799 22	0.849 7	0.730 19	0.822 39	0.493 34	0.897 5	0.664 13	0.681 5	0.955 21	0.562 17	0.378 1	0.760 12	0.903 6	0.738 19	0.801 15	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 18	0.816 30	0.806 19	0.807 29	0.752 11	0.828 33	0.575 4	0.839 23	0.699 4	0.637 22	0.954 26	0.520 30	0.320 16	0.755 15	0.834 30	0.760 13	0.772 31	0.676 16	0.915 25	0.862 18	0.717 21

SAT	0.742 18	0.860 17	0.765 39	0.819 21	0.769 6	0.848 16	0.533 15	0.829 25	0.663 14	0.631 23	0.955 21	0.586 9	0.274 38	0.753 16	0.896 9	0.729 21	0.760 41	0.666 20	0.921 20	0.855 24	0.733 11

TXC	0.740 20	0.842 22	0.832 4	0.805 33	0.715 24	0.846 17	0.473 38	0.885 9	0.615 29	0.671 11	0.971 6	0.547 21	0.320 16	0.697 25	0.799 40	0.777 7	0.819 3	0.682 14	0.946 5	0.871 12	0.696 27

LargeKernel3D	0.739 21	0.909 6	0.820 8	0.806 31	0.740 17	0.852 13	0.545 12	0.826 26	0.594 43	0.643 17	0.955 21	0.541 23	0.263 46	0.723 23	0.858 21	0.775 9	0.767 35	0.678 15	0.933 14	0.848 29	0.694 28
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
MinkowskiNet	0.736 22	0.859 18	0.818 11	0.832 17	0.709 26	0.840 22	0.521 21	0.853 16	0.660 16	0.643 17	0.951 35	0.544 22	0.286 32	0.731 22	0.893 10	0.675 43	0.772 31	0.683 13	0.874 55	0.852 27	0.727 15
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 23	0.890 10	0.837 3	0.864 2	0.726 21	0.873 3	0.530 18	0.824 28	0.489 77	0.647 14	0.978 2	0.609 3	0.336 9	0.624 40	0.733 49	0.758 14	0.776 29	0.570 56	0.949 3	0.877 8	0.728 13

Retro-FPN	0.725 24	0.827 27	0.809 16	0.862 3	0.711 25	0.836 26	0.443 60	0.893 7	0.579 50	0.675 6	0.956 17	0.519 31	0.298 25	0.606 44	0.766 43	0.782 5	0.792 23	0.644 25	0.914 26	0.867 14	0.722 19

PointTransformer++	0.725 24	0.727 65	0.811 15	0.819 21	0.765 7	0.841 21	0.502 26	0.814 33	0.621 28	0.623 25	0.955 21	0.556 19	0.284 33	0.620 41	0.866 16	0.781 6	0.757 44	0.648 23	0.932 16	0.862 18	0.709 23

SparseConvNet	0.725 24	0.647 80	0.821 7	0.846 10	0.721 22	0.869 4	0.533 15	0.754 47	0.603 39	0.614 27	0.955 21	0.572 13	0.325 14	0.710 24	0.870 15	0.724 24	0.823 2	0.628 33	0.934 13	0.865 17	0.683 31

MatchingNet	0.724 27	0.812 32	0.812 14	0.810 27	0.735 18	0.834 28	0.495 33	0.860 15	0.572 52	0.602 34	0.954 26	0.512 33	0.280 35	0.757 13	0.845 28	0.725 23	0.780 27	0.606 42	0.937 10	0.851 28	0.700 26

INS-Conv-semantic	0.717 28	0.751 53	0.759 42	0.812 25	0.704 27	0.868 5	0.537 14	0.842 21	0.609 35	0.608 30	0.953 29	0.534 25	0.293 28	0.616 42	0.864 17	0.719 28	0.793 21	0.640 28	0.933 14	0.845 33	0.663 36

PointMetaBase	0.714 29	0.835 23	0.785 29	0.821 19	0.684 32	0.846 17	0.531 17	0.865 14	0.614 30	0.596 38	0.953 29	0.500 36	0.246 52	0.674 26	0.888 12	0.692 36	0.764 37	0.624 34	0.849 70	0.844 34	0.675 33

contrastBoundary	0.705 30	0.769 47	0.775 34	0.809 28	0.687 31	0.820 42	0.439 62	0.812 34	0.661 15	0.591 40	0.945 53	0.515 32	0.171 80	0.633 37	0.856 22	0.720 26	0.796 17	0.668 19	0.889 44	0.847 30	0.689 29
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR	0.702 31	0.889 11	0.745 51	0.813 24	0.672 35	0.818 46	0.493 34	0.815 32	0.623 26	0.610 28	0.947 47	0.470 46	0.249 51	0.594 46	0.848 27	0.705 33	0.779 28	0.646 24	0.892 42	0.823 40	0.611 50
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 32	0.825 28	0.796 23	0.723 52	0.716 23	0.832 29	0.433 64	0.816 30	0.634 23	0.609 29	0.969 7	0.418 71	0.344 7	0.559 58	0.833 31	0.715 30	0.808 11	0.560 61	0.902 34	0.847 30	0.680 32

