Benchmark Results - ScanNet Benchmark

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

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

AWCS	0.305 3	0.508 3	0.225 3	0.142 2	0.463 3	0.063 4	0.195 3	0.000 1	0.000 1	0.000 1	0.467 2	0.551 1	0.504 3	0.773 2	0.764 3	0.142 3	0.029 6	0.000 3	0.626 4	0.100 3	0.000 3	0.360 3	0.179 4	0.507 4	0.137 4	0.006 3	0.300 3	0.000 1	0.000 3	0.172 2	0.364 4	0.512 2	0.000 1	0.056 3	0.000 2	0.865 3	0.093 2	0.634 6	0.000 3	0.071 4	0.396 4	0.296 5	0.876 3	0.000 1	0.000 1	0.373 3	0.436 6	0.063 4	0.749 1	0.877 2	0.721 2	0.131 2	0.124 3	0.804 4	0.000 1	0.000 2	0.515 3	0.010 4	0.452 3	0.252 3	0.578 3	0.417 1	0.179 6	0.484 3	0.171 2	0.337 3	0.606 3	0.000 2	0.115 3	0.937 3	0.142 4	0.000 1	0.008 3	0.000 5	0.157 5	0.484 3	0.402 6	0.501 4	0.339 3	0.553 1	0.529 2	0.478 3	0.000 2	0.000 1	0.404 1	0.001 4	0.022 5	0.077 2	0.000 3	0.894 4	0.219 1	0.628 1	0.093 4	0.305 3	0.886 1	0.233 2	0.000 1	0.603 2	0.112 3	0.023 5	0.000 4	0.000 1	0.000 2	0.741 1	0.664 2	0.097 4	0.253 3	0.782 3	0.264 2	0.523 2	0.154 1	0.707 5	0.000 3	0.411 3	0.000 1	0.000 2	0.000 2	0.332 5	0.000 1	0.000 3	0.000 1	0.602 2	0.595 2	0.185 5	0.656 5	0.159 1	0.000 1	0.355 3	0.424 4	0.154 4	0.729 4	0.516 3	0.220 3	0.620 2	0.084 3	0.000 1	0.707 4	0.651 3	0.173 1	0.014 4	0.381 6	0.582 5	0.000 1	0.619 1	0.049 5	0.000 4	0.000 1	0.702 1	0.000 2	0.000 1	0.302 5	0.489 4	0.317 3	0.334 3	0.392 1	0.922 3	0.254 3	0.533 4	0.394 2	0.129 6	0.613 4	0.000 1	0.000 4	0.820 1	0.649 4	0.749 3	0.000 1	0.782 3	0.282 3	0.863 1	0.000 1	0.288 5	0.006 4	0.220 4	0.633 3	0.542 1

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

LGround	0.272 4	0.485 4	0.184 4	0.106 4	0.476 1	0.077 3	0.218 2	0.000 1	0.000 1	0.000 1	0.547 1	0.295 4	0.540 1	0.746 4	0.745 4	0.058 5	0.112 5	0.005 1	0.658 3	0.077 6	0.000 3	0.322 4	0.178 5	0.512 3	0.190 3	0.199 1	0.277 4	0.000 1	0.000 3	0.173 1	0.399 3	0.000 3	0.000 1	0.039 5	0.000 2	0.858 4	0.085 4	0.676 2	0.002 1	0.103 1	0.498 2	0.323 3	0.703 4	0.000 1	0.000 1	0.296 4	0.549 4	0.216 1	0.702 2	0.768 4	0.718 4	0.028 4	0.092 5	0.786 5	0.000 1	0.000 2	0.453 5	0.022 3	0.251 6	0.252 3	0.572 4	0.348 4	0.321 3	0.514 2	0.063 5	0.279 5	0.552 4	0.000 2	0.019 5	0.932 4	0.132 5	0.000 1	0.000 4	0.000 5	0.156 6	0.457 4	0.623 3	0.518 3	0.265 5	0.358 3	0.381 4	0.395 4	0.000 2	0.000 1	0.127 6	0.012 3	0.051 1	0.000 3	0.000 3	0.886 5	0.014 4	0.437 6	0.179 2	0.244 4	0.826 4	0.000 3	0.000 1	0.599 3	0.136 1	0.085 2	0.000 4	0.000 1	0.000 2	0.565 3	0.612 4	0.143 1	0.207 4	0.566 4	0.232 5	0.446 4	0.127 2	0.708 4	0.000 3	0.384 4	0.000 1	0.000 2	0.000 2	0.402 3	0.000 1	0.059 2	0.000 1	0.525 6	0.566 3	0.229 4	0.659 4	0.000 3	0.000 1	0.265 4	0.446 3	0.147 5	0.720 6	0.597 2	0.066 4	0.000 4	0.187 1	0.000 1	0.726 3	0.467 6	0.134 4	0.000 5	0.413 4	0.629 3	0.000 1	0.363 5	0.055 4	0.022 2	0.000 1	0.626 3	0.000 2	0.000 1	0.323 4	0.479 6	0.154 5	0.117 4	0.028 5	0.901 4	0.243 4	0.415 6	0.295 6	0.143 5	0.610 5	0.000 1	0.000 4	0.777 3	0.397 6	0.324 5	0.000 1	0.778 4	0.179 4	0.702 5	0.000 1	0.274 6	0.404 1	0.233 3	0.622 4	0.398 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CeCo	0.340 1	0.551 2	0.247 2	0.181 1	0.475 2	0.057 6	0.142 5	0.000 1	0.000 1	0.000 1	0.387 3	0.463 3	0.499 4	0.924 1	0.774 2	0.213 1	0.257 2	0.000 3	0.546 6	0.100 3	0.006 2	0.615 1	0.177 6	0.534 1	0.246 1	0.000 4	0.400 2	0.000 1	0.338 1	0.006 5	0.484 2	0.609 1	0.000 1	0.083 2	0.000 2	0.873 2	0.089 3	0.661 3	0.000 3	0.048 6	0.560 1	0.408 1	0.892 2	0.000 1	0.000 1	0.586 1	0.616 3	0.000 5	0.692 3	0.900 1	0.721 2	0.162 1	0.228 1	0.860 1	0.000 1	0.000 2	0.575 1	0.083 2	0.550 1	0.347 1	0.624 2	0.410 2	0.360 2	0.740 1	0.109 4	0.321 4	0.660 2	0.000 2	0.121 2	0.939 2	0.143 3	0.000 1	0.400 1	0.003 3	0.190 1	0.564 1	0.652 2	0.615 2	0.421 1	0.304 5	0.579 1	0.547 1	0.000 2	0.000 1	0.296 3	0.000 5	0.030 4	0.096 1	0.000 3	0.916 1	0.037 3	0.551 3	0.171 3	0.376 1	0.865 3	0.286 1	0.000 1	0.633 1	0.102 6	0.027 4	0.011 3	0.000 1	0.000 2	0.474 4	0.742 1	0.133 2	0.311 2	0.824 2	0.242 3	0.503 3	0.068 4	0.828 1	0.000 3	0.429 2	0.000 1	0.063 1	0.000 2	0.781 1	0.000 1	0.000 3	0.000 1	0.665 1	0.633 1	0.450 1	0.818 1	0.000 3	0.000 1	0.429 2	0.532 1	0.226 2	0.825 1	0.510 4	0.377 1	0.709 1	0.079 4	0.000 1	0.753 1	0.683 1	0.102 6	0.063 2	0.401 5	0.620 4	0.000 1	0.619 1	0.000 6	0.000 4	0.000 1	0.595 4	0.000 2	0.000 1	0.345 3	0.564 2	0.411 1	0.603 1	0.384 2	0.945 2	0.266 2	0.643 1	0.367 3	0.304 1	0.663 2	0.000 1	0.010 1	0.726 4	0.767 2	0.898 1	0.000 1	0.784 2	0.435 1	0.861 2	0.000 1	0.447 2	0.000 6	0.257 2	0.656 2	0.377 4
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
Minkowski 34D	0.253 5	0.463 5	0.154 6	0.102 5	0.381 6	0.084 1	0.134 6	0.000 1	0.000 1	0.000 1	0.386 4	0.141 6	0.279 6	0.737 5	0.703 5	0.014 6	0.164 4	0.000 3	0.663 2	0.092 5	0.000 3	0.224 5	0.291 2	0.531 2	0.056 6	0.000 4	0.242 5	0.000 1	0.000 3	0.013 4	0.331 5	0.000 3	0.000 1	0.035 6	0.001 1	0.858 4	0.059 5	0.650 5	0.000 3	0.056 5	0.353 5	0.299 4	0.670 5	0.000 1	0.000 1	0.284 5	0.484 5	0.071 3	0.594 5	0.720 5	0.710 5	0.027 5	0.068 6	0.813 3	0.000 1	0.005 1	0.492 4	0.164 1	0.274 5	0.111 6	0.571 5	0.307 6	0.293 4	0.307 6	0.150 3	0.163 6	0.531 5	0.002 1	0.545 1	0.932 4	0.093 6	0.000 1	0.000 4	0.002 4	0.159 4	0.368 6	0.581 5	0.440 6	0.228 6	0.406 2	0.282 6	0.294 5	0.000 2	0.000 1	0.189 5	0.060 1	0.036 3	0.000 3	0.000 3	0.897 3	0.000 6	0.525 5	0.025 6	0.205 6	0.771 6	0.000 3	0.000 1	0.593 4	0.108 4	0.044 3	0.000 4	0.000 1	0.000 2	0.282 6	0.589 5	0.094 5	0.169 5	0.466 6	0.227 6	0.419 6	0.125 3	0.757 3	0.002 1	0.334 5	0.000 1	0.000 2	0.000 2	0.357 4	0.000 1	0.000 3	0.000 1	0.582 4	0.513 6	0.337 3	0.612 6	0.000 3	0.000 1	0.250 5	0.352 6	0.136 6	0.724 5	0.655 1	0.280 2	0.000 4	0.046 6	0.000 1	0.606 6	0.559 4	0.159 2	0.102 1	0.445 2	0.655 2	0.000 1	0.310 6	0.117 1	0.000 4	0.000 1	0.581 6	0.026 1	0.000 1	0.265 6	0.483 5	0.084 6	0.097 6	0.044 4	0.865 6	0.142 6	0.588 2	0.351 4	0.272 2	0.596 6	0.000 1	0.003 2	0.622 5	0.720 3	0.096 6	0.000 1	0.771 5	0.016 5	0.772 4	0.000 1	0.302 4	0.194 3	0.214 5	0.621 5	0.197 6
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain	0.249 6	0.455 6	0.171 5	0.079 6	0.418 5	0.059 5	0.186 4	0.000 1	0.000 1	0.000 1	0.335 6	0.250 5	0.316 5	0.766 3	0.697 6	0.142 3	0.170 3	0.003 2	0.553 5	0.112 2	0.097 1	0.201 6	0.186 3	0.476 6	0.081 5	0.000 4	0.216 6	0.000 1	0.000 3	0.001 6	0.314 6	0.000 3	0.000 1	0.055 4	0.000 2	0.832 6	0.094 1	0.659 4	0.002 1	0.076 2	0.310 6	0.293 6	0.664 6	0.000 1	0.000 1	0.175 6	0.634 2	0.130 2	0.552 6	0.686 6	0.700 6	0.076 3	0.110 4	0.770 6	0.000 1	0.000 2	0.430 6	0.000 5	0.319 4	0.166 5	0.542 6	0.327 5	0.205 5	0.332 5	0.052 6	0.375 2	0.444 6	0.000 2	0.012 6	0.930 6	0.203 1	0.000 1	0.000 4	0.046 2	0.175 3	0.413 5	0.592 4	0.471 5	0.299 4	0.152 6	0.340 5	0.247 6	0.000 2	0.000 1	0.225 4	0.058 2	0.037 2	0.000 3	0.207 1	0.862 6	0.014 4	0.548 4	0.033 5	0.233 5	0.816 5	0.000 3	0.000 1	0.542 6	0.123 2	0.121 1	0.019 2	0.000 1	0.000 2	0.463 5	0.454 6	0.045 6	0.128 6	0.557 5	0.235 4	0.441 5	0.063 5	0.484 6	0.000 3	0.308 6	0.000 1	0.000 2	0.000 2	0.318 6	0.000 1	0.000 3	0.000 1	0.545 5	0.543 4	0.164 6	0.734 3	0.000 3	0.000 1	0.215 6	0.371 5	0.198 3	0.743 3	0.205 5	0.062 5	0.000 4	0.079 4	0.000 1	0.683 5	0.547 5	0.142 3	0.000 5	0.441 3	0.579 6	0.000 1	0.464 4	0.098 3	0.041 1	0.000 1	0.590 5	0.000 2	0.000 1	0.373 2	0.494 3	0.174 4	0.105 5	0.001 6	0.895 5	0.222 5	0.537 3	0.307 5	0.180 4	0.625 3	0.000 1	0.000 4	0.591 6	0.609 5	0.398 4	0.000 1	0.766 6	0.014 6	0.638 6	0.000 1	0.377 3	0.004 5	0.206 6	0.609 6	0.465 2
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021

