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

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
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
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.
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
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

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

Method	avg	chair	table	door	couch	cabinet	shelf	desk	office chair	bed	pillow	sink	picture	window	toilet	bookshelf	monitor	curtain	book	armchair	coffee table	box	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	ottoman	bench	board	mirror	copier	basket	sofa chair	file cabinet	fan	laptop	shower	paper	person	paper towel dispenser	oven	blinds	rack	plate	blackboard	piano	suitcase	rail	radiator	recycling bin	wardrobe	soap dispenser	bucket	clock	stand	light	laundry basket	pipe	clothes dryer	guitar	toilet paper holder	seat	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	fireplace	soap dish	kitchen counter	mini fridge	fire extinguisher	ball	hat	shower curtain rod	water cooler	paper cutter	tray	shower door	pillar	ledge	toilet seat cover dispenser	cart	tissue box	light switch	crate	power outlet	decoration	projector	closet door	vacuum cleaner	candle	stuffed animal	headphones	dish rack	broom	guitar case	range hood	dustpan	water bottle	vent	mailbox	bowl	paper bag	alarm clock	music stand	projector screen	laundry detergent	bathroom counter	bathroom vanity	laundry hamper	bathroom stall door	trash bin	dumbbell	stair rail	tube	bathroom cabinet	cd case	closet rod	coffee kettle	shower head	keyboard piano	case of water bottles	coat rack	storage organizer	folded chair	fire alarm	power strip	calendar	poster	potted plant	luggage	mattress

TD3D Scannet200	0.320 2	0.501 2	0.264 2	0.164 2	0.841 1	0.679 1	0.716 2	0.879 2	0.280 3	0.192 1	0.634 1	0.231 1	0.733 3	0.459 2	0.565 3	0.498 5	0.560 2	1.000 1	0.686 1	0.890 2	0.708 1	0.123 4	0.820 1	0.152 2	0.967 1	0.456 1	0.458 2	0.387 2	0.194 1	0.435 5	0.906 1	0.077 1	0.396 2	0.509 1	0.217 2	0.715 1	0.619 2	1.000 1	0.099 2	0.792 1	0.513 2	0.062 2	0.506 3	0.549 1	0.605 1	1.000 1	0.123 4	0.106 1	1.000 1	0.744 4	0.000 2	1.000 1	0.504 5	0.525 2	0.185 2	0.790 4	0.101 2	0.008 2	0.587 2	0.356 1	0.817 1	0.083 5	1.000 1	0.000 1	0.621 1	0.842 1	0.415 1	0.268 4	0.083 4	0.000 2	0.098 3	0.881 1	0.125 2	0.000 2	0.000 1	0.000 2	0.000 3	0.125 4	0.332 3	0.448 5	0.202 2	0.196 1	0.798 1	0.264 2	0.000 2	0.000 1	0.017 2	0.233 2	0.000 1	0.063 1	0.333 2	0.038 1	0.111 1	0.250 3	0.000 2	0.516 1	0.208 1	0.470 2	0.094 3	0.218 1	0.000 1	0.667 2	0.033 5	0.000 2	0.000 1	0.400 1	0.156 2	0.000 1	0.267 1	0.226 1	0.000 1	0.104 2	0.159 2	0.299 5	0.095 3	0.458 1	0.500 1	0.000 1	1.000 1	0.472 1	0.792 3	0.000 1	0.022 1	0.061 2	0.250 1	0.008 1	0.250 2	0.333 2	0.143 2	0.396 2	0.049 2	0.012 1	0.000 1	0.283 4	0.000 1	0.241 4	0.000 1	0.101 2	0.331 4	0.000 1	0.629 3	0.000 1	0.000 1	0.857 2	0.222 3	0.677 1	0.000 1	0.003 2	0.000 1	0.000 2	0.000 1	0.076 2	0.252 3	0.400 1	0.431 2	0.061 3	0.328 3	0.331 4	0.500 1	0.000 2	0.000 1	0.167 1	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	1.000 1	0.542 1	0.000 2	0.063 1	0.000 2	0.000 1