JSENet	0.699 33	0.881 13	0.762 40	0.821 19	0.667 36	0.800 59	0.522 20	0.792 39	0.613 31	0.607 31	0.935 73	0.492 38	0.205 67	0.576 51	0.853 24	0.691 37	0.758 43	0.652 22	0.872 58	0.828 37	0.649 40
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 34	0.743 56	0.794 25	0.655 76	0.684 32	0.822 39	0.497 32	0.719 57	0.622 27	0.617 26	0.977 4	0.447 58	0.339 8	0.750 19	0.664 65	0.703 34	0.790 24	0.596 46	0.946 5	0.855 24	0.647 41
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 35	0.732 61	0.772 35	0.786 38	0.677 34	0.866 6	0.517 22	0.848 18	0.509 69	0.626 24	0.952 33	0.536 24	0.225 58	0.545 64	0.704 56	0.689 40	0.810 10	0.564 60	0.903 33	0.854 26	0.729 12
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 36	0.884 12	0.754 46	0.795 37	0.647 42	0.818 46	0.422 66	0.802 37	0.612 32	0.604 32	0.945 53	0.462 49	0.189 75	0.563 57	0.853 24	0.726 22	0.765 36	0.632 31	0.904 31	0.821 43	0.606 54
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 37	0.704 70	0.741 55	0.754 49	0.656 38	0.829 31	0.501 27	0.741 52	0.609 35	0.548 47	0.950 39	0.522 29	0.371 2	0.633 37	0.756 44	0.715 30	0.771 33	0.623 35	0.861 66	0.814 45	0.658 37
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 38	0.866 15	0.748 48	0.819 21	0.645 44	0.794 62	0.450 50	0.802 37	0.587 45	0.604 32	0.945 53	0.464 48	0.201 70	0.554 60	0.840 29	0.723 25	0.732 53	0.602 44	0.907 29	0.822 42	0.603 57

KP-FCNN	0.684 39	0.847 21	0.758 44	0.784 40	0.647 42	0.814 49	0.473 38	0.772 42	0.605 37	0.594 39	0.935 73	0.450 56	0.181 78	0.587 47	0.805 37	0.690 38	0.785 26	0.614 38	0.882 48	0.819 44	0.632 46
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 39	0.712 69	0.784 30	0.782 42	0.658 37	0.835 27	0.499 31	0.823 29	0.641 20	0.597 37	0.950 39	0.487 39	0.281 34	0.575 52	0.619 68	0.647 56	0.764 37	0.620 37	0.871 61	0.846 32	0.688 30

VACNN++	0.684 39	0.728 64	0.757 45	0.776 43	0.690 29	0.804 56	0.464 44	0.816 30	0.577 51	0.587 41	0.945 53	0.508 35	0.276 37	0.671 27	0.710 54	0.663 48	0.750 47	0.589 51	0.881 49	0.832 36	0.653 39

Superpoint Network	0.683 42	0.851 20	0.728 59	0.800 36	0.653 40	0.806 54	0.468 41	0.804 35	0.572 52	0.602 34	0.946 50	0.453 55	0.239 55	0.519 69	0.822 32	0.689 40	0.762 40	0.595 48	0.895 40	0.827 38	0.630 47

PointContrast_LA_SEM	0.683 42	0.757 51	0.784 30	0.786 38	0.639 46	0.824 37	0.408 69	0.775 41	0.604 38	0.541 49	0.934 77	0.532 26	0.269 42	0.552 61	0.777 41	0.645 59	0.793 21	0.640 28	0.913 27	0.824 39	0.671 34

VI-PointConv	0.676 44	0.770 46	0.754 46	0.783 41	0.621 50	0.814 49	0.552 9	0.758 45	0.571 54	0.557 45	0.954 26	0.529 27	0.268 44	0.530 67	0.682 60	0.675 43	0.719 56	0.603 43	0.888 45	0.833 35	0.665 35
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 45	0.789 36	0.748 48	0.763 47	0.635 48	0.814 49	0.407 71	0.747 49	0.581 49	0.573 42	0.950 39	0.484 40	0.271 41	0.607 43	0.754 45	0.649 53	0.774 30	0.596 46	0.883 47	0.823 40	0.606 54

SALANet	0.670 46	0.816 30	0.770 37	0.768 45	0.652 41	0.807 53	0.451 47	0.747 49	0.659 17	0.545 48	0.924 83	0.473 45	0.149 90	0.571 54	0.811 36	0.635 62	0.746 48	0.623 35	0.892 42	0.794 57	0.570 67

PointConv	0.666 47	0.781 39	0.759 42	0.699 61	0.644 45	0.822 39	0.475 37	0.779 40	0.564 57	0.504 65	0.953 29	0.428 65	0.203 69	0.586 49	0.754 45	0.661 49	0.753 45	0.588 52	0.902 34	0.813 47	0.642 42
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 47	0.703 71	0.781 32	0.751 51	0.655 39	0.830 30	0.471 40	0.769 43	0.474 80	0.537 51	0.951 35	0.475 44	0.279 36	0.635 35	0.698 59	0.675 43	0.751 46	0.553 66	0.816 77	0.806 49	0.703 25
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 49	0.746 54	0.708 63	0.722 53	0.638 47	0.820 42	0.451 47	0.566 84	0.599 41	0.541 49	0.950 39	0.510 34	0.313 19	0.648 32	0.819 34	0.616 67	0.682 72	0.590 50	0.869 62	0.810 48	0.656 38
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 50	0.778 40	0.702 66	0.806 31	0.619 51	0.813 52	0.468 41	0.693 65	0.494 72	0.524 57	0.941 64	0.449 57	0.298 25	0.510 71	0.821 33	0.675 43	0.727 55	0.568 58	0.826 75	0.803 51	0.637 44
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 51	0.698 72	0.743 53	0.650 77	0.564 68	0.820 42	0.505 25	0.758 45	0.631 24	0.479 70	0.945 53	0.480 42	0.226 56	0.572 53	0.774 42	0.690 38	0.735 51	0.614 38	0.853 69	0.776 72	0.597 60
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 52	0.752 52	0.734 57	0.664 74	0.583 63	0.815 48	0.399 73	0.754 47	0.639 21	0.535 53	0.942 62	0.470 46	0.309 21	0.665 28	0.539 74	0.650 52	0.708 61	0.635 30	0.857 68	0.793 59	0.642 42
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 53	0.778 40	0.731 58	0.699 61	0.577 64	0.829 31	0.446 52	0.736 53	0.477 79	0.523 59	0.945 53	0.454 53	0.269 42	0.484 78	0.749 48	0.618 65	0.738 49	0.599 45	0.827 74	0.792 62	0.621 49