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

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

Mask3D Scannet200	0.278 1	0.383 1	0.263 1	0.168 1	0.506 1	0.068 1	0.083 5	0.000 1	0.000 1	0.000 1	0.023 2	0.149 4	0.302 1	0.778 3	0.647 1	0.569 1	0.500 1	0.031 1	0.014 2	0.027 2	0.173 1	0.311 1	0.195 1	0.351 3	0.258 1	0.000 1	0.082 1	0.000 1	0.003 1	0.037 2	0.391 1	1.000 1	0.000 1	0.014 2	0.000 1	0.572 1	0.573 1	0.661 2	0.000 1	0.003 1	0.005 4	0.082 4	0.349 1	0.028 1	0.000 1	0.605 1	0.515 3	0.509 1	0.711 1	1.000 1	0.665 3	0.015 2	0.107 1	0.402 4	0.201 1	0.083 1	0.304 1	0.759 1	0.491 1	0.378 1	0.572 1	0.119 1	0.277 1	0.013 5	0.089 1	0.283 2	0.411 2	0.267 1	0.006 3	0.156 2	0.000 1	0.116 1	0.000 1	0.105 3	0.556 1	0.514 1	0.396 1	0.275 1	0.323 1	0.215 2	0.380 1	0.000 1	0.000 1	0.356 1	0.005 2	0.208 1	0.325 1	0.000 1	0.050 4	0.400 1	0.561 1	0.258 1	0.179 1	0.722 1	0.147 1	0.000 1	0.586 1	0.063 1	0.015 2	0.139 1	0.016 1	0.028 1	0.708 1	0.418 2	0.016 1	0.048 3	0.500 1	0.489 1	0.349 1	0.001 2	0.475 2	0.086 1	0.365 1	0.000 1	0.500 1	0.000 2	0.323 3	0.000 1	0.222 1	0.000 1	0.497 1	0.626 1	0.044 3	0.795 1	0.556 1	0.008 2	0.121 4	0.265 1	0.667 1	0.789 1	0.568 2	0.579 1	0.444 1	0.176 1	0.004 2	0.474 1	0.752 1	0.233 2	0.014 2	0.002 4	0.570 2	0.007 1	0.377 5	0.000 1	0.000 2	0.000 2	0.337 1	0.000 1	0.000 1	0.384 1	0.465 1	0.287 1	0.085 1	0.048 2	0.816 5	0.467 1	0.810 1	0.377 1	0.415 1	0.744 1	0.000 1	0.004 1	0.724 1	0.778 2	0.590 1	0.000 1	0.032 2	0.441 1	0.000 1	0.377 2	0.391 1	0.427 1	0.321 1	0.192 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	0.295 5	0.002 4	0.278 1	0.000 1	0.000 1	0.000 1	0.006 4	0.272 2	0.064 5	0.815 2	0.503 3	0.333 5	0.000 2	0.000 3	0.556 1	0.001 4	0.000 2	0.148 3	0.078 2	0.448 1	0.007 3	0.000 1	0.024 3	0.000 1	0.000 2	0.000 3	0.190 4	0.000 2	0.000 1	0.000 3	0.000 1	0.209 5	0.031 5	0.573 3	0.000 1	0.000 2	0.041 2	0.099 3	0.037 4	0.000 2	0.000 1	0.327 2	0.364 5	0.181 2	0.642 2	1.000 1	0.654 4	0.000 3	0.023 3	0.429 3	0.000 3	0.000 3	0.097 3	0.000 3	0.278 2	0.267 2	0.434 3	0.048 2	0.092 3	0.257 2	0.030 3	0.097 4	0.189 3	0.000 2	0.089 2	0.000 5	0.000 1	0.000 3	0.000 1	0.115 2	0.166 3	0.222 5	0.222 3	0.003 3	0.127 3	0.213 4	0.169 2	0.000 1	0.000 1	0.000 3	0.000 3	0.044 3	0.000 3	0.000 1	0.000 5	0.000 4	0.268 5	0.222 2	0.130 2	0.494 3	0.000 3	0.000 1	0.363 3	0.015 3	0.000 3	0.000 3	0.000 2	0.000 3	0.611 2	0.400 3	0.000 2	0.056 2	0.278 3	0.242 4	0.180 3	0.000 3	0.383 4	0.000 2	0.209 2	0.000 1	0.000 3	0.000 2	0.364 2	0.000 1	0.000 2	0.000 1	0.323 4	0.302 3	0.019 4	0.654 2	0.000 3	0.000 3	0.141 2	0.045 3	0.000 5	0.427 5	0.514 3	0.143 3	0.000 2	0.028 4	0.000 3	0.252 3	0.402 4	0.156 4	0.000 3	0.028 2	0.470 3	0.000 3	0.444 3	0.000 1	0.000 2	0.000 2	0.205 3	0.000 1	0.000 1	0.203 3	0.381 3	0.026 3	0.037 3	0.000 3	0.881 3	0.099 4	0.135 4	0.239 3	0.000 4	0.585 4	0.000 1	0.000 2	0.616 2	0.778 2	0.322 2	0.000 1	0.000 3	0.407 3	0.000 1	0.333 4	0.148 3	0.177 3	0.242 3	0.028 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
CSC-Pretrain Inst.	0.123 5	0.223 5	0.082 5	0.046 4	0.308 3	0.004 3	0.278 1	0.000 1	0.000 1	0.000 1	0.000 5	0.032 5	0.105 3	0.537 4	0.348 5	0.378 4	0.000 2	0.000 3	0.000 3	0.000 5	0.000 2	0.000 5	0.037 5	0.323 4	0.000 4	0.000 1	0.013 5	0.000 1	0.000 2	0.000 3	0.235 2	0.000 2	0.000 1	0.000 3	0.000 1	0.231 3	0.045 3	0.564 4	0.000 1	0.000 2	0.006 3	0.078 5	0.065 3	0.000 2	0.000 1	0.259 3	0.516 2	0.000 4	0.600 4	1.000 1	0.578 5	0.000 3	0.000 5	0.184 5	0.000 3	0.000 3	0.034 5	0.000 3	0.211 4	0.089 3	0.394 5	0.018 5	0.064 4	0.171 4	0.001 5	0.144 3	0.172 4	0.000 2	0.000 4	0.044 4	0.000 1	0.000 3	0.000 1	0.064 5	0.126 4	0.278 2	0.093 5	0.000 4	0.094 4	0.214 3	0.011 5	0.000 1	0.000 1	0.000 3	0.000 3	0.022 5	0.000 3	0.000 1	0.275 3	0.000 4	0.275 4	0.000 5	0.098 4	0.407 4	0.000 3	0.000 1	0.250 5	0.007 5	0.000 3	0.000 3	0.000 2	0.000 3	0.333 4	0.376 4	0.000 2	0.000 5	0.042 5	0.285 3	0.119 4	0.000 3	0.224 5	0.000 2	0.184 3	0.000 1	0.000 3	0.000 2	0.244 4	0.000 1	0.000 2	0.000 1	0.377 3	0.378 2	0.051 2	0.424 5	0.000 3	0.000 3	0.116 5	0.030 4	0.125 2	0.441 4	0.444 5	0.063 5	0.000 2	0.042 3	0.000 3	0.297 2	0.483 3	0.096 5	0.000 3	0.028 2	0.338 4	0.000 3	0.444 3	0.000 1	0.000 2	0.000 2	0.189 4	0.000 1	0.000 1	0.141 4	0.152 5	0.017 4	0.000 5	0.000 3	0.838 4	0.193 3	0.111 5	0.105 5	0.198 3	0.588 3	0.000 1	0.000 2	0.542 3	0.343 5	0.267 3	0.000 1	0.000 3	0.108 5	0.000 1	0.333 4	0.000 5	0.228 2	0.202 5	0.022 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34D Inst.	0.130 4	0.246 4	0.083 4	0.043 5	0.299 4	0.000 5	0.278 1	0.000 1	0.000 1	0.000 1	0.022 3	0.175 3	0.122 2	0.537 4	0.521 2	0.400 3	0.000 2	0.000 3	0.000 3	0.008 3	0.000 2	0.048 4	0.076 3	0.182 5	0.000 4	0.000 1	0.022 4	0.000 1	0.000 2	0.000 3	0.141 5	0.000 2	0.000 1	0.000 3	0.000 1	0.210 4	0.063 2	0.547 