Mask3D Scannet200	0.388 1	0.542 1	0.357 1	0.237 1	0.808 2	0.676 2	0.741 1	0.832 4	0.496 1	0.151 3	0.628 2	0.021 2	0.955 1	0.578 1	0.753 1	0.612 1	0.591 1	0.822 5	0.609 3	0.926 1	0.614 3	0.291 1	0.725 4	0.163 1	0.890 2	0.380 5	0.615 1	0.517 1	0.130 3	0.806 1	0.857 2	0.024 2	0.511 1	0.412 5	0.226 1	0.597 2	0.756 1	1.000 1	0.111 1	0.792 1	0.736 1	0.091 1	0.610 1	0.527 2	0.323 4	1.000 1	0.504 1	0.063 2	1.000 1	0.853 1	0.010 1	0.974 3	0.839 1	0.667 1	0.301 1	0.883 1	0.266 1	0.039 1	0.640 1	0.311 2	0.739 2	0.463 1	1.000 1	0.000 1	0.287 2	0.715 2	0.313 2	0.600 1	1.000 1	0.027 1	0.076 4	0.502 5	0.500 1	0.409 1	0.000 1	0.194 1	0.125 2	0.500 1	0.491 1	0.748 1	0.050 4	0.042 2	0.776 2	0.352 1	0.008 1	0.000 1	0.033 1	0.254 1	0.000 1	0.005 2	0.552 1	0.008 2	0.020 2	0.750 1	0.500 1	0.409 2	0.065 3	0.511 1	0.107 1	0.178 2	0.000 1	1.000 1	0.400 1	0.016 1	0.000 1	0.400 1	0.571 1	0.000 1	0.060 2	0.044 2	0.000 1	0.514 1	0.278 1	1.000 1	0.258 1	0.017 3	0.125 5	0.000 1	0.792 3	0.399 3	1.000 1	0.000 1	0.013 2	0.265 1	0.018 2	0.000 2	1.000 1	0.335 1	0.381 1	0.500 1	0.250 1	0.004 2	0.000 1	0.727 1	0.000 1	0.497 1	0.000 1	0.188 1	0.677 2	0.000 1	0.708 2	0.000 1	0.000 1	0.945 1	0.391 1	0.123 4	0.000 1	0.028 1	0.000 1	1.000 1	0.000 1	0.099 1	0.451 1	0.400 1	0.668 1	0.573 1	0.606 1	0.077 5	0.003 4	0.004 1	0.000 1	0.042 3	0.000 1	0.000 1	1.000 1	1.000 1	0.000 1	0.042 1	0.000 2	0.200 2	0.302 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.	0.203 5	0.369 4	0.134 5	0.078 5	0.706 4	0.382 4	0.693 3	0.845 3	0.221 5	0.150 4	0.158 4	0.000 3	0.746 2	0.369 4	0.545 4	0.595 2	0.387 4	0.997 3	0.413 5	0.720 5	0.636 2	0.165 3	0.732 3	0.070 4	0.851 4	0.402 4	0.251 4	0.313 4	0.123 4	0.583 4	0.696 3	0.000 3	0.051 5	0.500 2	0.000 3	0.500 4	0.372 5	0.667 4	0.009 4	0.000 3	0.307 5	0.003 4	0.479 4	0.107 5	0.226 5	0.903 4	0.109 5	0.031 3	0.981 3	0.726 5	0.000 2	0.522 5	0.669 2	0.282 5	0.052 5	0.778 5	0.000 4	0.000 3	0.400 3	0.074 4	0.333 4	0.218 4	1.000 1	0.000 1	0.250 3	0.406 5	0.118 5	0.317 2	0.100 3	0.000 2	0.191 1	0.596 2	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.500 1	0.178 5	0.701 2	0.000 5	0.000 3	0.522 5	0.018 5	0.000 2	0.000 1	0.000 3	0.060 4	0.000 1	0.000 3	0.033 5	0.000 3	0.000 3	0.000 4	0.000 2	0.281 3	0.100 2	0.000 5	0.090 4	0.133 4	0.000 1	0.422 5	0.050 4	0.000 2	0.000 1	0.200 3	0.000 5	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.123 4	0.677 2	0.021 4	0.000 4	0.500 1	0.000 1	0.500 4	0.442 2	0.125 5	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.056 4	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.200 5	0.000 1	0.143 5	0.000 1	0.000 3	0.250 5	0.000 1	0.511 4	0.000 1	0.000 1	0.286 3	0.083 4	0.396 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.025 4	0.300 2	0.000 3	0.371 3	0.070 2	0.000 4	0.385 3	0.000 5	0.000 2	0.000 1	0.000 5	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.200 2	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.	