MVPNet	0.641 54	0.831 24	0.715 61	0.671 71	0.590 59	0.781 68	0.394 75	0.679 67	0.642 19	0.553 46	0.937 70	0.462 49	0.256 48	0.649 31	0.406 87	0.626 63	0.691 69	0.666 20	0.877 51	0.792 62	0.608 53
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN	0.641 54	0.776 42	0.703 65	0.721 54	0.557 71	0.826 34	0.451 47	0.672 69	0.563 58	0.483 69	0.943 61	0.425 68	0.162 85	0.644 33	0.726 50	0.659 50	0.709 60	0.572 55	0.875 53	0.786 67	0.559 72

PointMRNet	0.640 56	0.717 68	0.701 67	0.692 64	0.576 65	0.801 58	0.467 43	0.716 58	0.563 58	0.459 75	0.953 29	0.429 64	0.169 82	0.581 50	0.854 23	0.605 68	0.710 58	0.550 67	0.894 41	0.793 59	0.575 65

FPConv	0.639 57	0.785 37	0.760 41	0.713 59	0.603 54	0.798 60	0.392 76	0.534 89	0.603 39	0.524 57	0.948 45	0.457 51	0.250 50	0.538 65	0.723 52	0.598 72	0.696 67	0.614 38	0.872 58	0.799 52	0.567 69
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 58	0.797 34	0.769 38	0.641 82	0.590 59	0.820 42	0.461 45	0.537 88	0.637 22	0.536 52	0.947 47	0.388 79	0.206 66	0.656 29	0.668 63	0.647 56	0.732 53	0.585 53	0.868 63	0.793 59	0.473 91

PointSPNet	0.637 59	0.734 60	0.692 74	0.714 58	0.576 65	0.797 61	0.446 52	0.743 51	0.598 42	0.437 80	0.942 62	0.403 75	0.150 89	0.626 39	0.800 39	0.649 53	0.697 66	0.557 64	0.846 71	0.777 71	0.563 70

SConv	0.636 60	0.830 25	0.697 70	0.752 50	0.572 67	0.780 70	0.445 54	0.716 58	0.529 63	0.530 54	0.951 35	0.446 59	0.170 81	0.507 73	0.666 64	0.636 61	0.682 72	0.541 72	0.886 46	0.799 52	0.594 61

Supervoxel-CNN	0.635 61	0.656 78	0.711 62	0.719 55	0.613 52	0.757 79	0.444 57	0.765 44	0.534 62	0.566 43	0.928 81	0.478 43	0.272 40	0.636 34	0.531 76	0.664 47	0.645 82	0.508 80	0.864 65	0.792 62	0.611 50

joint point-based	0.634 62	0.614 85	0.778 33	0.667 73	0.633 49	0.825 35	0.420 67	0.804 35	0.467 82	0.561 44	0.951 35	0.494 37	0.291 29	0.566 55	0.458 82	0.579 79	0.764 37	0.559 63	0.838 72	0.814 45	0.598 59
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 63	0.731 62	0.688 77	0.675 68	0.591 58	0.784 67	0.444 57	0.565 85	0.610 33	0.492 67	0.949 43	0.456 52	0.254 49	0.587 47	0.706 55	0.599 71	0.665 78	0.612 41	0.868 63	0.791 66	0.579 64

APCF-Net	0.631 64	0.742 57	0.687 79	0.672 69	0.557 71	0.792 65	0.408 69	0.665 70	0.545 60	0.508 62	0.952 33	0.428 65	0.186 76	0.634 36	0.702 57	0.620 64	0.706 62	0.555 65	0.873 56	0.798 54	0.581 63
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF	0.631 64	0.626 82	0.745 51	0.801 35	0.607 53	0.751 80	0.506 24	0.729 56	0.565 56	0.491 68	0.866 97	0.434 60	0.197 73	0.595 45	0.630 67	0.709 32	0.705 63	0.560 61	0.875 53	0.740 82	0.491 86

PointNet2-SFPN	0.631 64	0.771 44	0.692 74	0.672 69	0.524 76	0.837 24	0.440 61	0.706 63	0.538 61	0.446 77	0.944 59	0.421 70	0.219 61	0.552 61	0.751 47	0.591 75	0.737 50	0.543 71	0.901 36	0.768 74	0.557 73

FusionAwareConv	0.630 67	0.604 87	0.741 55	0.766 46	0.590 59	0.747 81	0.501 27	0.734 54	0.503 71	0.527 55	0.919 87	0.454 53	0.323 15	0.550 63	0.420 86	0.678 42	0.688 70	0.544 69	0.896 39	0.795 56	0.627 48
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 68	0.800 33	0.625 89	0.719 55	0.545 74	0.806 54	0.445 54	0.597 79	0.448 86	0.519 60	0.938 69	0.481 41	0.328 13	0.489 77	0.499 81	0.657 51	0.759 42	0.592 49	0.881 49	0.797 55	0.634 45