5	0.000 1	0.000 2	0.000 5	0.100 2	0.026 5	0.000 2	0.000 1	0.241 4	0.488 4	0.000 4	0.564 5	1.000 1	0.672 2	0.000 3	0.021 4	0.486 1	0.000 3	0.000 3	0.067 4	0.000 3	0.194 5	0.033 4	0.415 4	0.026 4	0.025 5	0.271 1	0.004 4	0.094 5	0.142 5	0.000 2	0.000 4	0.111 3	0.000 1	0.000 3	0.000 1	0.088 4	0.083 5	0.278 2	0.110 4	0.000 4	0.082 5	0.199 5	0.137 3	0.000 1	0.000 1	0.000 3	0.000 3	0.041 4	0.000 3	0.000 1	0.308 2	0.067 3	0.280 3	0.016 4	0.101 3	0.373 5	0.000 3	0.000 1	0.319 4	0.007 4	0.000 3	0.000 3	0.000 2	0.000 3	0.028 5	0.355 5	0.000 2	0.101 1	0.444 2	0.289 2	0.114 5	0.000 3	0.394 3	0.000 2	0.032 5	0.000 1	0.000 3	0.000 2	0.201 5	0.000 1	0.000 2	0.000 1	0.384 2	0.248 4	0.000 5	0.529 4	0.000 3	0.000 3	0.133 3	0.020 5	0.089 3	0.720 3	0.500 4	0.099 4	0.000 2	0.000 5	0.000 3	0.238 4	0.334 5	0.190 3	0.000 3	0.000 5	0.317 5	0.000 3	0.472 1	0.000 1	0.000 2	0.000 2	0.094 5	0.000 1	0.000 1	0.082 5	0.236 4	0.004 5	0.019 4	0.000 3	0.883 2	0.061 5	0.262 2	0.217 4	0.000 4	0.557 5	0.000 1	0.000 2	0.460 4	0.761 4	0.156 5	0.000 1	0.000 3	0.259 4	0.000 1	0.394 1	0.019 4	0.084 4	0.232 4	0.000 5
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	0.337 2	0.036 2	0.222 4	0.000 1	0.000 1	0.000 1	0.031 1	0.342 1	0.093 4	0.852 1	0.452 4	0.559 2	0.000 2	0.004 2	0.000 3	0.039 1	0.000 2	0.309 2	0.047 4	0.380 2	0.028 2	0.000 1	0.080 2	0.000 1	0.000 2	0.147 1	0.192 3	0.000 2	0.000 1	0.083 1	0.000 1	0.395 2	0.039 4	0.662 1	0.000 1	0.000 2	0.074 1	0.135 1	0.296 2	0.000 2	0.000 1	0.231 5	0.646 1	0.139 3	0.633 3	1.000 1	0.705 1	0.048 1	0.088 2	0.439 2	0.184 2	0.039 2	0.266 2	0.551 2	0.260 3	0.026 5	0.463 2	0.046 3	0.252 2	0.249 3	0.083 2	0.372 1	0.411 1	0.000 2	0.414 1	0.323 1	0.000 1	0.052 2	0.000 1	0.157 1	0.278 2	0.278 2	0.237 2	0.015 2	0.321 2	0.253 1	0.060 4	0.000 1	0.000 1	0.272 2	0.008 1	0.169 2	0.032 2	0.000 1	0.404 1	0.356 2	0.283 2	0.073 3	0.028 5	0.617 2	0.038 2	0.000 1	0.494 2	0.037 2	0.215 1	0.083 2	0.000 2	0.003 2	0.486 3	0.694 1	0.000 2	0.040 4	0.083 4	0.219 5	0.209 2	0.007 1	0.483 1	0.000 2	0.125 4	0.000 1	0.150 2	0.014 1	0.544 1	0.000 1	0.000 2	0.000 1	0.260 5	0.143 5	0.200 1	0.610 3	0.028 2	0.032 1	0.145 1	0.059 2	0.046 4	0.740 2	0.806 1	0.543 2	0.000 2	0.108 2	0.008 1	0.222 5	0.669 2	0.456 1	0.074 1	0.224 1	0.586 1	0.006 2	0.451 2	0.000 1	0.002 1	0.889 1	0.282 2	0.000 1	0.000 1	0.252 2	0.413 2	0.111 2	0.074 2	0.240 1	0.893 1	0.266 2	0.144 3	0.293 2	0.281 2	0.604 2	0.000 1	0.000 2	0.379 5	0.963 1	0.250 4	0.000 1	0.160 1	0.420 2	0.000 1	0.343 3	0.207 2	0.079 5	0.315 2	0.052 2

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

OccuSeg+Semantic	0.764 2	0.758 47	0.796 21	0.839 13	0.746 12	0.907 1	0.562 5	0.850 14	0.680 9	0.672 6	0.978 2	0.610 1	0.335 8	0.777 4	0.819 32	0.847 1	0.830 1	0.691 8	0.972 1	0.885 2	0.727 12

IPCA	0.731 21	0.890 8	0.837 3	0.864 2	0.726 19	0.873 2	0.530 16	0.824 25	0.489 74	0.647 10	0.978 2	0.609 2	0.336 7	0.624 38	0.733 46	0.758 12	0.776 26	0.570 53	0.949 2	0.877 6	0.728 10

PointConvFormer	0.749 8	0.793 32	0.790 25	0.807 25	0.750 11	0.856 9	0.524 17	0.881 8	0.588 42	0.642 16	0.977 4	0.591 5	0.274 35	0.781 2	0.929 1	0.804 3	0.796 15	0.642 24	0.947 3	0.885 2	0.715 19
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
TXC	0.740 18	0.842 19	0.832 4	0.805 29	0.715 22	0.846 15	0.473 36	0.885 7	0.615 27	0.671 7	0.971 6	0.547 19	0.320 14	0.697 23	0.799 38	0.777 6	0.819 3	0.682 11	0.946 4	0.871 9	0.696 24

One-Thing-One-Click	0.693 31	0.743 53	0.794 23	0.655 73	0.684 29	0.822 36	0.497 30	0.719 54	0.622 25	0.617 23	0.977 4	0.447 55	0.339 6	0.750 17	0.664 62	0.703 31	0.790 21	0.596 43	0.946 4	0.855 21	0.647 38
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PointTransformerV2	0.752 6	0.742 54	0.809 15	0.872 1	0.758 6	0.860 7	0.552 7	0.891 6	0.610 31	0.687 2	0.960 9	0.559 15	0.304 21	0.766 9	0.926 2	0.767 9	0.797 14	0.644 23	0.942 6	0.876 8	0.722 16
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
Retro-FPN	0.744 14	0.842 19	0.800 19	0.767 42	0.740 14	0.836 23	0.541 11	0.914 1	0.672 11	0.626 20	0.958 11	0.552 18	0.272 37	0.777 4	0.886 11	0.696 32	0.801 12	0.674 15	0.941 7	0.858 17	0.717 17

Mix3D	0.781 1	0.964 1	0.855 1	0.843 11	0.781 1	0.858 8	0.575 3	0.831 21	0.685 7	0.714 1	0.979 1	0.594 3	0.310 18	0.801 1	0.892 8	0.841 2	0.819 3	0.723 3	0.940 8	0.887 1	0.725 14
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
VMNet	0.746 12	0.870 12	0.838 2	0.858 4	0.729 18	0.850 13	0.501 25	0.874 9	0.587 43	0.658 9	0.956 15	0.564 13	0.299 22	0.765 10	0.900 5	0.716 26	0.812 8	0.631 29	0.939 9	0.858 17	0.709 20
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)
MatchingNet	0.724 24	0.812 29	0.812 13	0.810 23	0.735 16	0.834 25	0.495 31	0.860 12	0.572 49	0.602 31	0.954 23	0.512 30	0.280 32	0.757 13	0.845 26	0.725 20	0.780 24	0.606 39	0.937 10	0.851 25	0.700 23