0.209 4	0.361 5	0.157 4	0.085 4	0.700 5	0.248 5	0.634 5	0.776 5	0.322 2	0.135 5	0.103 5	0.000 3	0.524 5	0.364 5	0.618 2	0.592 3	0.381 5	0.997 3	0.589 4	0.747 4	0.340 5	0.109 5	0.768 2	0.059 5	0.702 5	0.448 2	0.188 5	0.149 5	0.091 5	0.636 3	0.573 5	0.000 3	0.246 3	0.500 2	0.000 3	0.450 5	0.405 3	0.667 4	0.006 5	0.000 3	0.356 4	0.007 3	0.506 2	0.420 3	0.340 3	0.667 5	0.294 2	0.004 4	0.571 4	0.748 2	0.000 2	1.000 1	0.573 4	0.502 4	0.094 4	0.807 3	0.000 4	0.000 3	0.400 3	0.000 5	0.278 5	0.228 3	1.000 1	0.000 1	0.115 5	0.432 4	0.198 3	0.050 5	0.125 2	0.000 2	0.000 5	0.573 3	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.125 4	0.312 4	0.610 3	0.221 1	0.000 3	0.667 4	0.050 4	0.000 2	0.000 1	0.000 3	0.032 5	0.000 1	0.000 3	0.083 3	0.000 3	0.000 3	0.000 4	0.000 2	0.220 4	0.000 5	0.125 3	0.000 5	0.111 5	0.000 1	0.667 2	0.200 3	0.000 2	0.000 1	0.000 4	0.110 3	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.053 5	0.500 4	0.000 5	0.000 4	0.500 1	0.000 1	0.500 4	0.333 4	0.500 4	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.000 5	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.600 2	0.000 1	0.364 2	0.000 1	0.000 3	0.750 1	0.000 1	0.833 1	0.000 1	0.000 1	0.143 5	0.000 5	0.396 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.021 5	0.221 4	0.000 3	0.093 5	0.055 4	0.451 2	0.677 2	0.125 2	0.000 2	0.000 1	0.028 4	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.050 4	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.	0.246 3	0.413 3	0.170 3	0.130 3	0.754 3	0.541 3	0.682 4	0.903 1	0.264 4	0.164 2	0.234 3	0.000 3	0.681 4	0.452 3	0.464 5	0.541 4	0.399 3	1.000 1	0.637 2	0.772 3	0.588 4	0.190 2	0.589 5	0.081 3	0.857 3	0.426 3	0.373 3	0.318 3	0.135 2	0.690 2	0.653 4	0.000 3	0.159 4	0.500 2	0.000 3	0.581 3	0.387 4	1.000 1	0.046 3	0.000 3	0.402 3	0.003 5	0.455 5	0.196 4	0.571 2	1.000 1	0.270 3	0.003 5	0.530 5	0.748 3	0.000 2	0.744 4	0.575 3	0.511 3	0.112 3	0.815 2	0.067 3	0.000 3	0.400 3	0.167 3	0.667 3	0.241 2	1.000 1	0.000 1	0.208 4	0.660 3	0.125 4	0.317 2	0.000 5	0.000 2	0.100 2	0.561 4	0.000 3	0.000 2	0.000 1	0.000 2	1.000 1	0.500 1	0.344 2	0.568 4	0.167 3	0.000 3	0.706 3	0.068 3	0.000 2	0.000 1	0.000 3	0.063 3	0.000 1	0.000 3	0.056 4	0.000 3	0.000 3	0.500 2	0.000 2	0.143 5	0.017 4	0.125 3	0.097 2	0.164 3	0.000 1	0.582 4	0.400 1	0.000 2	0.000 1	0.000 4	0.083 4	0.000 1	0.000 3	0.000 3	0.000 1	0.025 3	0.156 3	0.533 3	0.250 2	0.200 2	0.500 1	0.000 1	1.000 1	0.333 4	1.000 1	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.333 2	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.400 3	0.000 1	0.364 2	0.000 1	0.000 3	0.500 3	0.000 1	0.511 4	0.000 1	0.000 1	0.286 3	0.333 2	0.000 5	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.034 3	0.111 5	0.000 3	0.333 4	0.031 5	0.000 4	0.750 1	0.125 2	0.000 2	0.000 1	0.151 2	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.000 5	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 50%	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

