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

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
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.
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
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
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
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.

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

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

Mask3D Scannet200	0.278 1	0.383 1	0.263 1	0.168 1	0.661 2	0.465 1	0.572 1	0.665 3	0.391 1	0.121 4	0.304 1	0.015 2	0.647 1	0.349 1	0.474 1	0.489 1	0.321 1	0.816 5	0.351 3	0.722 1	0.402 4	0.195 1	0.515 3	0.082 1	0.795 1	0.215 2	0.396 1	0.377 1	0.082 4	0.724 1	0.586 1	0.015 2	0.277 1	0.377 5	0.201 1	0.475 2	0.572 1	0.778 3	0.089 1	0.759 1	0.556 1	0.068 1	0.506 1	0.467 1	0.323 3	0.778 2	0.427 1	0.027 2	0.789 1	0.744 1	0.003 1	0.570 2	0.561 1	0.337 1	0.265 1	0.711 1	0.258 1	0.031 1	0.569 1	0.311 1	0.441 1	0.179 1	1.000 1	0.000 1	0.233 2	0.411 2	0.283 2	0.380 1	0.667 1	0.016 1	0.048 3	0.418 2	0.139 1	0.173 1	0.000 1	0.086 1	0.014 2	0.500 1	0.384 1	0.497 1	0.044 3	0.032 2	0.752 1	0.287 1	0.003 1	0.000 1	0.007 1	0.208 1	0.000 1	0.001 2	0.349 1	0.008 2	0.014 2	0.509 1	0.500 1	0.323 1	0.023 2	0.176 1	0.107 1	0.105 3	0.000 1	0.605 1	0.378 1	0.016 1	0.000 1	0.400 1	0.192 1	0.000 1	0.048 2	0.037 2	0.000 1	0.275 1	0.119 1	0.810 1	0.258 1	0.006 3	0.083 5	0.000 1	0.568 2	0.377 2	0.708 1	0.000 1	0.005 2	0.147 1	0.014 2	0.000 2	0.556 1	0.085 1	0.325 1	0.500 1	0.083 1	0.004 2	0.000 1	0.590 1	0.000 1	0.365 1	0.000 1	0.116 1	0.491 1	0.000 1	0.626 1	0.000 1	0.000 1	0.579 1	0.391 1	0.050 4	0.000 1	0.028 1	0.000 1	0.222 1	0.000 1	0.063 1	0.302 1	0.356 1	0.149 4	0.573 1	0.415 1	0.013 5	0.002 4	0.004 1	0.000 1	0.005 4	0.000 1	0.000 1	0.444 1	0.514 1	0.000 1	0.028 1	0.000 2	0.156 2	0.267 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
LGround Inst.	0.154 3	0.275 3	0.108 3	0.060 3	0.573 3	0.381 3	0.434 3	0.654 4	0.190 4	0.141 2	0.097 3	0.000 3	0.503 3	0.180 3	0.252 3	0.242 4	0.242 3	0.881 3	0.448 1	0.494 3	0.429 3	0.078 2	0.364 5	0.024 3	0.654 2	0.213 4	0.222 3	0.239 3	0.099 3	0.616 2	0.363 3	0.000 3	0.092 3	0.444 3	0.000 3	0.383 4	0.209 5	0.815 2	0.030 3	0.000 3	0.166 3	0.002 4	0.295 5	0.099 4	0.364 2	0.778 2	0.177 3	0.001 4	0.427 5	0.585 4	0.000 2	0.470 3	0.268 5	0.205 3	0.045 3	0.642 2	0.007 3	0.000 3	0.333 5	0.148 3	0.407 3	0.130 2	1.000 1	0.000 1	0.156 4	0.189 3	0.097 4	0.169 2	0.000 5	0.000 2	0.056 2	0.400 3	0.000 3	0.000 2	0.000 1	0.000 2	0.556 1	0.278 3	0.203 3	0.323 4	0.019 4	0.000 3	0.402 4	0.026 3	0.000 2	0.000 1	0.000 3	0.044 3	0.000 1	0.000 3	0.037 4	0.000 3	0.000 3	0.181 2	0.000 2	0.127 3	0.006 4	0.028 4	0.023 3	0.115 2	0.000 1	0.327 2	0.267 2	0.000 2	0.000 1	0.000 4	0.028 3	0.000 1	0.000 3	0.000 3	0.000 1	0.003 3	0.048 2	0.135 4	0.222 2	0.089 2	0.278 1	0.000 1	0.514 3	0.333 4	0.611 2	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.037 3	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.322 2	0.000 1	0.209 2	0.000 1	0.000 3	0.278 2	0.000 1	0.302 3	0.000 1	0.000 1	0.143 3	0.148 3	0.000 5	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.015 3	0.064 5	0.000 3	0.272 2	0.031 5	0.000 4	0.257 2	0.028 2	0.000 2	0.000 1	0.041 2	0.000 1	0.000 1	0.000 2	0.222 5	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.
Minkowski 34D Inst.	0.130 4	0.246 4	0.083 4	0.043 5	0.547 5	0.236 4	0.415 4	0.672 2	0.141 5	0.133 3	0.067 4	0.000 3	0.521 2	0.114 5	0.238 4	0.289 2	0.232 4	0.883 2	0.182 5	0.373 5	0.486 1	0.076 3	0.488 4	0.022 4	0.529 4	0.199 5	0.110 4	0.217 4	0.100 2	0.460 4	0.319 4	0.000 3	0.025 5	0.472 1	0.000 3	0.394 3	0.210 4	0.537 4	0.004 4	0.000 3	0.083 5	0.000 5	0.299 4	0.061 5	0.201 5	0.761 4	0.084 4	0.008 3	0.720 3	0.557 5	0.000 2	0.317 5	0.280 3	0.094 5	0.020 5	0.564 5	0.000 4	0.000 3	0.400 3	0.048 4	0.259 4	0.101 3	1.000 1	0.000 1	0.190 3	0.142 5	0.094 5	0.137 3	0.089 3	0.000 2	0.101 1	0.355 5	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.444 2	0.082 5	0.384 2	0.000 5	0.000 3	0.334 5	0.004 5	0.000 2	0.000 1	0.000 3	0.041 4	0.000 1	0.000 3	0.026 5	0.000 3	0.000 3	0.000 4	0.000 2	0.082 5	0.022 3	0.000 5	0.021 4	0.088 4	0.000 1	0.241 4	0.033 4	0.000 2	0.000 1	0.067 3	0.000 5	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.026 4	0.262 2	0.016 4	0.000 4	0.278 1	0.000 1	0.500 4	0.394 1	0.028 5	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.019 4	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.156 5	0.000 1	0.032 5	0.000 1	0.000 3	0.194 5	0.000 1	0.248 4	0.000 1	0.000 1	0.099 4	0.019 4	0.308 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.007 4	0.122 2	0.000 3	0.175 3	0.063 2	0.000 4	0.271 1	0.000 5	0.000 2	0.000 1	0.000 5	0.000 1	0.000 1	0.000 2	0.278 2	0.000 1	0.000 2	0.000 2	0.111 3	0.000 2	0.000 2	0.000 2	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
TD3D Scannet200	0.211 2	0.332 2	0.177 2	0.103 2	0.662 1	0.413 2	0.463 2	0.705 1	0.192 3	0.145 1	0.266 2	0.215 1	0.452 4	0.209 2	0.222 5	0.219 5	0.315 2	0.893 1	0.380 2	0.617 2	0.439 2	0.047 4	0.646 1	0.080 2	0.610 3	0.253 1	0.237 2	0.293 2	0.135 1	0.379 5	0.494 2	0.048 1	0.252 2	0.451 2	0.184 2	0.483 1	0.395 2	0.852 1	0.083 2	0.551 2	0.278 2	0.036 2	0.337 2	0.266 2	0.544 1	0.963 1	0.079 5	0.039 1	0.740 2	0.604 2	0.000 2	0.586 1	0.283 2	0.282 2	0.059 2	0.633 3	0.028 2	0.004 2	0.559 2	0.309 2	0.420 2	0.028 5	1.000 1	0.000 1	0.456 1	0.411 1	0.372 1	0.060 4	0.046 4	0.000 2	0.040 4	0.694 1	0.083 2	0.000 2	0.000 1	0.000 2	0.000 3	0.083 4	0.252 2	0.260 5	0.200 1	0.160 1	0.669 2	0.111 2	0.000 2	0.000 1	0.006 2	0.169 2	0.000 1	0.007 1	0.296 2	0.032 1	0.074 1	0.139 3	0.000 2	0.321 2	0.031 1	0.108 2	0.088 2	0.157 1	0.000 1	0.231 5	0.026 5	0.000 2	0.000 1	0.356 2	0.052 2	0.000 1	0.240 1	0.147 1	0.000 1	0.015 2	0.046 3	0.144 3	0.073 3	0.414 1	0.222 4	0.000 1	0.806 1	0.343 3	0.486 3	0.000 1	0.008 1	0.038 2	0.083 1	0.002 1	0.028 2	0.074 2	0.032 2	0.150 2	0.039 2	0.008 1	0.000 1	0.250 4	0.000 1	0.125 4	0.000 1	0.052 2	0.260 3	0.000 1	0.143 5	0.000 1	0.000 1	0.543 2	0.207 2	0.404 1	0.000 1	0.003 2	0.000 1	0.000 2	0.000 1	0.037 2	0.093 4	0.272 2	0.342 1	0.039 4	0.281 2	0.249 3	0.224 1	0.000 2	0.000 1	0.074 1	0.000 1	0.000 1	0.000 2	0.278 2	0.000 1	0.000 2	0.889 1	0.323 1	0.000 2	0.014 1	0.000 2	0.000 1