OA-CNN-L_ScanNet20	0.756 5	0.783 35	0.826 5	0.858 4	0.776 2	0.837 21	0.548 9	0.896 5	0.649 17	0.675 5	0.962 8	0.586 8	0.335 8	0.771 7	0.802 36	0.770 8	0.787 22	0.691 8	0.936 11	0.880 5	0.761 3

Virtual MVFusion	0.746 12	0.771 41	0.819 9	0.848 7	0.702 25	0.865 6	0.397 71	0.899 3	0.699 3	0.664 8	0.948 42	0.588 6	0.330 10	0.746 18	0.851 24	0.764 10	0.796 15	0.704 6	0.935 12	0.866 12	0.728 10
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
SparseConvNet	0.725 22	0.647 77	0.821 7	0.846 8	0.721 20	0.869 3	0.533 13	0.754 44	0.603 37	0.614 24	0.955 18	0.572 11	0.325 12	0.710 22	0.870 13	0.724 21	0.823 2	0.628 30	0.934 13	0.865 13	0.683 28

LargeKernel3D	0.739 19	0.909 4	0.820 8	0.806 27	0.740 14	0.852 11	0.545 10	0.826 23	0.594 41	0.643 13	0.955 18	0.541 21	0.263 44	0.723 21	0.858 19	0.775 7	0.767 32	0.678 12	0.933 14	0.848 26	0.694 25

INS-Conv-semantic	0.717 25	0.751 50	0.759 39	0.812 21	0.704 24	0.868 4	0.537 12	0.842 18	0.609 33	0.608 27	0.953 26	0.534 23	0.293 25	0.616 40	0.864 15	0.719 25	0.793 19	0.640 25	0.933 14	0.845 30	0.663 33

PointTransformer++	0.725 22	0.727 62	0.811 14	0.819 17	0.765 5	0.841 18	0.502 24	0.814 30	0.621 26	0.623 22	0.955 18	0.556 16	0.284 30	0.620 39	0.866 14	0.781 5	0.757 41	0.648 21	0.932 16	0.862 14	0.709 20

O-CNN	0.762 4	0.924 2	0.823 6	0.844 10	0.770 3	0.852 11	0.577 2	0.847 17	0.711 1	0.640 17	0.958 11	0.592 4	0.217 59	0.762 11	0.888 9	0.758 12	0.813 7	0.726 1	0.932 16	0.868 10	0.744 5
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
CU-Hybrid Net	0.764 2	0.924 2	0.819 9	0.840 12	0.757 7	0.853 10	0.580 1	0.848 15	0.709 2	0.643 13	0.958 11	0.587 7	0.295 24	0.753 15	0.884 12	0.758 12	0.815 6	0.725 2	0.927 18	0.867 11	0.743 6

MSP	0.748 10	0.623 80	0.804 17	0.859 3	0.745 13	0.824 34	0.501 25	0.912 2	0.690 6	0.685 3	0.956 15	0.567 12	0.320 14	0.768 8	0.918 3	0.720 23	0.802 11	0.676 13	0.921 19	0.881 4	0.779 1

SAT	0.742 16	0.860 14	0.765 36	0.819 17	0.769 4	0.848 14	0.533 13	0.829 22	0.663 13	0.631 19	0.955 18	0.586 8	0.274 35	0.753 15	0.896 6	0.729 18	0.760 38	0.666 18	0.921 19	0.855 21	0.733 8

DMF-Net	0.752 6	0.906 6	0.793 24	0.802 30	0.689 27	0.825 32	0.556 6	0.867 10	0.681 8	0.602 31	0.960 9	0.555 17	0.365 3	0.779 3	0.859 17	0.747 15	0.795 18	0.717 4	0.917 21	0.856 19	0.764 2

BPNet	0.749 8	0.909 4	0.818 11	0.811 22	0.752 9	0.839 20	0.485 34	0.842 18	0.673 10	0.644 12	0.957 14	0.528 26	0.305 20	0.773 6	0.859 17	0.788 4	0.818 5	0.693 7	0.916 22	0.856 19	0.723 15
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
LRPNet	0.742 16	0.816 27	0.806 16	0.807 25	0.752 9	0.828 30	0.575 3	0.839 20	0.699 3	0.637 18	0.954 23	0.520 28	0.320 14	0.755 14	0.834 28	0.760 11	0.772 28	0.676 13	0.915 23	0.862 14	0.717 17

PointContrast_LA_SEM	0.683 39	0.757 48	0.784 27	0.786 34	0.639 43	0.824 34	0.408 66	0.775 38	0.604 36	0.541 46	0.934 74	0.532 24	0.269 40	0.552 58	0.777 39	0.645 56	0.793 19	0.640 25	0.913 24	0.824 36	0.671 31

dtc_net	0.596 72	0.683 70	0.725 57	0.715 54	0.549 70	0.803 54	0.444 55	0.647 70	0.493 70	0.495 63	0.941 61	0.409 70	0.000 100	0.424 82	0.544 70	0.598 69	0.703 62	0.522 74	0.912 25	0.792 59	0.520 79

EQ-Net	0.743 15	0.620 81	0.799 20	0.849 6	0.730 17	0.822 36	0.493 32	0.897 4	0.664 12	0.681 4	0.955 18	0.562 14	0.378 1	0.760 12	0.903 4	0.738 17	0.801 12	0.673 16	0.907 26	0.877 6	0.745 4
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
Feature-Geometry Net	0.685 35	0.866 13	0.748 45	0.819 17	0.645 41	0.794 59	0.450 48	0.802 34	0.587 43	0.604 29	0.945 50	0.464 45	0.201 67	0.554 57	0.840 27	0.723 22	0.732 50	0.602 41	0.907 26	0.822 39	0.603 54

AttAN	0.609 70	0.760 46	0.667 78	0.649 75	0.521 74	0.793 60	0.457 44	0.648 69	0.528 61	0.434 79	0.947 44	0.401 73	0.153 85	0.454 77	0.721 50	0.648 52	0.717 54	0.536 71	0.904 28	0.765 72	0.485 84
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
Feature_GeometricNet	0.690 33	0.884 10	0.754 43	0.795 33	0.647 39	0.818 43	0.422 63	0.802 34	0.612 30	0.604 29	0.945 50	0.462 46	0.189 72	0.563 54	0.853 22	0.726 19	0.765 33	0.632 28	0.904 28	0.821 40	0.606 51
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
PicassoNet-II	0.692 32	0.732 58	0.772 32	0.786 34	0.677 31	0.866 5	0.517 20	0.848 15	0.509 66	0.626 20	0.952 30	0.536 22	0.225 55	0.545 61	0.704 53	0.689 37	0.810 9	0.564 57	0.903 30	0.854 23	0.729 9
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One Thing One Click	0.701 29	0.825 25	0.796 21	0.723 49	0.716 21	0.832 26	0.433 61	0.816 27	0.634 22	0.609 26	0.969 7	0.418 68	0.344 5	0.559 55	0.833 29	0.715 27	0.808 10	0.560 58	0.902 31	0.847 27	0.680 29

PointConv	0.666 44	0.781 36	0.759 39	0.699 58	0.644 42	0.822 36	0.475 35	0.779 37	0.564 54	0.504 62	0.953 26	0.428 62	0.203 66	0.586 46	0.754 42	0.661 46	0.753 42	0.588 49	0.902 31	0.813 44	0.642 39
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointNet2-SFPN	0.631 61	0.771 41	0.692 71	0.672 66	0.524 73	0.837 21	0.440 58	0.706 60	0.538 58	0.446 74	0.944 56	0.421 67	0.219 58	0.552 58	0.751 44	0.591 72	0.737 47	0.543 68	0.901 33	0.768 71	0.557 70

StratifiedFormer	0.747 11	0.901 7	0.803 18	0.845 9	0.757 7	0.846 15	0.512 21	0.825 24	0.696 5	0.645 11	0.956 15	0.576 10	0.262 45	0.744 19	0.861 16	0.742 16	0.770 31	0.705 5	0.899 34	0.860 16	0.734 7
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
HPEIN	0.618 68	0.729 60	0.668 77	0.647 76	0.597 54	0.766 72	0.414 65	0.680 63	0.520 62	0.525 53	0.946 47	0.432 58	0.215 60	0.493 73	0.599 67	0.638 57	0.617 84	0.570 53	0.897 35	0.806 46	0.605 53
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
FusionAwareConv	0.630 64	0.604 84	0.741 52	0.766 43	0.590 56	0.747 78	0.501 25	0.734 51	0.503 68	0.527 52	0.919 84	0.454 50	0.323 13	0.550 60	0.420 83	0.678 39	0.688 67	0.544 66	0.896 36	0.795 53	0.627 45
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
Superpoint Network	0.683 39	0.851 17	0.728 56	0.800 32	0.653 37	0.806 51	0.468 39	0.804 32	0.572 49	0.602 31	0.946 47	0.453 52	0.239 53	0.519 66	0.822 30	0.689 37	0.762 37	0.595 45	0.895 37	0.827 35	0.630 44

PointMRNet	0.640 53	0.717 65	0.701 64	0.692 61	0.576 62	0.801 55	0.467 41	0.716 55	0.563 55	0.459 72	0.953 26	0.429 61	0.169 79	0.581 47	0.854 21	0.605 65	0.710 55	0.550 64	0.894 38	0.793 56	0.575 62

SALANet	0.670 43	0.816 27	0.770 34	0.768 41	0.652 38	0.807 50	0.451 45	0.747 46	0.659 16	0.545 45	0.924 80	0.473 42	0.149 87	0.571 51	0.811 34	0.635 59	0.746 45	0.623 32	0.892 39	0.794 54	0.570 64