SegGroup_sem	0.627 69	0.818 29	0.747 50	0.701 60	0.602 55	0.764 76	0.385 80	0.629 76	0.490 75	0.508 62	0.931 80	0.409 73	0.201 70	0.564 56	0.725 51	0.618 65	0.692 68	0.539 73	0.873 56	0.794 57	0.548 76
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 70	0.830 25	0.694 72	0.757 48	0.563 69	0.772 74	0.448 51	0.647 73	0.520 65	0.509 61	0.949 43	0.431 63	0.191 74	0.496 75	0.614 69	0.647 56	0.672 76	0.535 75	0.876 52	0.783 68	0.571 66

HPEIN	0.618 71	0.729 63	0.668 80	0.647 79	0.597 57	0.766 75	0.414 68	0.680 66	0.520 65	0.525 56	0.946 50	0.432 61	0.215 63	0.493 76	0.599 70	0.638 60	0.617 87	0.570 56	0.897 38	0.806 49	0.605 56
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 72	0.858 19	0.772 35	0.489 94	0.532 75	0.792 65	0.404 72	0.643 75	0.570 55	0.507 64	0.935 73	0.414 72	0.046 99	0.510 71	0.702 57	0.602 70	0.705 63	0.549 68	0.859 67	0.773 73	0.534 79
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 73	0.760 49	0.667 81	0.649 78	0.521 77	0.793 63	0.457 46	0.648 72	0.528 64	0.434 82	0.947 47	0.401 76	0.153 88	0.454 80	0.721 53	0.648 55	0.717 57	0.536 74	0.904 31	0.765 75	0.485 87
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 74	0.634 81	0.743 53	0.697 63	0.601 56	0.781 68	0.437 63	0.585 82	0.493 73	0.446 77	0.933 78	0.394 77	0.011 101	0.654 30	0.661 66	0.603 69	0.733 52	0.526 76	0.832 73	0.761 77	0.480 88

dtc_net	0.596 75	0.683 73	0.725 60	0.715 57	0.549 73	0.803 57	0.444 57	0.647 73	0.493 73	0.495 66	0.941 64	0.409 73	0.000 103	0.424 85	0.544 73	0.598 72	0.703 65	0.522 77	0.912 28	0.792 62	0.520 82

LAP-D	0.594 76	0.720 66	0.692 74	0.637 83	0.456 86	0.773 73	0.391 78	0.730 55	0.587 45	0.445 79	0.940 67	0.381 80	0.288 30	0.434 83	0.453 84	0.591 75	0.649 80	0.581 54	0.777 81	0.749 81	0.610 52

DPC	0.592 77	0.720 66	0.700 68	0.602 87	0.480 82	0.762 78	0.380 81	0.713 61	0.585 48	0.437 80	0.940 67	0.369 82	0.288 30	0.434 83	0.509 80	0.590 77	0.639 85	0.567 59	0.772 82	0.755 79	0.592 62
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 78	0.766 48	0.659 84	0.683 66	0.470 85	0.740 83	0.387 79	0.620 78	0.490 75	0.476 71	0.922 85	0.355 85	0.245 53	0.511 70	0.511 79	0.571 80	0.643 83	0.493 84	0.872 58	0.762 76	0.600 58

ROSMRF	0.580 79	0.772 43	0.707 64	0.681 67	0.563 69	0.764 76	0.362 83	0.515 90	0.465 83	0.465 74	0.936 72	0.427 67	0.207 65	0.438 81	0.577 71	0.536 83	0.675 75	0.486 85	0.723 88	0.779 69	0.524 81

SD-DETR	0.576 80	0.746 54	0.609 93	0.445 98	0.517 78	0.643 94	0.366 82	0.714 60	0.456 84	0.468 73	0.870 96	0.432 61	0.264 45	0.558 59	0.674 61	0.586 78	0.688 70	0.482 86	0.739 86	0.733 84	0.537 78

SQN_0.1%	0.569 81	0.676 75	0.696 71	0.657 75	0.497 79	0.779 71	0.424 65	0.548 86	0.515 67	0.376 87	0.902 94	0.422 69	0.357 4	0.379 88	0.456 83	0.596 74	0.659 79	0.544 69	0.685 91	0.665 95	0.556 74

TextureNet	0.566 82	0.672 77	0.664 82	0.671 71	0.494 80	0.719 84	0.445 54	0.678 68	0.411 92	0.396 85	0.935 73	0.356 84	0.225 58	0.412 86	0.535 75	0.565 81	0.636 86	0.464 88	0.794 80	0.680 92	0.568 68
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 83	0.648 79	0.700 68	0.770 44	0.586 62	0.687 88	0.333 87	0.650 71	0.514 68	0.475 72	0.906 91	0.359 83	0.223 60	0.340 90	0.442 85	0.422 94	0.668 77	0.501 81	0.708 89	0.779 69	0.534 79

Pointnet++ & Feature	0.557 84	0.735 59	0.661 83	0.686 65	0.491 81	0.744 82	0.392 76	0.539 87	0.451 85	0.375 88	0.946 50	0.376 81	0.205 67	0.403 87	0.356 90	0.553 82	0.643 83	0.497 82	0.824 76	0.756 78	0.515 83

GMLPs	0.538 85	0.495 95	0.693 73	0.647 79	0.471 84	0.793 63	0.300 90	0.477 91	0.505 70	0.358 89	0.903 93	0.327 88	0.081 96	0.472 79	0.529 77	0.448 92	0.710 58	0.509 78	0.746 84	0.737 83	0.554 75