CSC-Pretrain Inst.	0.123 5	0.223 5	0.082 5	0.046 4	0.564 4	0.152 5	0.394 5	0.578 5	0.235 2	0.116 5	0.034 5	0.000 3	0.348 5	0.119 4	0.297 2	0.285 3	0.202 5	0.838 4	0.323 4	0.407 4	0.184 5	0.037 5	0.516 2	0.013 5	0.424 5	0.214 3	0.093 5	0.105 5	0.078 5	0.542 3	0.250 5	0.000 3	0.064 4	0.444 3	0.000 3	0.224 5	0.231 3	0.537 4	0.001 5	0.000 3	0.126 4	0.004 3	0.308 3	0.193 3	0.244 4	0.343 5	0.228 2	0.000 5	0.441 4	0.588 3	0.000 2	0.338 4	0.275 4	0.189 4	0.030 4	0.600 4	0.000 4	0.000 3	0.378 4	0.000 5	0.108 5	0.098 4	1.000 1	0.000 1	0.096 5	0.172 4	0.144 3	0.011 5	0.125 2	0.000 2	0.000 5	0.376 4	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.042 5	0.141 4	0.377 3	0.051 2	0.000 3	0.483 3	0.017 4	0.000 2	0.000 1	0.000 3	0.022 5	0.000 1	0.000 3	0.065 3	0.000 3	0.000 3	0.000 4	0.000 2	0.094 4	0.000 5	0.042 3	0.000 5	0.064 5	0.000 1	0.259 3	0.089 3	0.000 2	0.000 1	0.000 4	0.022 4	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.018 5	0.111 5	0.000 5	0.000 4	0.278 1	0.000 1	0.444 5	0.333 4	0.333 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	0.267 3	0.000 1	0.184 3	0.000 1	0.000 3	0.211 4	0.000 1	0.378 2	0.000 1	0.000 1	0.063 5	0.000 5	0.275 3	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.007 5	0.105 3	0.000 3	0.032 5	0.045 3	0.198 3	0.171 4	0.028 2	0.000 2	0.000 1	0.006 3	0.000 1	0.000 1	0.000 2	0.278 2	0.000 1	0.000 2	0.000 2	0.044 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

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 4	0.858 9	0.575 4	0.831 26	0.685 9	0.714 2	0.979 1	0.594 4	0.310 21	0.801 1	0.892 12	0.841 2	0.819 3	0.723 3	0.940 9	0.887 3	0.725 18
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 17	0.818 12	0.836 16	0.790 2	0.875 2	0.576 3	0.905 3	0.704 3	0.739 1	0.969 7	0.611 1	0.349 7	0.756 16	0.958 1	0.702 37	0.805 12	0.708 6	0.916 25	0.898 1	0.801 1

PPT-SpUNet-Joint	0.766 3	0.932 2	0.794 27	0.829 20	0.751 17	0.854 11	0.540 15	0.903 4	0.630 28	0.672 10	0.963 10	0.565 17	0.357 5	0.788 2	0.900 8	0.737 20	0.802 13	0.685 12	0.950 3	0.887 3	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.
OctFormer	0.766 3	0.925 4	0.808 18	0.849 7	0.786 3	0.846 21	0.566 7	0.876 10	0.690 7	0.674 9	0.960 12	0.576 13	0.226 59	0.753 18	0.904 6	0.777 8	0.815 5	0.722 4	0.923 21	0.877 9	0.776 4
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net	0.764 5	0.924 5	0.819 10	0.840 14	0.757 12	0.853 13	0.580 1	0.848 19	0.709 2	0.643 18	0.958 15	0.587 8	0.295 27	0.753 18	0.884 16	0.758 14	0.815 5	0.725 2	0.927 19	0.867 16	0.743 10

OccuSeg+Semantic	0.764 5	0.758 53	0.796 25	0.839 15	0.746 19	0.907 1	0.562 8	0.850 18	0.680 11	0.672 10	0.978 2	0.610 2	0.335 12	0.777 5	0.819 39	0.847 1	0.830 1	0.691 10	0.972 1	0.885 5	0.727 16

O-CNN	0.762 7	0.924 5	0.823 6	0.844 12	0.770 6	0.852 14	0.577 2	0.847 21	0.711 1	0.640 22	0.958 15	0.592 5	0.217 65	0.762 12	0.888 13	0.758 14	0.813 8	0.726 1	0.932 17	0.868 15	0.744 9
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 39	0.826 5	0.858 4	0.776 5	0.837 27	0.548 12	0.896 7	0.649 20	0.675 8	0.962 11	0.586 9	0.335 12	0.771 8	0.802 43	0.770 10	0.787 28	0.691 10	0.936 12	0.880 8	0.761 6

PNE	0.755 9	0.786 37	0.835 4	0.834 18	0.758 10	0.849 17	0.570 6	0.836 25	0.648 21	0.668 12	0.978 2	0.581 12	0.367 3	0.683 28	0.856 25	0.804 3	0.801 17	0.678 14	0.961 2	0.889 2	0.716 23

ConDaFormer	0.755 9	0.927 3	0.822 7	0.836 16	0.801 1	0.849 17	0.516 25	0.864 15	0.651 19	0.680 7	0.958 15	0.584 11	0.282 34	0.759 14	0.855 27	0.728 22	0.802 13	0.678 14	0.880 52	0.873 14	0.756 7
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
PointTransformerV2	0.752 11	0.742 60	0.809 17	0.872 1	0.758 10	0.860 8	0.552 10	0.891 8	0.610 35	0.687 3	0.960 12	0.559 20	0.304 24	0.766 10	0.926 3	0.767 11	0.797 20	0.644 27	0.942 7	0.876 12	0.722 20
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 11	0.906 9	0.793 29	0.802 35	0.689 33	0.825 38	0.556 9	0.867 12	0.681 10	0.602 37	0.960 12	0.555 22	0.365 4	0.779 4	0.859 22	0.747 17	0.795 24	0.717 5	0.917 24	0.856 24	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
PointConvFormer	0.749 13	0.793 35	0.790 30	0.807 31	0.750 18	0.856 10	0.524 21	0.881 9	0.588 47	0.642 21	0.977 5	0.591 6	0.274 39	0.781 3	0.929 2	0.804 3	0.796 21	0.642 28	0.947 5	0.885 5	0.715 24
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNet	0.749 13	0.909 7	0.818 12	0.811 28	0.752 15	0.839 26	0.485 40	0.842 22	0.673 12	0.644 17	0.957 19	0.528 31	0.305 23	0.773 7	0.859 22	0.788 5	0.818 4	0.693 9	0.916 25	0.856 24	0.723 19
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 15	0.623 86	0.804 20	0.859 3	0.745 20	0.824 40	0.501 30	0.912 2	0.690 7	0.685 5	0.956 20	0.567 16	0.320 18	0.768 9	0.918 4	0.720 27	0.802 13	0.676 17	0.921 22	0.881 7	0.779 3