RFCR	0.702 28	0.889 9	0.745 48	0.813 20	0.672 32	0.818 43	0.493 32	0.815 29	0.623 24	0.610 25	0.947 44	0.470 43	0.249 49	0.594 43	0.848 25	0.705 30	0.779 25	0.646 22	0.892 39	0.823 37	0.611 47
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
contrastBoundary	0.705 27	0.769 44	0.775 31	0.809 24	0.687 28	0.820 39	0.439 59	0.812 31	0.661 14	0.591 37	0.945 50	0.515 29	0.171 77	0.633 35	0.856 20	0.720 23	0.796 15	0.668 17	0.889 41	0.847 27	0.689 26
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
VI-PointConv	0.676 41	0.770 43	0.754 43	0.783 37	0.621 47	0.814 46	0.552 7	0.758 42	0.571 51	0.557 42	0.954 23	0.529 25	0.268 42	0.530 64	0.682 57	0.675 40	0.719 53	0.603 40	0.888 42	0.833 32	0.665 32
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
SConv	0.636 57	0.830 23	0.697 67	0.752 47	0.572 64	0.780 67	0.445 52	0.716 55	0.529 60	0.530 51	0.951 32	0.446 56	0.170 78	0.507 70	0.666 61	0.636 58	0.682 69	0.541 69	0.886 43	0.799 49	0.594 58

ROSMRF3D	0.673 42	0.789 33	0.748 45	0.763 44	0.635 45	0.814 46	0.407 68	0.747 46	0.581 47	0.573 39	0.950 36	0.484 37	0.271 39	0.607 41	0.754 42	0.649 50	0.774 27	0.596 43	0.883 44	0.823 37	0.606 51

KP-FCNN	0.684 36	0.847 18	0.758 41	0.784 36	0.647 39	0.814 46	0.473 36	0.772 39	0.605 35	0.594 36	0.935 70	0.450 53	0.181 75	0.587 44	0.805 35	0.690 35	0.785 23	0.614 35	0.882 45	0.819 41	0.632 43
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 36	0.728 61	0.757 42	0.776 39	0.690 26	0.804 53	0.464 42	0.816 27	0.577 48	0.587 38	0.945 50	0.508 32	0.276 34	0.671 25	0.710 51	0.663 45	0.750 44	0.589 48	0.881 46	0.832 33	0.653 36

DenSeR	0.628 65	0.800 30	0.625 86	0.719 52	0.545 71	0.806 51	0.445 52	0.597 76	0.448 83	0.519 57	0.938 66	0.481 38	0.328 11	0.489 74	0.499 78	0.657 48	0.759 39	0.592 46	0.881 46	0.797 52	0.634 42

MVPNet	0.641 51	0.831 22	0.715 58	0.671 68	0.590 56	0.781 65	0.394 72	0.679 64	0.642 18	0.553 43	0.937 67	0.462 46	0.256 46	0.649 29	0.406 84	0.626 60	0.691 66	0.666 18	0.877 48	0.792 59	0.608 50
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
SIConv	0.625 67	0.830 23	0.694 69	0.757 45	0.563 66	0.772 71	0.448 49	0.647 70	0.520 62	0.509 58	0.949 40	0.431 60	0.191 71	0.496 72	0.614 66	0.647 53	0.672 73	0.535 72	0.876 49	0.783 65	0.571 63

PointConv-SFPN	0.641 51	0.776 39	0.703 62	0.721 51	0.557 68	0.826 31	0.451 45	0.672 66	0.563 55	0.483 66	0.943 58	0.425 65	0.162 82	0.644 31	0.726 47	0.659 47	0.709 57	0.572 52	0.875 50	0.786 64	0.559 69

3DSM_DMMF	0.631 61	0.626 79	0.745 48	0.801 31	0.607 50	0.751 77	0.506 22	0.729 53	0.565 53	0.491 65	0.866 94	0.434 57	0.197 70	0.595 42	0.630 64	0.709 29	0.705 60	0.560 58	0.875 50	0.740 79	0.491 83

MinkowskiNet	0.736 20	0.859 15	0.818 11	0.832 14	0.709 23	0.840 19	0.521 19	0.853 13	0.660 15	0.643 13	0.951 32	0.544 20	0.286 29	0.731 20	0.893 7	0.675 40	0.772 28	0.683 10	0.874 52	0.852 24	0.727 12
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
APCF-Net	0.631 61	0.742 54	0.687 76	0.672 66	0.557 68	0.792 62	0.408 66	0.665 67	0.545 57	0.508 59	0.952 30	0.428 62	0.186 73	0.634 34	0.702 54	0.620 61	0.706 59	0.555 62	0.873 53	0.798 51	0.581 60
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
SegGroup_sem	0.627 66	0.818 26	0.747 47	0.701 57	0.602 52	0.764 73	0.385 77	0.629 73	0.490 72	0.508 59	0.931 77	0.409 70	0.201 67	0.564 53	0.725 48	0.618 62	0.692 65	0.539 70	0.873 53	0.794 54	0.548 73
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
CCRFNet	0.589 75	0.766 45	0.659 81	0.683 63	0.470 82	0.740 80	0.387 76	0.620 75	0.490 72	0.476 68	0.922 82	0.355 82	0.245 51	0.511 67	0.511 76	0.571 77	0.643 80	0.493 81	0.872 55	0.762 73	0.600 55

JSENet	0.699 30	0.881 11	0.762 37	0.821 15	0.667 33	0.800 56	0.522 18	0.792 36	0.613 29	0.607 28	0.935 70	0.492 35	0.205 64	0.576 48	0.853 22	0.691 34	0.758 40	0.652 20	0.872 55	0.828 34	0.649 37
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
FPConv	0.639 54	0.785 34	0.760 38	0.713 56	0.603 51	0.798 57	0.392 73	0.534 86	0.603 37	0.524 54	0.948 42	0.457 48	0.250 48	0.538 62	0.723 49	0.598 69	0.696 64	0.614 35	0.872 55	0.799 49	0.567 66
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
DGNet	0.684 36	0.712 66	0.784 27	0.782 38	0.658 34	0.835 24	0.499 29	0.823 26	0.641 19	0.597 34	0.950 36	0.487 36	0.281 31	0.575 49	0.619 65	0.647 53	0.764 34	0.620 34	0.871 58	0.846 29	0.688 27

PPCNN++	0.663 46	0.746 51	0.708 60	0.722 50	0.638 44	0.820 39	0.451 45	0.566 81	0.599 39	0.541 46	0.950 36	0.510 31	0.313 17	0.648 30	0.819 32	0.616 64	0.682 69	0.590 47	0.869 59	0.810 45	0.656 35
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
PD-Net	0.638 55	0.797 31	0.769 35	0.641 79	0.590 56	0.820 39	0.461 43	0.537 85	0.637 21	0.536 49	0.947 44	0.388 76	0.206 63	0.656 27	0.668 60	0.647 53	0.732 50	0.585 50	0.868 60	0.793 56	0.473 88

PointMTL	0.632 60	0.731 59	0.688 74	0.675 65	0.591 55	0.784 64	0.444 55	0.565 82	0.610 31	0.492 64	0.949 40	0.456 49	0.254 47	0.587 44	0.706 52	0.599 68	0.665 75	0.612 38	0.868 60	0.791 63	0.579 61

Supervoxel-CNN	0.635 58	0.656 75	0.711 59	0.719 52	0.613 49	0.757 76	0.444 55	0.765 41	0.534 59	0.566 40	0.928 78	0.478 40	0.272 37	0.636 32	0.531 73	0.664 44	0.645 79	0.508 77	0.864 62	0.792 59	0.611 47

FusionNet	0.688 34	0.704 67	0.741 52	0.754 46	0.656 35	0.829 28	0.501 25	0.741 49	0.609 33	0.548 44	0.950 36	0.522 27	0.371 2	0.633 35	0.756 41	0.715 27	0.771 30	0.623 32	0.861 63	0.814 42	0.658 34
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
SPH3D-GCN	0.610 69	0.858 16	0.772 32	0.489 91	0.532 72	0.792 62	0.404 69	0.643 72	0.570 52	0.507 61	0.935 70	0.414 69	0.046 96	0.510 68	0.702 54	0.602 67	0.705 60	0.549 65	0.859 64	0.773 70	0.534 76
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
SAFNet-seg	0.654 49	0.752 49	0.734 54	0.664 71	0.583 60	0.815 45	0.399 70	0.754 44	0.639 20	0.535 50	0.942 59	0.470 43	0.309 19	0.665 26	0.539 71	0.650 49	0.708 58	0.635 27	0.857 65	0.793 56	0.642 39
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
HPGCNN	0.656 48	0.698 69	0.743 50	0.650 74	0.564 65	0.820 39	0.505 23	0.758 42	0.631 23	0.479 67	0.945 50	0.480 39	0.226 54	0.572 50	0.774 40	0.690 35	0.735 48	0.614 35	0.853 66	0.776 69	0.597 57
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
PointMetaBase	0.714 26	0.835 21	0.785 26	0.821 15	0.684 29	0.846 15	0.531 15	0.865 11	0.614 28	0.596 35	0.953 26	0.500 33	0.246 50	0.674 24	0.888 9	0.692 33	0.764 34	0.624 31	0.849 67	0.844 31	0.675 30

PointSPNet	0.637 56	0.734 57	0.692 71	0.714 55	0.576 62	0.797 58	0.446 50	0.743 48	0.598 40	0.437 77	0.942 59	0.403 72	0.150 86	0.626 37	0.800 37	0.649 50	0.697 63	0.557 61	0.846 68	0.777 68	0.563 67

joint point-based	0.634 59	0.614 82	0.778 30	0.667 70	0.633 46	0.825 32	0.420 64	0.804 32	0.467 79	0.561 41	0.951 32	0.494 34	0.291 26	0.566 52	0.458 79	0.579 76	0.764 34	0.559 60	0.838 69	0.814 42	0.598 56
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
wsss-transformer	0.600 71	0.634 78	0.743 50	0.697 60	0.601 53	0.781 65	0.437 60	0.585 79	0.493 70	0.446 74	0.933 75	0.394 74	0.011 98	0.654 28	0.661 63	0.603 66	0.733 49	0.526 73	0.832 70	0.761 74	0.480 85