PanopticFusion-label	0.529 86	0.491 96	0.688 77	0.604 86	0.386 91	0.632 95	0.225 100	0.705 64	0.434 89	0.293 95	0.815 98	0.348 86	0.241 54	0.499 74	0.669 62	0.507 85	0.649 80	0.442 94	0.796 79	0.602 98	0.561 71
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 87	0.676 75	0.591 96	0.609 84	0.442 87	0.774 72	0.335 86	0.597 79	0.422 91	0.357 90	0.932 79	0.341 87	0.094 95	0.298 92	0.528 78	0.473 90	0.676 74	0.495 83	0.602 97	0.721 87	0.349 98

Online SegFusion	0.515 88	0.607 86	0.644 87	0.579 89	0.434 88	0.630 96	0.353 84	0.628 77	0.440 87	0.410 83	0.762 101	0.307 90	0.167 83	0.520 68	0.403 88	0.516 84	0.565 90	0.447 92	0.678 92	0.701 89	0.514 84
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 89	0.558 91	0.608 94	0.424 100	0.478 83	0.690 87	0.246 96	0.586 81	0.468 81	0.450 76	0.911 89	0.394 77	0.160 86	0.438 81	0.212 97	0.432 93	0.541 95	0.475 87	0.742 85	0.727 85	0.477 89

PCNN	0.498 90	0.559 90	0.644 87	0.560 91	0.420 90	0.711 86	0.229 98	0.414 92	0.436 88	0.352 91	0.941 64	0.324 89	0.155 87	0.238 97	0.387 89	0.493 86	0.529 96	0.509 78	0.813 78	0.751 80	0.504 85

3DMV	0.484 91	0.484 97	0.538 98	0.643 81	0.424 89	0.606 99	0.310 88	0.574 83	0.433 90	0.378 86	0.796 99	0.301 91	0.214 64	0.537 66	0.208 98	0.472 91	0.507 99	0.413 97	0.693 90	0.602 98	0.539 77
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 92	0.577 89	0.611 92	0.356 102	0.321 99	0.715 85	0.299 92	0.376 96	0.328 99	0.319 93	0.944 59	0.285 93	0.164 84	0.216 100	0.229 95	0.484 88	0.545 94	0.456 90	0.755 83	0.709 88	0.475 90
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 93	0.679 74	0.604 95	0.578 90	0.380 92	0.682 89	0.291 93	0.106 102	0.483 78	0.258 100	0.920 86	0.258 97	0.025 100	0.231 99	0.325 91	0.480 89	0.560 92	0.463 89	0.725 87	0.666 94	0.231 102
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 94	0.474 98	0.623 90	0.463 96	0.366 94	0.651 92	0.310 88	0.389 95	0.349 97	0.330 92	0.937 70	0.271 95	0.126 92	0.285 93	0.224 96	0.350 99	0.577 89	0.445 93	0.625 95	0.723 86	0.394 94
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 95	0.548 92	0.548 97	0.597 88	0.363 95	0.628 97	0.300 90	0.292 97	0.374 94	0.307 94	0.881 95	0.268 96	0.186 76	0.238 97	0.204 99	0.407 95	0.506 100	0.449 91	0.667 93	0.620 97	0.462 92

SurfaceConvPF	0.442 95	0.505 94	0.622 91	0.380 101	0.342 97	0.654 91	0.227 99	0.397 94	0.367 95	0.276 97	0.924 83	0.240 98	0.198 72	0.359 89	0.262 93	0.366 96	0.581 88	0.435 95	0.640 94	0.668 93	0.398 93
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 97	0.437 100	0.646 86	0.474 95	0.369 93	0.645 93	0.353 84	0.258 99	0.282 101	0.279 96	0.918 88	0.298 92	0.147 91	0.283 94	0.294 92	0.487 87	0.562 91	0.427 96	0.619 96	0.633 96	0.352 97
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 98	0.525 93	0.647 85	0.522 92	0.324 98	0.488 102	0.077 103	0.712 62	0.353 96	0.401 84	0.636 103	0.281 94	0.176 79	0.340 90	0.565 72	0.175 103	0.551 93	0.398 98	0.370 103	0.602 98	0.361 96

SPLAT Net	0.393 99	0.472 99	0.511 99	0.606 85	0.311 100	0.656 90	0.245 97	0.405 93	0.328 99	0.197 101	0.927 82	0.227 100	0.000 103	0.001 104	0.249 94	0.271 102	0.510 97	0.383 100	0.593 98	0.699 90	0.267 100
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	0.383 100	0.297 102	0.491 100	0.432 99	0.358 96	0.612 98	0.274 94	0.116 101	0.411 92	0.265 98	0.904 92	0.229 99	0.079 97	0.250 95	0.185 100	0.320 100	0.510 97	0.385 99	0.548 99	0.597 101	0.394 94

PointNet++	0.339 101	0.584 88	0.478 101	0.458 97	0.256 102	0.360 103	0.250 95	0.247 100	0.278 102	0.261 99	0.677 102	0.183 101	0.117 93	0.212 101	0.145 102	0.364 97	0.346 103	0.232 103	0.548 99	0.523 102	0.252 101
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNet	0.308 102	0.353 101	0.290 103	0.278 103	0.166 103	0.553 100	0.169 102	0.286 98	0.147 103	0.148 103	0.908 90	0.182 102	0.064 98	0.023 103	0.018 104	0.354 98	0.363 101	0.345 101	0.546 101	0.685 91	0.278 99

ScanNet	0.306 103	0.203 103	0.366 102	0.501 93	0.311 100	0.524 101	0.211 101	0.002 104	0.342 98	0.189 102	0.786 100	0.145 103	0.102 94	0.245 96	0.152 101	0.318 101	0.348 102	0.300 102	0.460 102	0.437 103	0.182 103
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 104	0.000 104	0.041 104	0.172 104	0.030 104	0.062 104	0.001 104	0.035 103	0.004 104	0.051 104	0.143 104	0.019 104	0.003 102	0.041 102	0.050 103	0.003 104	0.054 104	0.018 104	0.005 104	0.264 104	0.082 104