StratifiedFormer	0.747 16	0.901 10	0.803 21	0.845 11	0.757 12	0.846 21	0.512 26	0.825 29	0.696 6	0.645 16	0.956 20	0.576 13	0.262 50	0.744 23	0.861 21	0.742 18	0.770 37	0.705 7	0.899 38	0.860 21	0.734 11
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 17	0.870 15	0.838 2	0.858 4	0.729 25	0.850 16	0.501 30	0.874 11	0.587 48	0.658 14	0.956 20	0.564 18	0.299 25	0.765 11	0.900 8	0.716 30	0.812 9	0.631 33	0.939 10	0.858 22	0.709 25
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 17	0.771 47	0.819 10	0.848 9	0.702 31	0.865 7	0.397 77	0.899 5	0.699 4	0.664 13	0.948 48	0.588 7	0.330 14	0.746 22	0.851 31	0.764 12	0.796 21	0.704 8	0.935 13	0.866 17	0.728 14
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 19	0.842 23	0.800 22	0.767 48	0.740 21	0.836 29	0.541 14	0.914 1	0.672 13	0.626 26	0.958 15	0.552 23	0.272 41	0.777 5	0.886 15	0.696 38	0.801 17	0.674 19	0.941 8	0.858 22	0.717 21
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 20	0.620 87	0.799 24	0.849 7	0.730 24	0.822 42	0.493 37	0.897 6	0.664 14	0.681 6	0.955 23	0.562 19	0.378 1	0.760 13	0.903 7	0.738 19	0.801 17	0.673 20	0.907 30	0.877 9	0.745 8
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet	0.742 21	0.816 30	0.806 19	0.807 31	0.752 15	0.828 36	0.575 4	0.839 24	0.699 4	0.637 23	0.954 29	0.520 33	0.320 18	0.755 17	0.834 35	0.760 13	0.772 34	0.676 17	0.915 27	0.862 19	0.717 21

SAT	0.742 21	0.860 18	0.765 42	0.819 23	0.769 7	0.848 19	0.533 17	0.829 27	0.663 15	0.631 25	0.955 23	0.586 9	0.274 39	0.753 18	0.896 10	0.729 21	0.760 44	0.666 22	0.921 22	0.855 26	0.733 12

LargeKernel3D	0.739 23	0.909 7	0.820 9	0.806 33	0.740 21	0.852 14	0.545 13	0.826 28	0.594 46	0.643 18	0.955 23	0.541 25	0.263 49	0.723 26	0.858 24	0.775 9	0.767 38	0.678 14	0.933 15	0.848 31	0.694 30
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 24	0.776 43	0.790 30	0.851 6	0.754 14	0.854 11	0.491 39	0.866 13	0.596 45	0.686 4	0.955 23	0.536 26	0.342 9	0.624 43	0.869 18	0.787 6	0.802 13	0.628 34	0.927 19	0.875 13	0.704 27

MinkowskiNet	0.736 24	0.859 19	0.818 12	0.832 19	0.709 29	0.840 25	0.521 23	0.853 17	0.660 17	0.643 18	0.951 38	0.544 24	0.286 32	0.731 24	0.893 11	0.675 46	0.772 34	0.683 13	0.874 58	0.852 29	0.727 16
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 26	0.890 11	0.837 3	0.864 2	0.726 26	0.873 3	0.530 20	0.824 30	0.489 79	0.647 15	0.978 2	0.609 3	0.336 11	0.624 43	0.733 52	0.758 14	0.776 32	0.570 58	0.949 4	0.877 9	0.728 14

PointTransformer++	0.725 27	0.727 68	0.811 16	0.819 23	0.765 8	0.841 24	0.502 29	0.814 35	0.621 31	0.623 28	0.955 23	0.556 21	0.284 33	0.620 45	0.866 19	0.781 7	0.757 47	0.648 25	0.932 17	0.862 19	0.709 25

SparseConvNet	0.725 27	0.647 83	0.821 8	0.846 10	0.721 27	0.869 4	0.533 17	0.754 50	0.603 41	0.614 30	0.955 23	0.572 15	0.325 16	0.710 27	0.870 17	0.724 25	0.823 2	0.628 34	0.934 14	0.865 18	0.683 33

MatchingNet	0.724 29	0.812 32	0.812 15	0.810 29	0.735 23	0.834 31	0.495 36	0.860 16	0.572 54	0.602 37	0.954 29	0.512 35	0.280 36	0.757 15	0.845 33	0.725 24	0.780 30	0.606 44	0.937 11	0.851 30	0.700 29

INS-Conv-semantic	0.717 30	0.751 56	0.759 45	0.812 27	0.704 30	0.868 5	0.537 16	0.842 22	0.609 37	0.608 33	0.953 32	0.534 28	0.293 28	0.616 46	0.864 20	0.719 29	0.793 25	0.640 29	0.933 15	0.845 35	0.663 38

PointMetaBase	0.714 31	0.835 24	0.785 32	0.821 21	0.684 35	0.846 21	0.531 19	0.865 14	0.614 32	0.596 41	0.953 32	0.500 38	0.246 55	0.674 29	0.888 13	0.692 39	0.764 40	0.624 36	0.849 73	0.844 36	0.675 35

contrastBoundary	0.705 32	0.769 50	0.775 37	0.809 30	0.687 34	0.820 45	0.439 65	0.812 36	0.661 16	0.591 43	0.945 56	0.515 34	0.171 83	0.633 40	0.856 25	0.720 27	0.796 21	0.668 21	0.889 45	0.847 32	0.689 31
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 33	0.774 45	0.800 22	0.793 39	0.760 9	0.847 20	0.471 43	0.802 39	0.463 86	0.634 24	0.968 9	0.491 41	0.271 43	0.726 25	0.910 5	0.706 34	0.815 5	0.551 69	0.878 53	0.833 37	0.570 69

RFCR	0.702 34	0.889 12	0.745 54	0.813 26	0.672 38	0.818 49	0.493 37	0.815 34	0.623 29	0.610 31	0.947 50	0.470 49	0.249 54	0.594 49	0.848 32	0.705 35	0.779 31	0.646 26	0.892 43	0.823 43	0.611 52
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 35	0.825 28	0.796 25	0.723 55	0.716 28	0.832 32	0.433 67	0.816 32	0.634 26	0.609 32	0.969 7	0.418 74	0.344 8	0.559 61	0.833 36	0.715 31	0.808 11	0.560 63	0.902 35	0.847 32	0.680 34

