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

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.
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

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

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
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
LGround	0.272 6	0.485 6	0.184 6	0.106 6	0.778 6	0.676 3	0.932 6	0.479 8	0.572 6	0.718 5	0.399 5	0.265 6	0.453 7	0.085 2	0.745 6	0.446 6	0.726 4	0.232 6	0.622 6	0.901 6	0.512 5	0.826 6	0.786 7	0.178 7	0.549 4	0.277 6	0.659 6	0.381 6	0.518 5	0.295 8	0.323 5	0.777 5	0.599 4	0.028 4	0.321 4	0.363 7	0.000 1	0.708 6	0.858 6	0.746 5	0.063 6	0.022 3	0.457 6	0.077 5	0.476 3	0.243 6	0.402 5	0.397 8	0.233 4	0.077 8	0.720 8	0.610 7	0.103 2	0.629 5	0.437 8	0.626 5	0.446 5	0.702 3	0.190 5	0.005 1	0.058 7	0.322 6	0.702 7	0.244 6	0.768 6	0.000 1	0.134 5	0.552 6	0.279 7	0.395 6	0.147 7	0.000 1	0.207 6	0.612 5	0.000 4	0.000 4	0.000 1	0.000 3	0.658 5	0.566 6	0.323 6	0.525 8	0.229 6	0.179 5	0.467 8	0.154 7	0.000 3	0.002 1	0.000 1	0.051 1	0.000 2	0.127 2	0.703 6	0.000 1	0.000 6	0.216 1	0.112 7	0.358 4	0.547 1	0.187 2	0.092 7	0.156 8	0.055 5	0.296 6	0.252 4	0.143 2	0.000 3	0.014 5	0.398 3	0.000 1	0.028 7	0.173 2	0.000 7	0.265 7	0.348 6	0.415 8	0.179 2	0.019 7	0.218 4	0.000 1	0.597 4	0.274 8	0.565 4	0.000 1	0.012 3	0.000 3	0.039 7	0.022 2	0.000 3	0.117 6	0.000 3	0.000 2	0.000 4	0.000 1	0.000 1	0.324 7	0.000 1	0.384 4	0.000 1	0.000 4	0.251 8	0.000 1	0.566 5	0.000 1	0.000 1	0.066 6	0.404 1	0.886 7	0.199 1	0.000 3	0.000 1	0.059 2	0.000 1	0.136 1	0.540 1	0.127 8	0.295 6	0.085 5	0.143 5	0.514 2	0.413 6	0.000 6	0.000 1	0.498 3	0.000 1	0.000 2	0.000 4	0.623 5	0.000 2	0.000 1	0.000 1	0.132 7	0.000 2	0.000 2	0.000 3	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrain	0.249 8	0.455 8	0.171 7	0.079 8	0.766 8	0.659 6	0.930 8	0.494 5	0.542 8	0.700 8	0.314 8	0.215 8	0.430 8	0.121 1	0.697 8	0.441 7	0.683 7	0.235 5	0.609 8	0.895 7	0.476 8	0.816 7	0.770 8	0.186 5	0.634 2	0.216 8	0.734 4	0.340 7	0.471 7	0.307 7	0.293 8	0.591 8	0.542 7	0.076 3	0.205 7	0.464 6	0.000 1	0.484 8	0.832 8	0.766 4	0.052 7	0.000 5	0.413 7	0.059 7	0.418 7	0.222 7	0.318 8	0.609 7	0.206 7	0.112 4	0.743 5	0.625 5	0.076 4	0.579 8	0.548 5	0.590 7	0.371 7	0.552 8	0.081 7	0.003 2	0.142 5	0.201 8	0.638 8	0.233 7	0.686 8	0.000 1	0.142 4	0.444 8	0.375 4	0.247 8	0.198 5	0.000 1	0.128 8	0.454 8	0.019 2	0.097 1	0.000 1	0.000 3	0.553 7	0.557 7	0.373 4	0.545 7	0.164 8	0.014 8	0.547 7	0.174 6	0.000 3	0.002 1	0.000 1	0.037 2	0.000 2	0.063 5	0.664 8	0.000 1	0.000 6	0.130 2	0.170 5	0.152 8	0.335 7	0.079 6	0.110 6	0.175 5	0.098 4	0.175 8	0.166 7	0.045 8	0.207 1	0.014 5	0.465 2	0.000 1	0.001 8	0.001 8	0.046 4	0.299 6	0.327 7	0.537 5	0.033 7	0.012 8	0.186 6	0.000 1	0.205 7	0.377 5	0.463 7	0.000 1	0.058 2	0.000 3	0.055 6	0.041 1	0.000 3	0.105 7	0.000 3	0.000 2	0.000 4	0.000 1	0.000 1	0.398 6	0.000 1	0.308 8	0.000 1	0.000 4	0.319 6	0.000 1	0.543 6	0.000 1	0.000 1	0.062 7	0.004 6	0.862 8	0.000 4	0.000 3	0.000 1	0.000 4	0.000 1	0.123 2	0.316 7	0.225 6	0.250 7	0.094 2	0.180 4	0.332 7	0.441 3	0.000 6	0.000 1	0.310 8	0.000 1	0.000 2	0.000 4	0.592 6	0.000 2	0.000 1	0.000 1	0.203 1	0.000 2	0.000 2	0.000 3	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021