Queryformer	0.787 1	1.000 1	0.933 1	0.601 35	0.754 1	0.886 5	0.558 2	0.661 26	0.767 3	0.665 5	0.716 3	0.639 12	0.808 3	1.000 1	0.844 1	0.897 2	0.804 2	1.000 1	0.624 3

MAFT	0.786 2	1.000 1	0.894 5	0.807 13	0.694 6	0.893 3	0.486 5	0.674 22	0.740 5	0.786 1	0.704 5	0.727 1	0.739 7	1.000 1	0.707 10	0.849 5	0.756 11	1.000 1	0.685 1

Mask3D	0.780 3	1.000 1	0.786 28	0.716 26	0.696 5	0.885 6	0.500 4	0.714 18	0.810 2	0.672 4	0.715 4	0.679 8	0.809 2	1.000 1	0.831 2	0.833 9	0.787 4	1.000 1	0.602 7
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormer	0.770 4	0.903 41	0.903 2	0.806 14	0.609 18	0.886 4	0.568 1	0.815 6	0.705 8	0.711 2	0.655 7	0.652 11	0.685 12	1.000 1	0.789 4	0.809 15	0.776 7	1.000 1	0.583 12
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++	0.769 5	1.000 1	0.803 21	0.937 1	0.684 7	0.865 8	0.213 21	0.870 2	0.664 10	0.571 11	0.758 1	0.702 5	0.807 4	1.000 1	0.653 17	0.902 1	0.792 3	1.000 1	0.626 2

ISBNet	0.763 6	1.000 1	0.873 6	0.717 25	0.666 10	0.858 12	0.508 3	0.667 24	0.764 4	0.643 6	0.676 6	0.688 7	0.825 1	1.000 1	0.773 5	0.741 28	0.777 6	1.000 1	0.556 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
SoftGroup	0.761 7	1.000 1	0.808 18	0.845 8	0.716 2	0.862 10	0.243 18	0.824 4	0.655 12	0.620 7	0.734 2	0.699 6	0.791 6	0.981 26	0.716 8	0.844 6	0.769 8	1.000 1	0.594 10
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
TD3D	0.751 8	1.000 1	0.774 29	0.867 7	0.621 14	0.934 1	0.404 8	0.706 19	0.812 1	0.605 9	0.633 12	0.626 13	0.690 11	1.000 1	0.640 19	0.820 12	0.777 5	1.000 1	0.612 5

PBNet	0.747 9	1.000 1	0.818 14	0.837 10	0.713 3	0.844 13	0.457 7	0.647 29	0.711 7	0.614 8	0.617 14	0.657 10	0.650 14	1.000 1	0.692 11	0.822 11	0.765 10	1.000 1	0.595 9
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut	0.732 10	1.000 1	0.788 26	0.724 24	0.642 12	0.859 11	0.248 17	0.787 11	0.618 15	0.596 10	0.653 9	0.722 3	0.583 32	1.000 1	0.766 6	0.861 3	0.825 1	1.000 1	0.504 24

IPCA-Inst	0.731 11	1.000 1	0.788 27	0.884 6	0.698 4	0.788 28	0.252 16	0.760 13	0.646 13	0.511 19	0.637 11	0.665 9	0.804 5	1.000 1	0.644 18	0.778 18	0.747 13	1.000 1	0.561 16

TopoSeg	0.725 12	1.000 1	0.806 20	0.933 2	0.668 9	0.758 31	0.272 15	0.734 17	0.630 14	0.549 15	0.654 8	0.606 14	0.697 10	0.966 28	0.612 23	0.839 7	0.754 12	1.000 1	0.573 13

DKNet	0.718 13	1.000 1	0.814 15	0.782 17	0.619 15	0.872 7	0.224 19	0.751 15	0.569 19	0.677 3	0.585 17	0.724 2	0.633 24	0.981 26	0.515 33	0.819 13	0.736 14	1.000 1	0.617 4
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC	0.707 14	1.000 1	0.850 8	0.924 3	0.648 11	0.747 34	0.162 23	0.862 3	0.572 18	0.520 17	0.624 13	0.549 17	0.649 22	1.000 1	0.560 28	0.706 34	0.768 9	1.000 1	0.591 11