JSENet	0.699 36	0.881 14	0.762 43	0.821 21	0.667 39	0.800 62	0.522 22	0.792 42	0.613 33	0.607 34	0.935 76	0.492 40	0.205 70	0.576 54	0.853 29	0.691 40	0.758 46	0.652 24	0.872 61	0.828 40	0.649 42
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 37	0.743 59	0.794 27	0.655 79	0.684 35	0.822 42	0.497 35	0.719 60	0.622 30	0.617 29	0.977 5	0.447 61	0.339 10	0.750 21	0.664 68	0.703 36	0.790 27	0.596 48	0.946 6	0.855 26	0.647 43
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 38	0.732 64	0.772 38	0.786 40	0.677 37	0.866 6	0.517 24	0.848 19	0.509 71	0.626 26	0.952 36	0.536 26	0.225 61	0.545 67	0.704 59	0.689 43	0.810 10	0.564 62	0.903 34	0.854 28	0.729 13
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 39	0.884 13	0.754 49	0.795 38	0.647 45	0.818 49	0.422 69	0.802 39	0.612 34	0.604 35	0.945 56	0.462 52	0.189 78	0.563 60	0.853 29	0.726 23	0.765 39	0.632 32	0.904 32	0.821 46	0.606 56
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 40	0.704 73	0.741 58	0.754 52	0.656 41	0.829 34	0.501 30	0.741 55	0.609 37	0.548 50	0.950 42	0.522 32	0.371 2	0.633 40	0.756 47	0.715 31	0.771 36	0.623 37	0.861 69	0.814 48	0.658 39
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 41	0.866 16	0.748 51	0.819 23	0.645 47	0.794 65	0.450 54	0.802 39	0.587 48	0.604 35	0.945 56	0.464 51	0.201 73	0.554 63	0.840 34	0.723 26	0.732 56	0.602 46	0.907 30	0.822 45	0.603 59

KP-FCNN	0.684 42	0.847 22	0.758 47	0.784 42	0.647 45	0.814 52	0.473 42	0.772 45	0.605 39	0.594 42	0.935 76	0.450 59	0.181 81	0.587 50	0.805 42	0.690 41	0.785 29	0.614 40	0.882 49	0.819 47	0.632 48
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 42	0.712 72	0.784 33	0.782 44	0.658 40	0.835 30	0.499 34	0.823 31	0.641 23	0.597 40	0.950 42	0.487 42	0.281 35	0.575 55	0.619 71	0.647 59	0.764 40	0.620 39	0.871 64	0.846 34	0.688 32

VACNN++	0.684 42	0.728 67	0.757 48	0.776 45	0.690 32	0.804 59	0.464 48	0.816 32	0.577 53	0.587 44	0.945 56	0.508 37	0.276 38	0.671 30	0.710 57	0.663 51	0.750 50	0.589 53	0.881 50	0.832 39	0.653 41

Superpoint Network	0.683 45	0.851 21	0.728 62	0.800 37	0.653 43	0.806 57	0.468 45	0.804 37	0.572 54	0.602 37	0.946 53	0.453 58	0.239 58	0.519 72	0.822 37	0.689 43	0.762 43	0.595 50	0.895 41	0.827 41	0.630 49

PointContrast_LA_SEM	0.683 45	0.757 54	0.784 33	0.786 40	0.639 49	0.824 40	0.408 72	0.775 44	0.604 40	0.541 52	0.934 80	0.532 29	0.269 45	0.552 64	0.777 45	0.645 62	0.793 25	0.640 29	0.913 28	0.824 42	0.671 36

VI-PointConv	0.676 47	0.770 49	0.754 49	0.783 43	0.621 53	0.814 52	0.552 10	0.758 48	0.571 56	0.557 48	0.954 29	0.529 30	0.268 47	0.530 70	0.682 63	0.675 46	0.719 59	0.603 45	0.888 46	0.833 37	0.665 37
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 48	0.789 36	0.748 51	0.763 50	0.635 51	0.814 52	0.407 74	0.747 52	0.581 52	0.573 45	0.950 42	0.484 43	0.271 43	0.607 47	0.754 48	0.649 56	0.774 33	0.596 48	0.883 48	0.823 43	0.606 56

SALANet	0.670 49	0.816 30	0.770 40	0.768 47	0.652 44	0.807 56	0.451 51	0.747 52	0.659 18	0.545 51	0.924 86	0.473 48	0.149 93	0.571 57	0.811 41	0.635 65	0.746 51	0.623 37	0.892 43	0.794 60	0.570 69

PointConv	0.666 50	0.781 40	0.759 45	0.699 64	0.644 48	0.822 42	0.475 41	0.779 43	0.564 59	0.504 68	0.953 32	0.428 68	0.203 72	0.586 52	0.754 48	0.661 52	0.753 48	0.588 54	0.902 35	0.813 50	0.642 44
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 50	0.703 74	0.781 35	0.751 54	0.655 42	0.830 33	0.471 43	0.769 46	0.474 82	0.537 54	0.951 38	0.475 47	0.279 37	0.635 38	0.698 62	0.675 46	0.751 49	0.553 68	0.816 80	0.806 52	0.703 28
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 52	0.746 57	0.708 66	0.722 56	0.638 50	0.820 45	0.451 51	0.566 87	0.599 43	0.541 52	0.950 42	0.510 36	0.313 20	0.648 35	0.819 39	0.616 70	0.682 75	0.590 52	0.869 65	0.810 51	0.656 40
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 53	0.778 41	0.702 69	0.806 33	0.619 54	0.813 55	0.468 45	0.693 68	0.494 74	0.524 60	0.941 67	0.449 60	0.298 26	0.510 74	0.821 38	0.675 46	0.727 58	0.568 60	0.826 78	0.803 54	0.637 46
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 54	0.698 75	0.743 56	0.650 80	0.564 71	0.820 45	0.505 28	0.758 48	0.631 27	0.479 73	0.945 56	0.480 45	0.226 59	0.572 56	0.774 46	0.690 41	0.735 54	0.614 40	0.853 72	0.776 75	0.597 62
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 55	0.752 55	0.734 60	0.664 77	0.583 66	0.815 51	0.399 76	0.754 50	0.639 24	0.535 56	0.942 65	0.470 49	0.309 22	0.665 31	0.539 77	0.650 55	0.708 64	0.635 31	0.857 71	0.793 62	0.642 44
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 56	0.778 41	0.731 61	0.699 64	0.577 67	0.829 34	0.446 56	0.736 56	0.477 81	0.523 62	0.945 56	0.454 56	0.269 45	0.484 81	0.749 51	0.618 68	0.738 52	0.599 47	0.827 77	0.792 65	0.621 51

MVPNet	0.641 57	0.831 25	0.715 64	0.671 74	0.590 62	0.781 71	0.394 78	0.679 70	0.642 22	0.553 49	0.937 73	0.462 52	0.256 51	0.649 34	0.406 90	0.626 66	0.691 72	0.666 22	0.877 54	0.792 65	0.608 55
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN	0.641 57	0.776 43	0.703 68	0.721 57	0.557 74	0.826 37	0.451 51	0.672 72	0.563 60	0.483 72	0.943 64	0.425 71	0.162 88	0.644 36	0.726 53	0.659 53	0.709 63	0.572 57	0.875 56	0.786 70	0.559 75

PointMRNet	0.640 59	0.717 71	0.701 70	0.692 67	0.576 68	0.801 61	0.467 47	0.716 61	0.563 60	0.459 78	0.953 32	0.429 67	0.169 85	0.581 53	0.854 28	0.605 71	0.710 61	0.550 70	0.894 42	0.793 62	0.575 67

FPConv	0.639 60	0.785 38	0.760 44	0.713 62	0.603 57	0.798 63	0.392 79	0.534 92	0.603 41	0.524 60	0.948 48	0.457 54	0.250 53	0.538 68	0.723 55	0.598 75	0.696 70	0.614 40	0.872 61	0.799 55	0.567 72
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 61	0.797 34	0.769 41	0.641 85	0.590 62	0.820 45	0.461 49	0.537 91	0.637 25	0.536 55	0.947 50	0.388 82	0.206 69	0.656 32	0.668 66	0.647 59	0.732 56	0.585 55	0.868 66	0.793 62	0.473 94

PointSPNet	0.637 62	0.734 63	0.692 77	0.714 61	0.576 68	0.797 64	0.446 56	0.743 54	0.598 44	0.437 83	0.942 65	0.403 78	0.150 92	0.626 42	0.800 44	0.649 56	0.697 69	0.557 66	0.846 74	0.777 74	0.563 73

SConv	0.636 63	0.830 26	0.697 73	0.752 53	0.572 70	0.780 73	0.445 58	0.716 61	0.529 65	0.530 57	0.951 38	0.446 62	0.170 84	0.507 76	0.666 67	0.636 64	0.682 75	0.541 75	0.886 47	0.799 55	0.594 63