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 12	0.877 9	0.783 14	0.970 1	0.889 1	0.828 12	0.945 3	0.803 6	0.713 10	0.720 10	0.709 8	1.000 1	0.936 6	0.934 3	0.873 7	1.000 1	0.791 5

Queryformer	0.874 2	1.000 1	0.978 10	0.809 24	0.876 1	0.936 5	0.702 8	0.716 26	0.920 5	0.875 3	0.766 4	0.772 2	0.818 4	1.000 1	0.995 1	0.916 4	0.892 1	1.000 1	0.767 8

SoftGroup++	0.874 2	1.000 1	0.972 13	0.947 1	0.839 5	0.898 12	0.556 23	0.913 2	0.881 11	0.756 8	0.828 2	0.748 6	0.821 2	1.000 1	0.937 5	0.937 1	0.887 2	1.000 1	0.821 2

Mask3D	0.870 4	1.000 1	0.985 6	0.782 32	0.818 8	0.938 4	0.760 4	0.749 23	0.923 4	0.877 2	0.760 5	0.785 1	0.820 3	1.000 1	0.912 9	0.864 23	0.878 5	0.983 39	0.825 1
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 14	0.860 12	0.860 2	0.913 8	0.558 21	0.899 3	0.911 6	0.760 7	0.828 1	0.736 7	0.802 6	0.981 29	0.919 8	0.875 14	0.877 6	1.000 1	0.820 3
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
IPCA-Inst	0.851 6	1.000 1	0.968 15	0.884 8	0.842 4	0.862 24	0.693 10	0.812 17	0.888 9	0.677 20	0.783 3	0.698 11	0.807 5	1.000 1	0.911 13	0.865 22	0.865 9	1.000 1	0.757 10

SPFormer	0.851 6	1.000 1	0.994 2	0.806 25	0.774 16	0.942 3	0.637 13	0.849 10	0.859 14	0.889 1	0.720 8	0.730 8	0.665 13	1.000 1	0.911 13	0.868 21	0.873 8	1.000 1	0.796 4
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
ISBNet	0.845 8	1.000 1	0.976 11	0.798 26	0.794 11	0.916 6	0.757 5	0.667 33	0.882 10	0.842 4	0.715 9	0.757 4	0.832 1	1.000 1	0.905 16	0.803 43	0.843 12	1.000 1	0.715 17
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 9	1.000 1	0.963 18	0.891 6	0.794 10	0.954 2	0.822 3	0.710 27	0.961 2	0.721 12	0.693 16	0.530 31	0.653 15	1.000 1	0.867 23	0.857 26	0.859 10	0.991 36	0.771 7

TopoSeg	0.832 10	1.000 1	0.981 8	0.933 2	0.819 7	0.826 32	0.524 29	0.841 11	0.811 19	0.681 19	0.759 6	0.687 12	0.727 7	0.981 29	0.911 13	0.883 10	0.853 11	1.000 1	0.756 11

GraphCut	0.832 10	1.000 1	0.922 32	0.724 41	0.798 9	0.902 11	0.701 9	0.856 8	0.859 13	0.715 13	0.706 11	0.748 5	0.640 26	1.000 1	0.934 7	0.862 24	0.880 3	1.000 1	0.729 13

PBNet	0.825 12	1.000 1	0.963 17	0.837 16	0.843 3	0.865 19	0.822 2	0.647 35	0.878 12	0.733 10	0.639 24	0.683 13	0.650 16	1.000 1	0.853 24	0.870 18	0.820 13	1.000 1	0.744 12

SSEC	0.820 13	1.000 1	0.983 7	0.924 3	0.826 6	0.817 35	0.415 38	0.899 4	0.793 23	0.673 21	0.731 7	0.636 18	0.653 14	1.000 1	0.939 4	0.804 41	0.878 4	1.000 1	0.780 6

DKNet	0.815 14	1.000 1	0.930 24	0.844 14	0.765 20	0.915 7	0.534 27	0.805 19	0.805 21	0.807 5	0.654 18	0.763 3	0.650 16	1.000 1	0.794 36	0.881 11	0.766 17	1.000 1	0.758 9
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN	0.806 15	1.000 1	0.992 4	0.789 28	0.723 32	0.891 13	0.650 12	0.810 18	0.832 16	0.665 23	0.699 14	0.658 14	0.700 9	1.000 1	0.881 18	0.832 33	0.774 15	0.997 29	0.613 34
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
HAIS	0.803 16	1.000 1	0.994 2	0.820 20	0.759 21	0.855 25	0.554 24	0.882 5	0.827 18	0.615 29	0.676 17	0.638 17	0.646 24	1.000 1	0.912 9	0.797 45	0.767 16	0.994 34	0.726 14
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Box2Mask	0.803 16	1.000 1	0.962 19	0.874 10	0.707 35	0.887 16	0.686 11	0.598 39	0.961 1	0.715 14	0.694 15	0.469 36	0.700 9	1.000 1	0.912 9	0.902 5	0.753 22	0.997 29	0.637 28
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 18	1.000 1	0.968 16	0.812 21	0.766 19	0.864 20	0.460 32	0.815 16	0.888 8	0.598 32	0.651 21	0.639 16	0.600 30	0.918 33	0.941 2	0.896 6	0.721 29	1.000 1	0.723 15
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 19	1.000 1	0.996 1	0.829 19	0.767 18	0.889 15	0.600 16	0.819 15	0.770 28	0.594 33	0.620 27	0.541 28	0.700 9	1.000 1	0.941 2	0.889 8	0.763 18	1.000 1	0.526 43