HAIS	0.699 15	1.000 1	0.849 9	0.820 11	0.675 8	0.808 22	0.279 13	0.757 14	0.465 24	0.517 18	0.596 15	0.559 16	0.600 26	1.000 1	0.654 16	0.767 20	0.676 18	0.994 36	0.560 17
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet	0.698 16	1.000 1	0.697 45	0.888 5	0.556 24	0.803 23	0.387 9	0.626 31	0.417 28	0.556 14	0.585 18	0.702 4	0.600 26	1.000 1	0.824 3	0.720 33	0.692 16	1.000 1	0.509 23
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup	0.694 17	1.000 1	0.799 23	0.811 12	0.622 13	0.817 17	0.376 10	0.805 9	0.590 17	0.487 22	0.568 21	0.525 21	0.650 14	0.835 40	0.600 24	0.829 10	0.655 20	1.000 1	0.526 20

SphereSeg	0.680 18	1.000 1	0.856 7	0.744 23	0.618 16	0.893 2	0.151 24	0.651 28	0.713 6	0.537 16	0.579 20	0.430 30	0.651 13	1.000 1	0.389 43	0.744 27	0.697 15	0.991 38	0.601 8

Box2Mask	0.677 19	1.000 1	0.847 10	0.771 19	0.509 33	0.816 18	0.277 14	0.558 38	0.482 21	0.562 13	0.640 10	0.448 26	0.700 8	1.000 1	0.666 12	0.852 4	0.578 32	0.997 31	0.488 28
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance	0.672 20	1.000 1	0.758 37	0.682 29	0.576 22	0.842 14	0.477 6	0.504 43	0.524 20	0.567 12	0.585 19	0.451 25	0.557 34	1.000 1	0.751 7	0.797 16	0.563 35	1.000 1	0.467 32
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group	0.664 21	1.000 1	0.822 13	0.764 22	0.616 17	0.815 19	0.139 28	0.694 21	0.597 16	0.459 26	0.566 22	0.599 15	0.600 26	0.516 50	0.715 9	0.819 14	0.635 24	1.000 1	0.603 6
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance	0.657 22	1.000 1	0.760 35	0.667 31	0.581 20	0.863 9	0.323 11	0.655 27	0.477 22	0.473 24	0.549 24	0.432 29	0.650 14	1.000 1	0.655 15	0.738 29	0.585 31	0.944 42	0.472 31

CSC-Pretrained	0.648 23	1.000 1	0.810 16	0.768 20	0.523 31	0.813 20	0.143 27	0.819 5	0.389 31	0.422 35	0.511 28	0.443 27	0.650 14	1.000 1	0.624 21	0.732 30	0.634 25	1.000 1	0.375 39

PE	0.645 24	1.000 1	0.773 31	0.798 16	0.538 26	0.786 29	0.088 36	0.799 10	0.350 35	0.435 33	0.547 25	0.545 18	0.646 23	0.933 30	0.562 27	0.761 23	0.556 40	0.997 31	0.501 26
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 25	1.000 1	0.758 36	0.582 41	0.539 25	0.826 16	0.046 40	0.765 12	0.372 33	0.436 32	0.588 16	0.539 20	0.650 14	1.000 1	0.577 25	0.750 25	0.653 22	0.997 31	0.495 27
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3D	0.641 26	1.000 1	0.841 11	0.893 4	0.531 28	0.802 24	0.115 33	0.588 36	0.448 25	0.438 30	0.537 27	0.430 31	0.550 35	0.857 32	0.534 31	0.764 22	0.657 19	0.987 39	0.568 14
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN	0.638 27	1.000 1	0.895 4	0.800 15	0.480 37	0.676 39	0.144 26	0.737 16	0.354 34	0.447 27	0.400 41	0.365 36	0.700 8	1.000 1	0.569 26	0.836 8	0.599 27	1.000 1	0.473 30

PointGroup	0.636 28	1.000 1	0.765 32	0.624 33	0.505 35	0.797 25	0.116 32	0.696 20	0.384 32	0.441 28	0.559 23	0.476 23	0.596 29	1.000 1	0.666 12	0.756 24	0.556 39	0.997 31	0.513 22
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group	0.635 29	0.667 42	0.797 25	0.714 27	0.562 23	0.774 30	0.146 25	0.810 8	0.429 27	0.476 23	0.546 26	0.399 33	0.633 24	1.000 1	0.632 20	0.722 32	0.609 26	1.000 1	0.514 21
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
DENet	0.629 30	1.000 1	0.797 24	0.608 34	0.589 19	0.627 43	0.219 20	0.882 1	0.310 37	0.402 40	0.383 43	0.396 34	0.650 14	1.000 1	0.663 14	0.543 51	0.691 17	1.000 1	0.568 15