Supervoxel-CNN	0.635 64	0.656 81	0.711 65	0.719 58	0.613 55	0.757 82	0.444 61	0.765 47	0.534 64	0.566 46	0.928 84	0.478 46	0.272 41	0.636 37	0.531 79	0.664 50	0.645 85	0.508 83	0.864 68	0.792 65	0.611 52

joint point-based	0.634 65	0.614 88	0.778 36	0.667 76	0.633 52	0.825 38	0.420 70	0.804 37	0.467 84	0.561 47	0.951 38	0.494 39	0.291 29	0.566 58	0.458 85	0.579 82	0.764 40	0.559 65	0.838 75	0.814 48	0.598 61
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 66	0.731 65	0.688 80	0.675 71	0.591 61	0.784 70	0.444 61	0.565 88	0.610 35	0.492 70	0.949 46	0.456 55	0.254 52	0.587 50	0.706 58	0.599 74	0.665 81	0.612 43	0.868 66	0.791 69	0.579 66

3DSM_DMMF	0.631 67	0.626 85	0.745 54	0.801 36	0.607 56	0.751 83	0.506 27	0.729 59	0.565 58	0.491 71	0.866 100	0.434 63	0.197 76	0.595 48	0.630 70	0.709 33	0.705 66	0.560 63	0.875 56	0.740 85	0.491 89

APCF-Net	0.631 67	0.742 60	0.687 82	0.672 72	0.557 74	0.792 68	0.408 72	0.665 73	0.545 62	0.508 65	0.952 36	0.428 68	0.186 79	0.634 39	0.702 60	0.620 67	0.706 65	0.555 67	0.873 59	0.798 57	0.581 65
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointNet2-SFPN	0.631 67	0.771 47	0.692 77	0.672 72	0.524 79	0.837 27	0.440 64	0.706 66	0.538 63	0.446 80	0.944 62	0.421 73	0.219 64	0.552 64	0.751 50	0.591 78	0.737 53	0.543 74	0.901 37	0.768 77	0.557 76

FusionAwareConv	0.630 70	0.604 90	0.741 58	0.766 49	0.590 62	0.747 84	0.501 30	0.734 57	0.503 73	0.527 58	0.919 90	0.454 56	0.323 17	0.550 66	0.420 89	0.678 45	0.688 73	0.544 72	0.896 40	0.795 59	0.627 50
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 71	0.800 33	0.625 92	0.719 58	0.545 77	0.806 57	0.445 58	0.597 82	0.448 89	0.519 63	0.938 72	0.481 44	0.328 15	0.489 80	0.499 84	0.657 54	0.759 45	0.592 51	0.881 50	0.797 58	0.634 47

SegGroup_sem	0.627 72	0.818 29	0.747 53	0.701 63	0.602 58	0.764 79	0.385 83	0.629 79	0.490 77	0.508 65	0.931 83	0.409 76	0.201 73	0.564 59	0.725 54	0.618 68	0.692 71	0.539 76	0.873 59	0.794 60	0.548 79
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 73	0.830 26	0.694 75	0.757 51	0.563 72	0.772 77	0.448 55	0.647 76	0.520 67	0.509 64	0.949 46	0.431 66	0.191 77	0.496 78	0.614 72	0.647 59	0.672 79	0.535 78	0.876 55	0.783 71	0.571 68

HPEIN	0.618 74	0.729 66	0.668 83	0.647 82	0.597 60	0.766 78	0.414 71	0.680 69	0.520 67	0.525 59	0.946 53	0.432 64	0.215 66	0.493 79	0.599 73	0.638 63	0.617 90	0.570 58	0.897 39	0.806 52	0.605 58
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 75	0.858 20	0.772 38	0.489 97	0.532 78	0.792 68	0.404 75	0.643 78	0.570 57	0.507 67	0.935 76	0.414 75	0.046 102	0.510 74	0.702 60	0.602 73	0.705 66	0.549 71	0.859 70	0.773 76	0.534 82
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 76	0.760 52	0.667 84	0.649 81	0.521 80	0.793 66	0.457 50	0.648 75	0.528 66	0.434 85	0.947 50	0.401 79	0.153 91	0.454 83	0.721 56	0.648 58	0.717 60	0.536 77	0.904 32	0.765 78	0.485 90
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 77	0.634 84	0.743 56	0.697 66	0.601 59	0.781 71	0.437 66	0.585 85	0.493 75	0.446 80	0.933 81	0.394 80	0.011 104	0.654 33	0.661 69	0.603 72	0.733 55	0.526 79	0.832 76	0.761 80	0.480 91

dtc_net	0.596 78	0.683 76	0.725 63	0.715 60	0.549 76	0.803 60	0.444 61	0.647 76	0.493 75	0.495 69	0.941 67	0.409 76	0.000 106	0.424 88	0.544 76	0.598 75	0.703 68	0.522 80	0.912 29	0.792 65	0.520 85

LAP-D	0.594 79	0.720 69	0.692 77	0.637 86	0.456 89	0.773 76	0.391 81	0.730 58	0.587 48	0.445 82	0.940 70	0.381 83	0.288 30	0.434 86	0.453 87	0.591 78	0.649 83	0.581 56	0.777 84	0.749 84	0.610 54

DPC	0.592 80	0.720 69	0.700 71	0.602 90	0.480 85	0.762 81	0.380 84	0.713 64	0.585 51	0.437 83	0.940 70	0.369 85	0.288 30	0.434 86	0.509 83	0.590 80	0.639 88	0.567 61	0.772 85	0.755 82	0.592 64
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 81	0.766 51	0.659 87	0.683 69	0.470 88	0.740 86	0.387 82	0.620 81	0.490 77	0.476 74	0.922 88	0.355 88	0.245 56	0.511 73	0.511 82	0.571 83	0.643 86	0.493 87	0.872 61	0.762 79	0.600 60

ROSMRF	0.580 82	0.772 46	0.707 67	0.681 70	0.563 72	0.764 79	0.362 86	0.515 93	0.465 85	0.465 77	0.936 75	0.427 70	0.207 68	0.438 84	0.577 74	0.536 86	0.675 78	0.486 88	0.723 91	0.779 72	0.524 84

SD-DETR	0.576 83	0.746 57	0.609 96	0.445 101	0.517 81	0.643 97	0.366 85	0.714 63	0.456 87	0.468 76	0.870 99	0.432 64	0.264 48	0.558 62	0.674 64	0.586 81	0.688 73	0.482 89	0.739 89	0.733 87	0.537 81

SQN_0.1%	0.569 84	0.676 78	0.696 74	0.657 78	0.497 82	0.779 74	0.424 68	0.548 89	0.515 69	0.376 90	0.902 97	0.422 72	0.357 5	0.379 91	0.456 86	0.596 77	0.659 82	0.544 72	0.685 94	0.665 98	0.556 77

TextureNet	0.566 85	0.672 80	0.664 85	0.671 74	0.494 83	0.719 87	0.445 58	0.678 71	0.411 95	0.396 88	0.935 76	0.356 87	0.225 61	0.412 89	0.535 78	0.565 84	0.636 89	0.464 91	0.794 83	0.680 95	0.568 71
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 86	0.648 82	0.700 71	0.770 46	0.586 65	0.687 91	0.333 90	0.650 74	0.514 70	0.475 75	0.906 94	0.359 86	0.223 63	0.340 93	0.442 88	0.422 97	0.668 80	0.501 84	0.708 92	0.779 72	0.534 82

Pointnet++ & Feature	0.557 87	0.735 62	0.661 86	0.686 68	0.491 84	0.744 85	0.392 79	0.539 90	0.451 88	0.375 91	0.946 53	0.376 84	0.205 70	0.403 90	0.356 93	0.553 85	0.643 86	0.497 85	0.824 79	0.756 81	0.515 86