SSTNet	0.789 20	1.000 1	0.840 46	0.888 7	0.717 33	0.835 28	0.717 7	0.684 32	0.627 42	0.724 11	0.652 20	0.727 9	0.600 30	1.000 1	0.912 9	0.822 36	0.757 21	1.000 1	0.691 22
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN	0.788 21	1.000 1	0.978 9	0.867 11	0.781 15	0.833 29	0.527 28	0.824 13	0.806 20	0.549 41	0.596 30	0.551 24	0.700 9	1.000 1	0.853 24	0.935 2	0.733 26	1.000 1	0.651 25

DENet	0.786 22	1.000 1	0.929 25	0.736 39	0.750 27	0.720 47	0.755 6	0.934 1	0.794 22	0.590 34	0.561 36	0.537 29	0.650 16	1.000 1	0.882 17	0.804 42	0.789 14	1.000 1	0.719 16

DualGroup	0.782 23	1.000 1	0.927 26	0.811 22	0.772 17	0.853 26	0.631 15	0.805 19	0.773 25	0.613 30	0.611 28	0.610 20	0.650 16	0.835 44	0.881 18	0.879 13	0.750 24	1.000 1	0.675 23

PointGroup	0.778 24	1.000 1	0.900 36	0.798 27	0.715 34	0.863 21	0.493 30	0.706 28	0.895 7	0.569 39	0.701 12	0.576 22	0.639 27	1.000 1	0.880 20	0.851 28	0.719 30	0.997 29	0.709 19
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 25	1.000 1	0.900 37	0.860 12	0.728 31	0.869 17	0.400 39	0.857 7	0.774 24	0.568 40	0.701 13	0.602 21	0.646 24	0.933 32	0.843 27	0.890 7	0.691 37	0.997 29	0.709 18
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
AOIA	0.767 26	1.000 1	0.937 21	0.810 23	0.740 29	0.906 9	0.550 25	0.800 21	0.706 34	0.577 38	0.624 26	0.544 27	0.596 35	0.857 36	0.879 22	0.880 12	0.750 23	0.992 35	0.658 24

DD-UNet+Group	0.764 27	1.000 1	0.897 39	0.837 15	0.753 24	0.830 31	0.459 34	0.824 13	0.699 36	0.629 27	0.653 19	0.438 39	0.650 16	1.000 1	0.880 20	0.858 25	0.690 38	1.000 1	0.650 26
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 28	1.000 1	0.923 29	0.765 35	0.785 13	0.905 10	0.600 16	0.655 34	0.646 41	0.683 18	0.647 22	0.530 30	0.650 16	1.000 1	0.824 29	0.830 34	0.693 36	0.944 43	0.644 27

Dyco3D	0.761 29	1.000 1	0.935 22	0.893 5	0.752 26	0.863 22	0.600 16	0.588 40	0.742 31	0.641 25	0.633 25	0.546 26	0.550 37	0.857 36	0.789 38	0.853 27	0.762 19	0.987 37	0.699 20
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance	0.742 30	1.000 1	0.923 29	0.785 29	0.745 28	0.867 18	0.557 22	0.578 43	0.729 32	0.670 22	0.644 23	0.488 34	0.577 36	1.000 1	0.794 36	0.830 34	0.620 45	1.000 1	0.550 39
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg	0.739 31	1.000 1	0.899 38	0.759 37	0.753 25	0.823 33	0.282 43	0.691 31	0.658 39	0.582 37	0.594 31	0.547 25	0.628 28	1.000 1	0.795 35	0.868 20	0.728 28	1.000 1	0.692 21

3D-MPA	0.737 32	1.000 1	0.933 23	0.785 29	0.794 12	0.831 30	0.279 45	0.588 40	0.695 37	0.616 28	0.559 37	0.556 23	0.650 16	1.000 1	0.809 33	0.875 15	0.696 34	1.000 1	0.608 36
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 33	1.000 1	0.992 4	0.779 34	0.609 44	0.746 42	0.308 42	0.867 6	0.601 45	0.607 31	0.539 40	0.519 32	0.550 37	1.000 1	0.824 29	0.869 19	0.729 27	1.000 1	0.616 32
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
OSIS	0.725 34	1.000 1	0.885 42	0.653 47	0.657 41	0.801 36	0.576 20	0.695 30	0.828 17	0.698 16	0.534 41	0.457 38	0.500 44	0.857 36	0.831 28	0.841 31	0.627 44	1.000 1	0.619 31

SSEN	0.724 35	1.000 1	0.926 27	0.781 33	0.661 39	0.845 27	0.596 19	0.529 45	0.764 30	0.653 24	0.489 46	0.461 37	0.500 44	0.859 35	0.765 39	0.872 17	0.761 20	1.000 1	0.577 37
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 36	1.000 1	0.945 20	0.901 4	0.754 23	0.817 34	0.460 32	0.700 29	0.772 26	0.688 17	0.568 35	0.000 56	0.500 44	0.981 29	0.606 47	0.872 16	0.740 25	1.000 1	0.614 33
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 37	1.000 1	0.926 28	0.694 42	0.699 37	0.890 14	0.636 14	0.516 46	0.693 38	0.743 9	0.588 32	0.369 42	0.601 29	0.594 49	0.800 34	0.886 9	0.676 39	0.986 38	0.546 40