3D-MPA	0.611 31	1.000 1	0.833 12	0.765 21	0.526 30	0.756 32	0.136 30	0.588 36	0.470 23	0.438 31	0.432 37	0.358 38	0.650 14	0.857 32	0.429 39	0.765 21	0.557 38	1.000 1	0.430 34
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS	0.605 32	1.000 1	0.801 22	0.599 36	0.535 27	0.728 36	0.286 12	0.436 47	0.679 9	0.491 20	0.433 35	0.256 40	0.404 47	0.857 32	0.620 22	0.724 31	0.510 45	1.000 1	0.539 19

AOIA	0.601 33	1.000 1	0.761 34	0.687 28	0.485 36	0.828 15	0.008 46	0.663 25	0.405 30	0.405 39	0.425 38	0.490 22	0.596 29	0.714 43	0.553 30	0.779 17	0.597 28	0.992 37	0.424 36

PCJC	0.578 34	1.000 1	0.810 17	0.583 40	0.449 40	0.813 21	0.042 41	0.603 34	0.341 36	0.490 21	0.465 32	0.410 32	0.650 14	0.835 40	0.264 49	0.694 38	0.561 36	0.889 47	0.504 25

SSEN	0.575 35	1.000 1	0.761 33	0.473 43	0.477 38	0.795 26	0.066 37	0.529 40	0.658 11	0.460 25	0.461 33	0.380 35	0.331 49	0.859 31	0.401 42	0.692 40	0.653 21	1.000 1	0.348 41
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg	0.567 36	0.528 52	0.708 44	0.626 32	0.580 21	0.745 35	0.063 38	0.627 30	0.240 41	0.400 41	0.497 29	0.464 24	0.515 36	1.000 1	0.475 35	0.745 26	0.571 33	1.000 1	0.429 35

NeuralBF	0.555 37	0.667 42	0.896 3	0.843 9	0.517 32	0.751 33	0.029 42	0.519 41	0.414 29	0.439 29	0.465 31	0.000 58	0.484 38	0.857 32	0.287 47	0.693 39	0.651 23	1.000 1	0.485 29
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML	0.549 38	1.000 1	0.807 19	0.588 39	0.327 45	0.647 41	0.004 48	0.815 7	0.180 44	0.418 36	0.364 45	0.182 43	0.445 41	1.000 1	0.442 38	0.688 41	0.571 34	1.000 1	0.396 37
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance	0.539 39	1.000 1	0.621 48	0.300 46	0.530 29	0.698 37	0.127 31	0.533 39	0.222 42	0.430 34	0.400 40	0.365 36	0.574 33	0.938 29	0.472 36	0.659 43	0.543 41	0.944 42	0.347 42

One_Thing_One_Click	0.529 40	0.667 42	0.718 40	0.777 18	0.399 41	0.683 38	0.000 51	0.669 23	0.138 47	0.391 42	0.374 44	0.539 19	0.360 48	0.641 47	0.556 29	0.774 19	0.593 29	0.997 31	0.251 47
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN	0.515 41	1.000 1	0.538 53	0.282 47	0.468 39	0.790 27	0.173 22	0.345 49	0.429 26	0.413 38	0.484 30	0.176 44	0.595 31	0.591 48	0.522 32	0.668 42	0.476 46	0.986 40	0.327 43

Occipital-SCS	0.512 42	1.000 1	0.716 41	0.509 42	0.506 34	0.611 44	0.092 35	0.602 35	0.177 45	0.346 45	0.383 42	0.165 45	0.442 42	0.850 39	0.386 44	0.618 47	0.543 42	0.889 47	0.389 38

3D-BoNet	0.488 43	1.000 1	0.672 47	0.590 38	0.301 47	0.484 54	0.098 34	0.620 32	0.306 38	0.341 46	0.259 49	0.125 47	0.434 44	0.796 42	0.402 41	0.499 53	0.513 44	0.909 46	0.439 33
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst	0.478 44	0.667 42	0.712 43	0.595 37	0.259 50	0.550 50	0.000 51	0.613 33	0.175 46	0.250 51	0.434 34	0.437 28	0.411 46	0.857 32	0.485 34	0.591 50	0.267 56	0.944 42	0.359 40
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS	0.470 45	0.667 42	0.685 46	0.677 30	0.372 43	0.562 48	0.000 51	0.482 44	0.244 40	0.316 48	0.298 46	0.052 53	0.442 43	0.857 32	0.267 48	0.702 35	0.559 37	1.000 1	0.287 45