GMLPs	0.538 88	0.495 98	0.693 76	0.647 82	0.471 87	0.793 66	0.300 93	0.477 94	0.505 72	0.358 92	0.903 96	0.327 91	0.081 99	0.472 82	0.529 80	0.448 95	0.710 61	0.509 81	0.746 87	0.737 86	0.554 78

PanopticFusion-label	0.529 89	0.491 99	0.688 80	0.604 89	0.386 94	0.632 98	0.225 103	0.705 67	0.434 92	0.293 98	0.815 101	0.348 89	0.241 57	0.499 77	0.669 65	0.507 88	0.649 83	0.442 97	0.796 82	0.602 101	0.561 74
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 90	0.676 78	0.591 99	0.609 87	0.442 90	0.774 75	0.335 89	0.597 82	0.422 94	0.357 93	0.932 82	0.341 90	0.094 98	0.298 95	0.528 81	0.473 93	0.676 77	0.495 86	0.602 100	0.721 90	0.349 101

Online SegFusion	0.515 91	0.607 89	0.644 90	0.579 92	0.434 91	0.630 99	0.353 87	0.628 80	0.440 90	0.410 86	0.762 104	0.307 93	0.167 86	0.520 71	0.403 91	0.516 87	0.565 93	0.447 95	0.678 95	0.701 92	0.514 87
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 92	0.558 94	0.608 97	0.424 103	0.478 86	0.690 90	0.246 99	0.586 84	0.468 83	0.450 79	0.911 92	0.394 80	0.160 89	0.438 84	0.212 100	0.432 96	0.541 98	0.475 90	0.742 88	0.727 88	0.477 92

PCNN	0.498 93	0.559 93	0.644 90	0.560 94	0.420 93	0.711 89	0.229 101	0.414 95	0.436 91	0.352 94	0.941 67	0.324 92	0.155 90	0.238 100	0.387 92	0.493 89	0.529 99	0.509 81	0.813 81	0.751 83	0.504 88

3DMV	0.484 94	0.484 100	0.538 101	0.643 84	0.424 92	0.606 102	0.310 91	0.574 86	0.433 93	0.378 89	0.796 102	0.301 94	0.214 67	0.537 69	0.208 101	0.472 94	0.507 102	0.413 100	0.693 93	0.602 101	0.539 80
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 95	0.577 92	0.611 95	0.356 105	0.321 102	0.715 88	0.299 95	0.376 99	0.328 102	0.319 96	0.944 62	0.285 96	0.164 87	0.216 103	0.229 98	0.484 91	0.545 97	0.456 93	0.755 86	0.709 91	0.475 93
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 96	0.679 77	0.604 98	0.578 93	0.380 95	0.682 92	0.291 96	0.106 105	0.483 80	0.258 103	0.920 89	0.258 100	0.025 103	0.231 102	0.325 94	0.480 92	0.560 95	0.463 92	0.725 90	0.666 97	0.231 105
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 97	0.474 101	0.623 93	0.463 99	0.366 97	0.651 95	0.310 91	0.389 98	0.349 100	0.330 95	0.937 73	0.271 98	0.126 95	0.285 96	0.224 99	0.350 102	0.577 92	0.445 96	0.625 98	0.723 89	0.394 97
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 98	0.548 95	0.548 100	0.597 91	0.363 98	0.628 100	0.300 93	0.292 100	0.374 97	0.307 97	0.881 98	0.268 99	0.186 79	0.238 100	0.204 102	0.407 98	0.506 103	0.449 94	0.667 96	0.620 100	0.462 95

SurfaceConvPF	0.442 98	0.505 97	0.622 94	0.380 104	0.342 100	0.654 94	0.227 102	0.397 97	0.367 98	0.276 100	0.924 86	0.240 101	0.198 75	0.359 92	0.262 96	0.366 99	0.581 91	0.435 98	0.640 97	0.668 96	0.398 96
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 100	0.437 103	0.646 89	0.474 98	0.369 96	0.645 96	0.353 87	0.258 102	0.282 104	0.279 99	0.918 91	0.298 95	0.147 94	0.283 97	0.294 95	0.487 90	0.562 94	0.427 99	0.619 99	0.633 99	0.352 100
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 101	0.525 96	0.647 88	0.522 95	0.324 101	0.488 105	0.077 106	0.712 65	0.353 99	0.401 87	0.636 106	0.281 97	0.176 82	0.340 93	0.565 75	0.175 106	0.551 96	0.398 101	0.370 106	0.602 101	0.361 99

SPLAT Net	0.393 102	0.472 102	0.511 102	0.606 88	0.311 103	0.656 93	0.245 100	0.405 96	0.328 102	0.197 104	0.927 85	0.227 103	0.000 106	0.001 107	0.249 97	0.271 105	0.510 100	0.383 103	0.593 101	0.699 93	0.267 103
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	0.383 103	0.297 105	0.491 103	0.432 102	0.358 99	0.612 101	0.274 97	0.116 104	0.411 95	0.265 101	0.904 95	0.229 102	0.079 100	0.250 98	0.185 103	0.320 103	0.510 100	0.385 102	0.548 102	0.597 104	0.394 97

PointNet++	0.339 104	0.584 91	0.478 104	0.458 100	0.256 105	0.360 106	0.250 98	0.247 103	0.278 105	0.261 102	0.677 105	0.183 104	0.117 96	0.212 104	0.145 105	0.364 100	0.346 106	0.232 106	0.548 102	0.523 105	0.252 104
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNet	0.308 105	0.353 104	0.290 106	0.278 106	0.166 106	0.553 103	0.169 105	0.286 101	0.147 106	0.148 106	0.908 93	0.182 105	0.064 101	0.023 106	0.018 107	0.354 101	0.363 104	0.345 104	0.546 104	0.685 94	0.278 102

ScanNet	0.306 106	0.203 106	0.366 105	0.501 96	0.311 103	0.524 104	0.211 104	0.002 107	0.342 101	0.189 105	0.786 103	0.145 106	0.102 97	0.245 99	0.152 104	0.318 104	0.348 105	0.300 105	0.460 105	0.437 106	0.182 106
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 107	0.000 107	0.041 107	0.172 107	0.030 107	0.062 107	0.001 107	0.035 106	0.004 107	0.051 107	0.143 107	0.019 107	0.003 105	0.041 105	0.050 106	0.003 107	0.054 107	0.018 107	0.005 107	0.264 107	0.082 107

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

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

MAFT	0.596 1	0.889 6	0.721 1	0.448 11	0.460 3	0.768 1	0.251 3	0.558 15	0.408 1	0.504 1	0.539 2	0.616 2	0.618 4	0.858 2	0.482 3	0.684 7	0.551 6	0.931 5	0.450 1

Queryformer	0.583 2	0.926 3	0.702 2	0.393 22	0.504 1	0.733 8	0.276 2	0.527 20	0.373 5	0.479 3	0.534 4	0.533 9	0.697 2	0.720 16	0.436 8	0.745 2	0.592 1	0.958 2	0.363 9

PBNet	0.573 3	0.926 3	0.575 11	0.619 1	0.472 2	0.736 6	0.239 5	0.487 26	0.383 4	0.459 5	0.506 8	0.533 8	0.585 6	0.767 8	0.404 9	0.717 3	0.559 5	0.969 1	0.381 6
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
Mask3D	0.566 4	0.926 3	0.597 7	0.408 19	0.420 5	0.737 5	0.239 4	0.598 8	0.386 3	0.458 6	0.549 1	0.568 6	0.716 1	0.601 28	0.480 4	0.646 11	0.575 3	0.922 6	0.364 8
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
ISBNet	0.559 5	0.939 2	0.655 3	0.383 24	0.426 4	0.763 2	0.180 7	0.534 19	0.386 2	0.499 2	0.509 7	0.621 1	0.427 23	0.704 19	0.467 5	0.649 10	0.571 4	0.948 3	0.401 2
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
GraphCut	0.552 6	1.000 1	0.611 6	0.438 14	0.392 8	0.714 9	0.139 9	0.598 9	0.327 8	0.389 8	0.510 6	0.598 3	0.427 24	0.754 11	0.463 6	0.761 1	0.588 2	0.903 10	0.329 15