RandLA-Net	0.645 50	0.778 37	0.731 55	0.699 58	0.577 61	0.829 28	0.446 50	0.736 50	0.477 76	0.523 56	0.945 50	0.454 50	0.269 40	0.484 75	0.749 45	0.618 62	0.738 46	0.599 42	0.827 71	0.792 59	0.621 46

DCM-Net	0.658 47	0.778 37	0.702 63	0.806 27	0.619 48	0.813 49	0.468 39	0.693 62	0.494 69	0.524 54	0.941 61	0.449 54	0.298 23	0.510 68	0.821 31	0.675 40	0.727 52	0.568 55	0.826 72	0.803 48	0.637 41
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
Pointnet++ & Feature	0.557 81	0.735 56	0.661 80	0.686 62	0.491 78	0.744 79	0.392 73	0.539 84	0.451 82	0.375 85	0.946 47	0.376 78	0.205 64	0.403 84	0.356 87	0.553 79	0.643 80	0.497 79	0.824 73	0.756 75	0.515 80

PointASNL	0.666 44	0.703 68	0.781 29	0.751 48	0.655 36	0.830 27	0.471 38	0.769 40	0.474 77	0.537 48	0.951 32	0.475 41	0.279 33	0.635 33	0.698 56	0.675 40	0.751 43	0.553 63	0.816 74	0.806 46	0.703 22
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PCNN	0.498 87	0.559 87	0.644 84	0.560 88	0.420 87	0.711 83	0.229 95	0.414 89	0.436 85	0.352 88	0.941 61	0.324 86	0.155 84	0.238 94	0.387 86	0.493 83	0.529 93	0.509 75	0.813 75	0.751 77	0.504 82

PanopticFusion-label	0.529 83	0.491 93	0.688 74	0.604 83	0.386 88	0.632 92	0.225 97	0.705 61	0.434 86	0.293 92	0.815 95	0.348 83	0.241 52	0.499 71	0.669 59	0.507 82	0.649 77	0.442 91	0.796 76	0.602 95	0.561 68
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
TextureNet	0.566 79	0.672 74	0.664 79	0.671 68	0.494 77	0.719 81	0.445 52	0.678 65	0.411 89	0.396 82	0.935 70	0.356 81	0.225 55	0.412 83	0.535 72	0.565 78	0.636 83	0.464 85	0.794 77	0.680 89	0.568 65
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
LAP-D	0.594 73	0.720 63	0.692 71	0.637 80	0.456 83	0.773 70	0.391 75	0.730 52	0.587 43	0.445 76	0.940 64	0.381 77	0.288 27	0.434 80	0.453 81	0.591 72	0.649 77	0.581 51	0.777 78	0.749 78	0.610 49

DPC	0.592 74	0.720 63	0.700 65	0.602 84	0.480 79	0.762 75	0.380 78	0.713 58	0.585 46	0.437 77	0.940 64	0.369 79	0.288 27	0.434 80	0.509 77	0.590 74	0.639 82	0.567 56	0.772 79	0.755 76	0.592 59
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
PointCNN with RGB	0.458 89	0.577 86	0.611 89	0.356 99	0.321 96	0.715 82	0.299 89	0.376 93	0.328 96	0.319 90	0.944 56	0.285 90	0.164 81	0.216 97	0.229 92	0.484 85	0.545 91	0.456 87	0.755 80	0.709 85	0.475 87
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
GMLPs	0.538 82	0.495 92	0.693 70	0.647 76	0.471 81	0.793 60	0.300 87	0.477 88	0.505 67	0.358 86	0.903 90	0.327 85	0.081 93	0.472 76	0.529 74	0.448 89	0.710 55	0.509 75	0.746 81	0.737 80	0.554 72

3DMV, FTSDF	0.501 86	0.558 88	0.608 91	0.424 97	0.478 80	0.690 84	0.246 93	0.586 78	0.468 78	0.450 73	0.911 86	0.394 74	0.160 83	0.438 78	0.212 94	0.432 90	0.541 92	0.475 84	0.742 82	0.727 82	0.477 86

SD-DETR	0.576 77	0.746 51	0.609 90	0.445 95	0.517 75	0.643 91	0.366 79	0.714 57	0.456 81	0.468 70	0.870 93	0.432 58	0.264 43	0.558 56	0.674 58	0.586 75	0.688 67	0.482 83	0.739 83	0.733 81	0.537 75

FCPN	0.447 90	0.679 71	0.604 92	0.578 87	0.380 89	0.682 86	0.291 90	0.106 99	0.483 75	0.258 97	0.920 83	0.258 94	0.025 97	0.231 96	0.325 88	0.480 86	0.560 89	0.463 86	0.725 84	0.666 91	0.231 99
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
ROSMRF	0.580 76	0.772 40	0.707 61	0.681 64	0.563 66	0.764 73	0.362 80	0.515 87	0.465 80	0.465 71	0.936 69	0.427 64	0.207 62	0.438 78	0.577 68	0.536 80	0.675 72	0.486 82	0.723 85	0.779 66	0.524 78

DVVNet	0.562 80	0.648 76	0.700 65	0.770 40	0.586 59	0.687 85	0.333 84	0.650 68	0.514 65	0.475 69	0.906 88	0.359 80	0.223 57	0.340 87	0.442 82	0.422 91	0.668 74	0.501 78	0.708 86	0.779 66	0.534 76

3DMV	0.484 88	0.484 94	0.538 95	0.643 78	0.424 86	0.606 96	0.310 85	0.574 80	0.433 87	0.378 83	0.796 96	0.301 88	0.214 61	0.537 63	0.208 95	0.472 88	0.507 96	0.413 94	0.693 87	0.602 95	0.539 74
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
SQN_0.1%	0.569 78	0.676 72	0.696 68	0.657 72	0.497 76	0.779 68	0.424 62	0.548 83	0.515 64	0.376 84	0.902 91	0.422 66	0.357 4	0.379 85	0.456 80	0.596 71	0.659 76	0.544 66	0.685 88	0.665 92	0.556 71

Online SegFusion	0.515 85	0.607 83	0.644 84	0.579 86	0.434 85	0.630 93	0.353 81	0.628 74	0.440 84	0.410 80	0.762 98	0.307 87	0.167 80	0.520 65	0.403 85	0.516 81	0.565 87	0.447 89	0.678 89	0.701 86	0.514 81
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
PNET2	0.442 92	0.548 89	0.548 94	0.597 85	0.363 92	0.628 94	0.300 87	0.292 94	0.374 91	0.307 91	0.881 92	0.268 93	0.186 73	0.238 94	0.204 96	0.407 92	0.506 97	0.449 88	0.667 90	0.620 94	0.462 89

SurfaceConvPF	0.442 92	0.505 91	0.622 88	0.380 98	0.342 94	0.654 88	0.227 96	0.397 91	0.367 92	0.276 94	0.924 80	0.240 95	0.198 69	0.359 86	0.262 90	0.366 93	0.581 85	0.435 92	0.640 91	0.668 90	0.398 90
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
DGCNN_reproduce	0.446 91	0.474 95	0.623 87	0.463 93	0.366 91	0.651 89	0.310 85	0.389 92	0.349 94	0.330 89	0.937 67	0.271 92	0.126 89	0.285 90	0.224 93	0.350 96	0.577 86	0.445 90	0.625 92	0.723 83	0.394 91
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
Tangent Convolutions	0.438 94	0.437 97	0.646 83	0.474 92	0.369 90	0.645 90	0.353 81	0.258 96	0.282 98	0.279 93	0.918 85	0.298 89	0.147 88	0.283 91	0.294 89	0.487 84	0.562 88	0.427 93	0.619 93	0.633 93	0.352 94
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
subcloud_weak	0.516 84	0.676 72	0.591 93	0.609 81	0.442 84	0.774 69	0.335 83	0.597 76	0.422 88	0.357 87	0.932 76	0.341 84	0.094 92	0.298 89	0.528 75	0.473 87	0.676 71	0.495 80	0.602 94	0.721 84	0.349 95

SPLAT Net	0.393 96	0.472 96	0.511 96	0.606 82	0.311 97	0.656 87	0.245 94	0.405 90	0.328 96	0.197 98	0.927 79	0.227 97	0.000 100	0.001 101	0.249 91	0.271 99	0.510 94	0.383 97	0.593 95	0.699 87	0.267 97
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 97	0.297 99	0.491 97	0.432 96	0.358 93	0.612 95	0.274 91	0.116 98	0.411 89	0.265 95	0.904 89	0.229 96	0.079 94	0.250 92	0.185 97	0.320 97	0.510 94	0.385 96	0.548 96	0.597 98	0.394 91

PointNet++	0.339 98	0.584 85	0.478 98	0.458 94	0.256 99	0.360 100	0.250 92	0.247 97	0.278 99	0.261 96	0.677 99	0.183 98	0.117 90	0.212 98	0.145 99	0.364 94	0.346 100	0.232 100	0.548 96	0.523 99	0.252 98
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 99	0.353 98	0.290 100	0.278 100	0.166 100	0.553 97	0.169 99	0.286 95	0.147 100	0.148 100	0.908 87	0.182 99	0.064 95	0.023 100	0.018 101	0.354 95	0.363 98	0.345 98	0.546 98	0.685 88	0.278 96