SALoss-ResNet	0.695 38	1.000 1	0.855 44	0.579 51	0.589 46	0.735 45	0.484 31	0.588 40	0.856 15	0.634 26	0.571 34	0.298 43	0.500 44	1.000 1	0.824 29	0.818 37	0.702 33	0.935 47	0.545 41
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 39	1.000 1	0.852 45	0.655 46	0.616 43	0.788 37	0.334 41	0.763 22	0.771 27	0.457 51	0.555 38	0.652 15	0.518 41	0.857 36	0.765 39	0.732 51	0.631 42	0.944 43	0.577 38
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 40	1.000 1	0.913 33	0.730 40	0.737 30	0.743 44	0.442 35	0.855 9	0.655 40	0.546 42	0.546 39	0.263 45	0.508 43	0.889 34	0.568 48	0.771 48	0.705 32	0.889 50	0.625 30

3D-BoNet	0.687 41	1.000 1	0.887 41	0.836 17	0.587 47	0.643 54	0.550 25	0.620 36	0.724 33	0.522 46	0.501 44	0.243 46	0.512 42	1.000 1	0.751 41	0.807 40	0.661 41	0.909 49	0.612 35
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
PCJC	0.684 42	1.000 1	0.895 40	0.757 38	0.659 40	0.862 23	0.189 52	0.739 24	0.606 44	0.712 15	0.581 33	0.515 33	0.650 16	0.857 36	0.357 53	0.785 46	0.631 43	0.889 50	0.635 29

SPG_WSIS	0.678 43	1.000 1	0.880 43	0.836 17	0.701 36	0.727 46	0.273 47	0.607 38	0.706 35	0.541 44	0.515 43	0.174 48	0.600 30	0.857 36	0.716 42	0.846 30	0.711 31	1.000 1	0.506 44

One_Thing_One_Click	0.675 44	1.000 1	0.823 47	0.782 31	0.621 42	0.766 39	0.211 49	0.736 25	0.560 48	0.586 35	0.522 42	0.636 19	0.453 48	0.641 48	0.853 24	0.850 29	0.694 35	0.997 29	0.411 48
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 45	1.000 1	0.923 31	0.593 50	0.561 48	0.746 43	0.143 54	0.504 47	0.766 29	0.485 49	0.442 47	0.372 41	0.530 40	0.714 45	0.815 32	0.775 47	0.673 40	1.000 1	0.431 47
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 46	0.711 52	0.802 48	0.540 52	0.757 22	0.777 38	0.029 55	0.577 44	0.588 47	0.521 47	0.600 29	0.436 40	0.534 39	0.697 46	0.616 46	0.838 32	0.526 47	0.980 40	0.534 42
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone	0.605 47	1.000 1	0.909 34	0.764 36	0.603 45	0.704 48	0.415 37	0.301 52	0.548 49	0.461 50	0.394 48	0.267 44	0.386 50	0.857 36	0.649 45	0.817 38	0.504 48	0.959 41	0.356 51

3D-SIS	0.558 48	1.000 1	0.773 49	0.614 49	0.503 50	0.691 50	0.200 50	0.412 48	0.498 52	0.546 43	0.311 53	0.103 52	0.600 30	0.857 36	0.382 50	0.799 44	0.445 54	0.938 46	0.371 49
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet	0.544 49	0.500 55	0.655 55	0.661 45	0.663 38	0.765 40	0.432 36	0.214 54	0.612 43	0.584 36	0.499 45	0.204 47	0.286 54	0.429 52	0.655 44	0.650 56	0.539 46	0.950 42	0.499 45

Hier3D	0.540 50	1.000 1	0.727 50	0.626 48	0.467 53	0.693 49	0.200 50	0.412 48	0.480 53	0.528 45	0.318 52	0.077 55	0.600 30	0.688 47	0.382 50	0.768 49	0.472 50	0.941 45	0.350 52
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class	0.497 51	0.250 57	0.902 35	0.689 43	0.540 49	0.747 41	0.276 46	0.610 37	0.268 56	0.489 48	0.348 49	0.000 56	0.243 56	0.220 55	0.663 43	0.814 39	0.459 52	0.928 48	0.496 46

tmp	0.474 52	1.000 1	0.727 50	0.433 55	0.481 52	0.673 52	0.022 57	0.380 50	0.517 51	0.436 53	0.338 51	0.128 50	0.343 52	0.429 52	0.291 55	0.728 52	0.473 49	0.833 53	0.300 54

SemRegionNet-20cls	0.470 53	1.000 1	0.727 50	0.447 54	0.481 51	0.678 51	0.024 56	0.380 50	0.518 50	0.440 52	0.339 50	0.128 50	0.350 51	0.429 52	0.212 56	0.711 53	0.465 51	0.833 53	0.290 55

ASIS	0.422 54	0.333 56	0.707 53	0.676 44	0.401 54	0.650 53	0.350 40	0.177 55	0.594 46	0.376 54	0.202 54	0.077 54	0.404 49	0.571 50	0.197 57	0.674 55	0.447 53	0.500 56	0.260 56

3D-BEVIS	0.401 55	0.667 53	0.687 54	0.419 56	0.137 57	0.587 55	0.188 53	0.235 53	0.359 55	0.211 56	0.093 57	0.080 53	0.311 53	0.571 50	0.382 50	0.754 50	0.300 56	0.874 52	0.357 50
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet	0.390 56	0.556 54	0.636 56	0.493 53	0.353 55	0.539 56	0.271 48	0.160 56	0.450 54	0.359 55	0.178 55	0.146 49	0.250 55	0.143 56	0.347 54	0.698 54	0.436 55	0.667 55	0.331 53

MaskRCNN 2d->3d Proj	0.261 57	0.903 51	0.081 57	0.008 57	0.233 56	0.175 57	0.280 44	0.106 57	0.150 57	0.203 57	0.175 56	0.480 35	0.218 57	0.143 56	0.542 49	0.404 57	0.153 57	0.393 57	0.049 57

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