SPFormer	0.549 7	0.745 15	0.640 4	0.484 6	0.395 7	0.739 4	0.311 1	0.566 13	0.335 7	0.468 4	0.492 9	0.555 7	0.478 14	0.747 13	0.436 7	0.712 4	0.540 7	0.893 14	0.343 14
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DKNet	0.532 8	0.815 10	0.624 5	0.517 3	0.377 10	0.749 3	0.107 11	0.509 23	0.304 10	0.437 7	0.475 10	0.581 4	0.539 9	0.775 7	0.339 14	0.640 13	0.506 10	0.901 11	0.385 5
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst	0.520 9	0.889 6	0.551 15	0.548 2	0.418 6	0.665 19	0.064 20	0.585 10	0.260 18	0.277 21	0.471 12	0.500 10	0.644 3	0.785 5	0.369 10	0.591 19	0.511 8	0.878 19	0.362 10

SoftGroup++	0.513 10	0.704 21	0.578 10	0.398 21	0.363 15	0.704 10	0.061 21	0.647 4	0.297 15	0.378 11	0.537 3	0.343 12	0.614 5	0.828 4	0.295 19	0.710 6	0.505 12	0.875 21	0.394 3

SSTNet	0.506 11	0.738 18	0.549 16	0.497 5	0.316 20	0.693 13	0.178 8	0.377 34	0.198 23	0.330 13	0.463 14	0.576 5	0.515 11	0.857 3	0.494 1	0.637 14	0.457 16	0.943 4	0.290 24
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SoftGroup	0.504 12	0.667 28	0.579 8	0.372 26	0.381 9	0.694 12	0.072 17	0.677 2	0.303 11	0.387 9	0.531 5	0.319 16	0.582 7	0.754 10	0.318 15	0.643 12	0.492 13	0.907 9	0.388 4
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
TD3D	0.489 13	0.852 8	0.511 24	0.434 15	0.322 19	0.735 7	0.101 14	0.512 22	0.355 6	0.349 12	0.468 13	0.283 20	0.514 12	0.676 23	0.268 24	0.671 8	0.510 9	0.908 8	0.329 16

OccuSeg+instance	0.486 14	0.802 12	0.536 18	0.428 17	0.369 12	0.702 11	0.205 6	0.331 39	0.301 12	0.379 10	0.474 11	0.327 13	0.437 19	0.862 1	0.485 2	0.601 17	0.394 26	0.846 30	0.273 26
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg	0.479 15	0.704 21	0.564 12	0.467 9	0.366 13	0.633 27	0.068 18	0.554 16	0.262 17	0.328 14	0.447 16	0.323 14	0.534 10	0.722 15	0.288 21	0.614 15	0.482 14	0.912 7	0.358 12

DualGroup	0.469 16	0.815 10	0.552 14	0.398 20	0.374 11	0.683 15	0.130 10	0.539 18	0.310 9	0.327 15	0.407 19	0.276 21	0.447 18	0.535 32	0.342 13	0.659 9	0.455 17	0.900 13	0.301 20

SSEC	0.465 17	0.667 28	0.578 9	0.502 4	0.362 16	0.641 26	0.035 30	0.605 6	0.291 16	0.323 16	0.451 15	0.296 18	0.417 27	0.677 22	0.245 28	0.501 36	0.506 11	0.900 12	0.366 7

HAIS	0.457 18	0.704 21	0.561 13	0.457 10	0.364 14	0.673 16	0.046 29	0.547 17	0.194 24	0.308 17	0.426 17	0.288 19	0.454 17	0.711 17	0.262 25	0.563 26	0.434 20	0.889 16	0.344 13
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group	0.436 19	0.630 36	0.508 27	0.480 7	0.310 21	0.624 30	0.065 19	0.638 5	0.174 25	0.256 25	0.384 22	0.194 33	0.428 21	0.759 9	0.289 20	0.574 23	0.400 24	0.849 28	0.291 23
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	0.435 20	0.716 20	0.495 29	0.355 28	0.331 17	0.689 14	0.102 13	0.394 33	0.208 22	0.280 19	0.395 21	0.250 24	0.544 8	0.741 14	0.309 17	0.536 32	0.391 27	0.842 33	0.258 30

Mask-Group	0.434 21	0.778 13	0.516 22	0.471 8	0.330 18	0.658 20	0.029 32	0.526 21	0.249 19	0.256 24	0.400 20	0.309 17	0.384 31	0.296 48	0.368 11	0.575 22	0.425 21	0.877 20	0.362 11
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Box2Mask	0.433 22	0.741 16	0.463 34	0.433 16	0.283 24	0.625 29	0.103 12	0.298 44	0.125 33	0.260 23	0.424 18	0.322 15	0.472 15	0.701 20	0.363 12	0.711 5	0.309 42	0.882 17	0.272 28
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
RPGN	0.428 23	0.630 36	0.508 26	0.367 27	0.249 31	0.658 21	0.016 39	0.673 3	0.131 31	0.234 28	0.383 23	0.270 22	0.434 20	0.748 12	0.274 23	0.609 16	0.406 23	0.842 32	0.267 29
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet	0.413 24	0.741 16	0.520 20	0.237 39	0.284 23	0.523 38	0.097 15	0.691 1	0.138 28	0.209 38	0.229 40	0.238 27	0.390 29	0.707 18	0.310 16	0.448 43	0.470 15	0.892 15	0.310 18

PointGroup	0.407 25	0.639 35	0.496 28	0.415 18	0.243 33	0.645 25	0.021 37	0.570 12	0.114 34	0.211 36	0.359 25	0.217 31	0.428 22	0.660 24	0.256 26	0.562 27	0.341 34	0.860 24	0.291 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]
CSC-Pretrained	0.405 26	0.738 18	0.465 33	0.331 32	0.205 37	0.655 22	0.051 25	0.601 7	0.092 38	0.211 37	0.329 28	0.198 32	0.459 16	0.775 6	0.195 35	0.524 34	0.400 25	0.878 18	0.184 39

PE	0.396 27	0.667 28	0.467 32	0.446 13	0.243 32	0.624 31	0.022 36	0.577 11	0.106 35	0.219 31	0.340 26	0.239 26	0.487 13	0.475 39	0.225 30	0.541 31	0.350 32	0.818 35	0.273 27
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3D	0.395 28	0.642 34	0.518 21	0.447 12	0.259 30	0.666 18	0.050 26	0.251 48	0.166 26	0.231 29	0.362 24	0.232 28	0.331 34	0.535 31	0.229 29	0.587 20	0.438 19	0.850 26	0.317 17
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS	0.392 29	0.778 13	0.530 19	0.220 41	0.278 25	0.567 35	0.083 16	0.330 40	0.299 13	0.270 22	0.310 31	0.143 38	0.260 38	0.624 26	0.277 22	0.568 25	0.361 30	0.865 23	0.301 19

AOIA	0.387 30	0.704 21	0.515 23	0.385 23	0.225 36	0.669 17	0.005 45	0.482 27	0.126 32	0.181 41	0.269 37	0.221 30	0.426 25	0.478 38	0.218 31	0.592 18	0.371 28	0.851 25	0.242 32

SSEN	0.384 31	0.852 8	0.494 30	0.192 42	0.226 35	0.648 24	0.022 35	0.398 32	0.299 14	0.277 20	0.317 30	0.231 29	0.194 45	0.514 35	0.196 33	0.586 21	0.444 18	0.843 31	0.184 38
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
PCJC	0.375 32	0.704 21	0.542 17	0.284 36	0.197 39	0.649 23	0.006 43	0.426 28	0.138 29	0.242 26	0.304 32	0.183 36	0.388 30	0.629 25	0.141 45	0.546 30	0.344 33	0.738 41	0.283 25

ClickSeg_Instance	0.366 33	0.654 32	0.375 38	0.184 43	0.302 22	0.592 33	0.050 27	0.300 43	0.093 37	0.283 18	0.277 34	0.249 25	0.426 26	0.615 27	0.299 18	0.504 35	0.367 29	0.832 34	0.191 37