ScanNet	0.306 100	0.203 100	0.366 99	0.501 90	0.311 97	0.524 98	0.211 98	0.002 101	0.342 95	0.189 99	0.786 97	0.145 100	0.102 91	0.245 93	0.152 98	0.318 98	0.348 99	0.300 99	0.460 99	0.437 100	0.182 100
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
3DWSSS	0.425 95	0.525 90	0.647 82	0.522 89	0.324 95	0.488 99	0.077 100	0.712 59	0.353 93	0.401 81	0.636 100	0.281 91	0.176 76	0.340 87	0.565 69	0.175 100	0.551 90	0.398 95	0.370 100	0.602 95	0.361 93

ERROR	0.054 101	0.000 101	0.041 101	0.172 101	0.030 101	0.062 101	0.001 101	0.035 100	0.004 101	0.051 101	0.143 101	0.019 101	0.003 99	0.041 99	0.050 100	0.003 101	0.054 101	0.018 101	0.005 101	0.264 101	0.082 101

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

ISBNet	0.559 4	0.926 2	0.597 6	0.390 22	0.436 3	0.722 7	0.276 3	0.556 15	0.380 3	0.450 5	0.505 7	0.583 2	0.730 1	0.575 26	0.455 5	0.603 15	0.573 4	0.979 1	0.332 13
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
PBNet	0.573 2	0.926 2	0.575 10	0.619 1	0.472 2	0.736 4	0.239 5	0.487 25	0.383 2	0.459 3	0.506 6	0.533 7	0.585 6	0.767 7	0.404 8	0.717 3	0.559 5	0.969 2	0.381 4

Queryformer	0.583 1	0.926 2	0.702 1	0.393 21	0.504 1	0.733 6	0.276 2	0.527 19	0.373 4	0.479 1	0.534 3	0.533 8	0.697 3	0.720 15	0.436 7	0.745 2	0.592 1	0.958 3	0.363 7

SSTNet	0.506 10	0.738 17	0.549 15	0.497 5	0.316 19	0.693 12	0.178 7	0.377 33	0.198 22	0.330 12	0.463 13	0.576 4	0.515 11	0.857 2	0.494 1	0.637 12	0.457 15	0.943 4	0.290 23
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
Mask3D	0.566 3	0.926 2	0.597 5	0.408 18	0.420 4	0.737 3	0.239 4	0.598 8	0.386 1	0.458 4	0.549 1	0.568 5	0.716 2	0.601 25	0.480 3	0.646 9	0.575 3	0.922 5	0.364 6
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
TopoSeg	0.479 14	0.704 20	0.564 11	0.467 9	0.366 12	0.633 26	0.068 17	0.554 16	0.262 16	0.328 13	0.447 15	0.323 13	0.534 10	0.722 14	0.288 19	0.614 13	0.482 13	0.912 6	0.358 10

TD3D	0.489 12	0.852 7	0.511 23	0.434 14	0.322 18	0.735 5	0.101 13	0.512 21	0.355 5	0.349 11	0.468 12	0.283 19	0.514 12	0.676 21	0.268 22	0.671 7	0.510 8	0.908 7	0.329 15

SoftGroup	0.504 11	0.667 27	0.579 7	0.372 25	0.381 8	0.694 11	0.072 16	0.677 2	0.303 10	0.387 8	0.531 4	0.319 15	0.582 7	0.754 9	0.318 14	0.643 10	0.492 12	0.907 8	0.388 2
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
GraphCut	0.552 5	1.000 1	0.611 4	0.438 13	0.392 7	0.714 8	0.139 8	0.598 9	0.327 7	0.389 7	0.510 5	0.598 1	0.427 23	0.754 10	0.463 4	0.761 1	0.588 2	0.903 9	0.329 14

DKNet	0.532 7	0.815 9	0.624 3	0.517 3	0.377 9	0.749 1	0.107 10	0.509 22	0.304 9	0.437 6	0.475 9	0.581 3	0.539 9	0.775 6	0.339 13	0.640 11	0.506 9	0.901 10	0.385 3
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC	0.465 16	0.667 27	0.578 8	0.502 4	0.362 15	0.641 25	0.035 28	0.605 6	0.291 15	0.323 15	0.451 14	0.296 17	0.417 25	0.677 20	0.245 26	0.501 34	0.506 10	0.900 11	0.366 5

DualGroup	0.469 15	0.815 9	0.552 13	0.398 19	0.374 10	0.683 14	0.130 9	0.539 18	0.310 8	0.327 14	0.407 18	0.276 20	0.447 18	0.535 30	0.342 12	0.659 8	0.455 16	0.900 12	0.301 19

SPFormer	0.549 6	0.745 14	0.640 2	0.484 6	0.395 6	0.739 2	0.311 1	0.566 13	0.335 6	0.468 2	0.492 8	0.555 6	0.478 14	0.747 12	0.436 6	0.712 4	0.540 6	0.893 13	0.343 12
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DENet	0.413 23	0.741 15	0.520 19	0.237 38	0.284 21	0.523 36	0.097 14	0.691 1	0.138 27	0.209 36	0.229 38	0.238 25	0.390 27	0.707 17	0.310 15	0.448 41	0.470 14	0.892 14	0.310 17

HAIS	0.457 17	0.704 20	0.561 12	0.457 10	0.364 13	0.673 15	0.046 27	0.547 17	0.194 23	0.308 16	0.426 16	0.288 18	0.454 17	0.711 16	0.262 23	0.563 25	0.434 19	0.889 15	0.344 11
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Box2Mask	0.433 21	0.741 15	0.463 33	0.433 15	0.283 22	0.625 28	0.103 11	0.298 42	0.125 32	0.260 21	0.424 17	0.322 14	0.472 15	0.701 18	0.363 11	0.711 5	0.309 40	0.882 16	0.272 27
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
CSC-Pretrained	0.405 25	0.738 17	0.465 32	0.331 31	0.205 35	0.655 21	0.051 24	0.601 7	0.092 36	0.211 35	0.329 27	0.198 30	0.459 16	0.775 5	0.195 33	0.524 33	0.400 24	0.878 17	0.184 37

IPCA-Inst	0.520 8	0.889 6	0.551 14	0.548 2	0.418 5	0.665 18	0.064 19	0.585 10	0.260 17	0.277 19	0.471 11	0.500 9	0.644 4	0.785 4	0.369 9	0.591 18	0.511 7	0.878 18	0.362 8

Mask-Group	0.434 20	0.778 12	0.516 21	0.471 8	0.330 17	0.658 19	0.029 30	0.526 20	0.249 18	0.256 22	0.400 19	0.309 16	0.384 29	0.296 46	0.368 10	0.575 21	0.425 20	0.877 19	0.362 9
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
SoftGroup++	0.513 9	0.704 20	0.578 9	0.398 20	0.363 14	0.704 9	0.061 20	0.647 4	0.297 14	0.378 10	0.537 2	0.343 11	0.614 5	0.828 3	0.295 17	0.710 6	0.505 11	0.875 20	0.394 1

RWSeg	0.348 35	0.475 42	0.456 34	0.320 32	0.275 25	0.476 38	0.020 36	0.491 24	0.056 43	0.212 33	0.320 28	0.261 22	0.302 34	0.520 31	0.182 35	0.557 27	0.285 42	0.867 21	0.197 35

OSIS	0.392 28	0.778 12	0.530 18	0.220 40	0.278 23	0.567 33	0.083 15	0.330 39	0.299 12	0.270 20	0.310 30	0.143 36	0.260 36	0.624 24	0.277 20	0.568 24	0.361 28	0.865 22	0.301 18

PointGroup	0.407 24	0.639 33	0.496 27	0.415 17	0.243 31	0.645 24	0.021 35	0.570 12	0.114 33	0.211 34	0.359 24	0.217 29	0.428 22	0.660 22	0.256 24	0.562 26	0.341 32	0.860 23	0.291 21
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]
AOIA	0.387 29	0.704 20	0.515 22	0.385 23	0.225 34	0.669 16	0.005 43	0.482 26	0.126 31	0.181 39	0.269 35	0.221 28	0.426 24	0.478 36	0.218 29	0.592 17	0.371 27	0.851 24	0.242 31

Dyco3D	0.395 27	0.642 32	0.518 20	0.447 11	0.259 28	0.666 17	0.050 25	0.251 46	0.166 25	0.231 27	0.362 23	0.232 26	0.331 32	0.535 29	0.229 27	0.587 19	0.438 18	0.850 25	0.317 16
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
3D-MPA	0.355 33	0.457 45	0.484 30	0.299 33	0.277 24	0.591 32	0.047 26	0.332 36	0.212 20	0.217 30	0.278 32	0.193 32	0.413 26	0.410 40	0.195 32	0.574 23	0.352 29	0.849 26	0.213 34
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
DD-UNet+Group	0.436 18	0.630 34	0.508 26	0.480 7	0.310 20	0.624 29	0.065 18	0.638 5	0.174 24	0.256 23	0.384 21	0.194 31	0.428 21	0.759 8	0.289 18	0.574 22	0.400 23	0.849 27	0.291 22
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
One_Thing_One_Click	0.326 37	0.472 43	0.361 38	0.232 39	0.183 38	0.555 34	0.000 49	0.498 23	0.038 45	0.195 37	0.226 39	0.362 10	0.168 44	0.469 38	0.251 25	0.553 28	0.335 34	0.846 28	0.117 45
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
OccuSeg+instance	0.486 13	0.802 11	0.536 17	0.428 16	0.369 11	0.702 10	0.205 6	0.331 38	0.301 11	0.379 9	0.474 10	0.327 12	0.437 19	0.862 1	0.485 2	0.601 16	0.394 25	0.846 29	0.273 25
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
SSEN	0.384 30	0.852 7	0.494 29	0.192 41	0.226 33	0.648 23	0.022 33	0.398 31	0.299 13	0.277 18	0.317 29	0.231 27	0.194 43	0.514 33	0.196 31	0.586 20	0.444 17	0.843 30	0.184 36
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RPGN	0.428 22	0.630 34	0.508 25	0.367 26	0.249 29	0.658 20	0.016 37	0.673 3	0.131 30	0.234 26	0.383 22	0.270 21	0.434 20	0.748 11	0.274 21	0.609 14	0.406 22	0.842 31	0.267 28
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
INS-Conv-instance	0.435 19	0.716 19	0.495 28	0.355 27	0.331 16	0.689 13	0.102 12	0.394 32	0.208 21	0.280 17	0.395 20	0.250 23	0.544 8	0.741 13	0.309 16	0.536 31	0.391 26	0.842 32	0.258 29