SALoss-ResNet	0.459 46	1.000 1	0.737 39	0.159 57	0.259 49	0.587 46	0.138 29	0.475 45	0.217 43	0.416 37	0.408 39	0.128 46	0.315 50	0.714 43	0.411 40	0.536 52	0.590 30	0.873 50	0.304 44
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.447 47	0.528 52	0.555 51	0.381 44	0.382 42	0.633 42	0.002 49	0.509 42	0.260 39	0.361 44	0.432 36	0.327 39	0.451 40	0.571 49	0.367 45	0.639 45	0.386 47	0.980 41	0.276 46
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_ins	0.445 48	0.667 42	0.773 30	0.185 54	0.317 46	0.656 40	0.000 51	0.407 48	0.134 48	0.381 43	0.267 48	0.217 42	0.476 39	0.714 43	0.452 37	0.629 46	0.514 43	1.000 1	0.222 50
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SIS	0.382 49	1.000 1	0.432 55	0.245 49	0.190 51	0.577 47	0.013 45	0.263 51	0.033 54	0.320 47	0.240 50	0.075 49	0.422 45	0.857 32	0.117 53	0.699 36	0.271 55	0.883 49	0.235 49
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.323 50	0.667 42	0.542 52	0.264 48	0.157 54	0.550 49	0.000 51	0.205 54	0.009 55	0.270 50	0.218 51	0.075 49	0.500 37	0.688 46	0.007 59	0.698 37	0.301 52	0.459 56	0.200 51
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone	0.319 51	0.667 42	0.715 42	0.233 50	0.189 52	0.479 55	0.008 46	0.218 52	0.067 53	0.201 53	0.173 52	0.107 48	0.123 55	0.438 51	0.150 51	0.615 48	0.355 48	0.916 45	0.093 58

R-PointNet	0.306 52	0.500 54	0.405 56	0.311 45	0.348 44	0.589 45	0.054 39	0.068 57	0.126 49	0.283 49	0.290 47	0.028 54	0.219 53	0.214 54	0.331 46	0.396 57	0.275 53	0.821 52	0.245 48

Region-18class	0.284 53	0.250 58	0.751 38	0.228 52	0.270 48	0.521 51	0.000 51	0.468 46	0.008 57	0.205 52	0.127 53	0.000 58	0.068 57	0.070 57	0.262 50	0.652 44	0.323 50	0.740 53	0.173 52

SemRegionNet-20cls	0.250 54	0.333 55	0.613 49	0.229 51	0.163 53	0.493 52	0.000 51	0.304 50	0.107 50	0.147 55	0.100 54	0.052 52	0.231 51	0.119 55	0.039 55	0.445 55	0.325 49	0.654 54	0.141 54

tmp	0.248 55	0.667 42	0.437 54	0.188 53	0.153 55	0.491 53	0.000 51	0.208 53	0.094 52	0.153 54	0.099 55	0.057 51	0.217 54	0.119 55	0.039 55	0.466 54	0.302 51	0.640 55	0.140 55

3D-BEVIS	0.248 55	0.667 42	0.566 50	0.076 58	0.035 59	0.394 57	0.027 44	0.035 58	0.098 51	0.099 57	0.030 58	0.025 55	0.098 56	0.375 53	0.126 52	0.604 49	0.181 57	0.854 51	0.171 53
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
ASIS	0.199 57	0.333 55	0.253 58	0.167 56	0.140 56	0.438 56	0.000 51	0.177 55	0.008 56	0.121 56	0.069 56	0.004 57	0.231 52	0.429 52	0.036 57	0.445 56	0.273 54	0.333 58	0.119 57

Sgpn_scannet	0.143 58	0.208 59	0.390 57	0.169 55	0.065 57	0.275 58	0.029 43	0.069 56	0.000 58	0.087 58	0.043 57	0.014 56	0.027 59	0.000 58	0.112 54	0.351 58	0.168 58	0.438 57	0.138 56

MaskRCNN 2d->3d Proj	0.058 59	0.333 55	0.002 59	0.000 59	0.053 58	0.002 59	0.002 50	0.021 59	0.000 58	0.045 59	0.024 59	0.238 41	0.065 58	0.000 58	0.014 58	0.107 59	0.020 59	0.110 59	0.006 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

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 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

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