SphereSeg	0.357 34	0.651 33	0.411 36	0.345 29	0.264 29	0.630 28	0.059 22	0.289 46	0.212 20	0.240 27	0.336 27	0.158 37	0.305 35	0.557 29	0.159 41	0.455 42	0.341 35	0.726 43	0.294 21

3D-MPA	0.355 35	0.457 47	0.484 31	0.299 34	0.277 26	0.591 34	0.047 28	0.332 37	0.212 21	0.217 32	0.278 33	0.193 34	0.413 28	0.410 42	0.195 34	0.574 24	0.352 31	0.849 27	0.213 35
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF	0.353 36	0.593 38	0.511 25	0.375 25	0.264 28	0.597 32	0.008 41	0.332 38	0.160 27	0.229 30	0.274 36	0.000 58	0.206 42	0.678 21	0.155 42	0.485 38	0.422 22	0.816 36	0.254 31
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
RWSeg	0.348 37	0.475 44	0.456 35	0.320 33	0.275 27	0.476 40	0.020 38	0.491 25	0.056 45	0.212 35	0.320 29	0.261 23	0.302 36	0.520 33	0.182 37	0.557 28	0.285 44	0.867 22	0.197 36

GICN	0.341 38	0.580 39	0.371 39	0.344 30	0.198 38	0.469 41	0.052 24	0.564 14	0.093 36	0.212 34	0.212 42	0.127 40	0.347 33	0.537 30	0.206 32	0.525 33	0.329 37	0.729 42	0.241 33

One_Thing_One_Click	0.326 39	0.472 45	0.361 40	0.232 40	0.183 40	0.555 36	0.000 51	0.498 24	0.038 47	0.195 39	0.226 41	0.362 11	0.168 46	0.469 40	0.251 27	0.553 29	0.335 36	0.846 29	0.117 47
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Occipital-SCS	0.320 40	0.679 27	0.352 41	0.334 31	0.229 34	0.436 42	0.025 33	0.412 31	0.058 43	0.161 46	0.240 39	0.085 42	0.262 37	0.496 37	0.187 36	0.467 40	0.328 38	0.775 37	0.231 34

Sparse R-CNN	0.292 41	0.704 21	0.213 51	0.153 45	0.154 42	0.551 37	0.053 23	0.212 49	0.132 30	0.174 43	0.274 35	0.070 44	0.363 32	0.441 41	0.176 38	0.424 45	0.234 46	0.758 39	0.161 43

MTML	0.282 42	0.577 40	0.380 37	0.182 44	0.107 48	0.430 43	0.001 48	0.422 29	0.057 44	0.179 42	0.162 45	0.070 45	0.229 40	0.511 36	0.161 39	0.491 37	0.313 39	0.650 48	0.162 41
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet	0.262 43	0.667 28	0.335 42	0.067 52	0.123 46	0.427 44	0.022 34	0.280 47	0.058 42	0.216 33	0.211 43	0.039 48	0.142 48	0.519 34	0.106 49	0.338 49	0.310 41	0.721 44	0.138 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	0.254 44	0.463 46	0.249 50	0.113 46	0.167 41	0.412 46	0.000 50	0.374 35	0.073 39	0.173 44	0.243 38	0.130 39	0.228 41	0.368 44	0.160 40	0.356 47	0.208 47	0.711 45	0.136 45
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet	0.253 45	0.519 42	0.324 45	0.251 38	0.137 45	0.345 51	0.031 31	0.419 30	0.069 40	0.162 45	0.131 47	0.052 46	0.202 44	0.338 46	0.147 44	0.301 52	0.303 43	0.651 47	0.178 40
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
SPG_WSIS	0.251 46	0.380 49	0.274 48	0.289 35	0.144 43	0.413 45	0.000 51	0.311 41	0.065 41	0.113 48	0.130 48	0.029 50	0.204 43	0.388 43	0.108 48	0.459 41	0.311 40	0.769 38	0.127 46

SegGroup_ins	0.246 47	0.556 41	0.335 43	0.062 54	0.115 47	0.490 39	0.000 51	0.297 45	0.018 51	0.186 40	0.142 46	0.083 43	0.233 39	0.216 50	0.153 43	0.469 39	0.251 45	0.744 40	0.083 50
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PanopticFusion-inst	0.214 48	0.250 53	0.330 44	0.275 37	0.103 49	0.228 57	0.000 51	0.345 36	0.024 49	0.088 50	0.203 44	0.186 35	0.167 47	0.367 45	0.125 46	0.221 55	0.112 57	0.666 46	0.162 42
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
UNet-backbone	0.161 49	0.519 42	0.259 49	0.084 48	0.059 51	0.325 53	0.002 46	0.093 54	0.009 53	0.077 52	0.064 51	0.045 47	0.044 55	0.161 52	0.045 51	0.331 50	0.180 49	0.566 49	0.033 58

3D-SIS	0.161 49	0.407 48	0.155 55	0.068 51	0.043 55	0.346 50	0.001 47	0.134 51	0.005 54	0.088 49	0.106 50	0.037 49	0.135 50	0.321 47	0.028 54	0.339 48	0.116 56	0.466 52	0.093 49
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet	0.158 51	0.356 50	0.173 53	0.113 47	0.140 44	0.359 47	0.012 40	0.023 56	0.039 46	0.134 47	0.123 49	0.008 54	0.089 51	0.149 53	0.117 47	0.221 54	0.128 54	0.563 50	0.094 48

Region-18class	0.146 52	0.175 57	0.321 46	0.080 49	0.062 50	0.357 48	0.000 51	0.307 42	0.002 55	0.066 53	0.044 53	0.000 58	0.018 57	0.036 57	0.054 50	0.447 44	0.133 52	0.472 51	0.060 53

SemRegionNet-20cls	0.121 53	0.296 52	0.203 52	0.071 50	0.058 52	0.349 49	0.000 51	0.150 50	0.019 50	0.054 54	0.034 55	0.017 53	0.052 53	0.042 56	0.013 57	0.209 56	0.183 48	0.371 53	0.057 54

3D-BEVIS	0.117 54	0.250 53	0.308 47	0.020 58	0.009 59	0.269 56	0.006 44	0.008 57	0.029 48	0.037 57	0.014 58	0.003 56	0.036 56	0.147 54	0.042 52	0.381 46	0.118 55	0.362 54	0.069 52
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Hier3D	0.117 54	0.222 55	0.161 54	0.054 56	0.027 56	0.289 54	0.000 51	0.124 52	0.001 57	0.079 51	0.061 52	0.027 51	0.141 49	0.240 49	0.005 58	0.310 51	0.129 53	0.153 58	0.081 51
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp	0.113 56	0.333 51	0.151 56	0.056 55	0.053 53	0.344 52	0.000 51	0.105 53	0.016 52	0.049 55	0.035 54	0.020 52	0.053 52	0.048 55	0.013 56	0.183 57	0.173 50	0.344 55	0.054 55

ASIS	0.085 57	0.037 58	0.080 58	0.066 53	0.047 54	0.282 55	0.000 51	0.052 55	0.002 56	0.047 56	0.026 56	0.001 57	0.046 54	0.194 51	0.031 53	0.264 53	0.140 51	0.167 57	0.047 57

Sgpn_scannet	0.049 58	0.023 59	0.134 57	0.031 57	0.013 58	0.144 58	0.006 42	0.008 58	0.000 58	0.028 58	0.017 57	0.003 55	0.009 59	0.000 58	0.021 55	0.122 58	0.095 58	0.175 56	0.054 56

MaskRCNN 2d->3d Proj	0.022 59	0.185 56	0.000 59	0.000 59	0.015 57	0.000 59	0.000 49	0.006 59	0.000 58	0.010 59	0.006 59	0.107 41	0.012 58	0.000 58	0.002 59	0.027 59	0.004 59	0.022 59	0.001 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