PE	0.396 26	0.667 27	0.467 31	0.446 12	0.243 30	0.624 30	0.022 34	0.577 11	0.106 34	0.219 29	0.340 25	0.239 24	0.487 13	0.475 37	0.225 28	0.541 30	0.350 30	0.818 33	0.273 26
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
NeuralBF	0.353 34	0.593 36	0.511 24	0.375 24	0.264 26	0.597 31	0.008 39	0.332 37	0.160 26	0.229 28	0.274 34	0.000 56	0.206 40	0.678 19	0.155 40	0.485 36	0.422 21	0.816 34	0.254 30
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
Occipital-SCS	0.320 38	0.679 26	0.352 39	0.334 30	0.229 32	0.436 40	0.025 31	0.412 30	0.058 41	0.161 44	0.240 37	0.085 40	0.262 35	0.496 35	0.187 34	0.467 38	0.328 36	0.775 35	0.231 33

SPG_WSIS	0.251 44	0.380 47	0.274 46	0.289 34	0.144 41	0.413 43	0.000 49	0.311 40	0.065 39	0.113 46	0.130 46	0.029 48	0.204 41	0.388 41	0.108 46	0.459 39	0.311 38	0.769 36	0.127 44

Sparse R-CNN	0.292 39	0.704 20	0.213 49	0.153 43	0.154 40	0.551 35	0.053 22	0.212 47	0.132 29	0.174 41	0.274 33	0.070 42	0.363 30	0.441 39	0.176 36	0.424 43	0.234 44	0.758 37	0.161 41

SegGroup_ins	0.246 45	0.556 39	0.335 41	0.062 52	0.115 45	0.490 37	0.000 49	0.297 43	0.018 49	0.186 38	0.142 44	0.083 41	0.233 37	0.216 48	0.153 41	0.469 37	0.251 43	0.744 38	0.083 48
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PCJC	0.375 31	0.704 20	0.542 16	0.284 35	0.197 37	0.649 22	0.006 41	0.426 27	0.138 28	0.242 24	0.304 31	0.183 34	0.388 28	0.629 23	0.141 43	0.546 29	0.344 31	0.738 39	0.283 24

GICN	0.341 36	0.580 37	0.371 37	0.344 29	0.198 36	0.469 39	0.052 23	0.564 14	0.093 35	0.212 32	0.212 40	0.127 38	0.347 31	0.537 28	0.206 30	0.525 32	0.329 35	0.729 40	0.241 32

SphereSeg	0.357 32	0.651 31	0.411 35	0.345 28	0.264 27	0.630 27	0.059 21	0.289 44	0.212 19	0.240 25	0.336 26	0.158 35	0.305 33	0.557 27	0.159 39	0.455 40	0.341 33	0.726 41	0.294 20

SALoss-ResNet	0.262 41	0.667 27	0.335 40	0.067 50	0.123 44	0.427 42	0.022 32	0.280 45	0.058 40	0.216 31	0.211 41	0.039 46	0.142 46	0.519 32	0.106 47	0.338 47	0.310 39	0.721 42	0.138 42
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.254 42	0.463 44	0.249 48	0.113 44	0.167 39	0.412 44	0.000 48	0.374 34	0.073 37	0.173 42	0.243 36	0.130 37	0.228 39	0.368 42	0.160 38	0.356 45	0.208 45	0.711 43	0.136 43
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
PanopticFusion-inst	0.214 46	0.250 51	0.330 42	0.275 36	0.103 47	0.228 55	0.000 49	0.345 35	0.024 47	0.088 48	0.203 42	0.186 33	0.167 45	0.367 43	0.125 44	0.221 53	0.112 55	0.666 44	0.162 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)
3D-BoNet	0.253 43	0.519 40	0.324 43	0.251 37	0.137 43	0.345 49	0.031 29	0.419 29	0.069 38	0.162 43	0.131 45	0.052 44	0.202 42	0.338 44	0.147 42	0.301 50	0.303 41	0.651 45	0.178 38
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
MTML	0.282 40	0.577 38	0.380 36	0.182 42	0.107 46	0.430 41	0.001 46	0.422 28	0.057 42	0.179 40	0.162 43	0.070 43	0.229 38	0.511 34	0.161 37	0.491 35	0.313 37	0.650 46	0.162 39
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
UNet-backbone	0.161 47	0.519 40	0.259 47	0.084 46	0.059 49	0.325 51	0.002 44	0.093 52	0.009 51	0.077 50	0.064 49	0.045 45	0.044 53	0.161 50	0.045 49	0.331 48	0.180 47	0.566 47	0.033 56

R-PointNet	0.158 49	0.356 48	0.173 51	0.113 45	0.140 42	0.359 45	0.012 38	0.023 54	0.039 44	0.134 45	0.123 47	0.008 52	0.089 49	0.149 51	0.117 45	0.221 52	0.128 52	0.563 48	0.094 46

Region-18class	0.146 50	0.175 55	0.321 44	0.080 47	0.062 48	0.357 46	0.000 49	0.307 41	0.002 53	0.066 51	0.044 51	0.000 56	0.018 55	0.036 55	0.054 48	0.447 42	0.133 50	0.472 49	0.060 51

3D-SIS	0.161 47	0.407 46	0.155 53	0.068 49	0.043 53	0.346 48	0.001 45	0.134 49	0.005 52	0.088 47	0.106 48	0.037 47	0.135 48	0.321 45	0.028 52	0.339 46	0.116 54	0.466 50	0.093 47
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
SemRegionNet-20cls	0.121 51	0.296 50	0.203 50	0.071 48	0.058 50	0.349 47	0.000 49	0.150 48	0.019 48	0.054 52	0.034 53	0.017 51	0.052 51	0.042 54	0.013 55	0.209 54	0.183 46	0.371 51	0.057 52

3D-BEVIS	0.117 52	0.250 51	0.308 45	0.020 56	0.009 57	0.269 54	0.006 42	0.008 55	0.029 46	0.037 55	0.014 56	0.003 54	0.036 54	0.147 52	0.042 50	0.381 44	0.118 53	0.362 52	0.069 50
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
tmp	0.113 54	0.333 49	0.151 54	0.056 53	0.053 51	0.344 50	0.000 49	0.105 51	0.016 50	0.049 53	0.035 52	0.020 50	0.053 50	0.048 53	0.013 54	0.183 55	0.173 48	0.344 53	0.054 53

Sgpn_scannet	0.049 56	0.023 57	0.134 55	0.031 55	0.013 56	0.144 56	0.006 40	0.008 56	0.000 56	0.028 56	0.017 55	0.003 53	0.009 57	0.000 56	0.021 53	0.122 56	0.095 56	0.175 54	0.054 54

ASIS	0.085 55	0.037 56	0.080 56	0.066 51	0.047 52	0.282 53	0.000 49	0.052 53	0.002 54	0.047 54	0.026 54	0.001 55	0.046 52	0.194 49	0.031 51	0.264 51	0.140 49	0.167 55	0.047 55

Hier3D	0.117 52	0.222 53	0.161 52	0.054 54	0.027 54	0.289 52	0.000 49	0.124 50	0.001 55	0.079 49	0.061 50	0.027 49	0.141 47	0.240 47	0.005 56	0.310 49	0.129 51	0.153 56	0.081 49
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
MaskRCNN 2d->3d Proj	0.022 57	0.185 54	0.000 57	0.000 57	0.015 55	0.000 57	0.000 47	0.006 57	0.000 56	0.010 57	0.006 57	0.107 39	0.012 56	0.000 56	0.002 57	0.027 57	0.004 57	0.022 57	0.001 57

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

Virtual MVFusion (R)	0.745 1	0.861 1	0.839 1	0.881 1	0.672 1	0.512 1	0.422 15	0.898 1	0.723 1	0.714 1	0.954 2	0.454 1	0.509 1	0.773 1	0.895 1	0.756 1	0.820 1	0.653 1	0.935 1	0.891 1	0.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
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
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

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

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

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)
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

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

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

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

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

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

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

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

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

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

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
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

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
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
Enet (reimpl)	0.376 22	0.264 23	0.452 23	0.452 13	0.365 21	0.181 21	0.143 23	0.456 22	0.409 22	0.346 22	0.769 23	0.164 21	0.218 21	0.359 22	0.123 23	0.403 23	0.381 23	0.313 23	0.571 22	0.685 21	0.472 20
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
ScanNet (2d proj)	0.330 23	0.293 22	0.521 22	0.657 6	0.361 22	0.161 22	0.250 22	0.004 23	0.440 20	0.183 23	0.836 20	0.125 22	0.060 23	0.319 23	0.132 22	0.417 22	0.412 22	0.344 22	0.541 23	0.427 23	0.109 23
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
DMMF	0.003 24	0.000 24	0.005 24	0.000 24	0.000 24	0.037 24	0.001 24	0.000 24	0.001 24	0.005 24	0.003 24	0.000 24	0.000 24	0.000 24	0.000 24	0.000 24	0.002 24	0.001 24	0.000 24	0.006 24	0.000 24

Method	avg 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

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