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

Method	avg ap 50%	head ap 50%	common ap 50%	tail ap 50%	chair	table	door	couch	cabinet	shelf	desk	office chair	bed	pillow	sink	picture	window	toilet	bookshelf	monitor	curtain	book	coffee table	box	refrigerator	lamp	kitchen cabinet	towel	clothes	tv	nightstand	counter	dresser	stool	cushion	plant	ceiling	bathtub	end table	dining table	keyboard	bag	backpack	toilet paper	printer	tv stand	whiteboard	blanket	shower curtain	trash can	closet	stairs	microwave	stove	shoe	computer tower	bottle	bin	bench	board	washing machine	mirror	copier	basket	sofa chair	file cabinet	fan	laptop	shower	paper	person	paper towel dispenser	oven	blinds	rack	plate	blackboard	piano	suitcase	radiator	recycling bin	wardrobe	soap dispenser	telephone	bucket	clock	stand	light	laundry basket	pipe	clothes dryer	seat	speaker	column	bicycle	ladder	bathroom stall	shower wall	cup	jacket	storage bin	coffee maker	dishwasher	paper towel roll	machine	mat	windowsill	bar	toaster	bulletin board	ironing board	kitchen counter	doorframe	toilet paper dispenser	mini fridge	fire extinguisher	ball	hat	shower curtain rod	water cooler	paper cutter	tray	ledge	mouse	cart	storage container	scale	tissue box	light switch	power outlet	decoration	sign	projector	vacuum cleaner	candle	plunger	stuffed animal	headphones	dish rack	broom	range hood	water bottle	vent	shower floor	water pitcher	mailbox	bowl	paper bag	music stand	projector screen	laundry detergent	object	bathroom vanity	laundry hamper	bathroom stall door	ceiling light	trash bin	dumbbell	stair rail	tube	bathroom cabinet	closet rod	coffee kettle	structure	shower head	keyboard piano	case of water bottles	coat rack	storage organizer	folded chair	fire alarm	power strip	calendar	poster

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

Mask3D Scannet200	0.388 1	0.542 1	0.357 1	0.237 1	0.808 2	0.676 2	0.741 1	0.832 4	0.496 1	0.151 3	0.628 2	0.021 2	0.955 1	0.578 1	0.753 1	0.612 1	0.591 1	0.822 5	0.609 3	0.926 1	0.614 3	0.291 1	0.725 4	0.163 1	0.890 2	0.380 5	0.615 1	0.517 1	0.130 3	0.806 1	0.857 2	0.024 2	0.511 1	0.412 5	0.226 1	0.597 2	0.756 1	1.000 1	0.111 1	0.792 1	0.736 1	0.091 1	0.610 1	0.527 2	0.323 4	1.000 1	0.504 1	0.063 2	1.000 1	0.853 1	0.010 1	0.974 3	0.839 1	0.667 1	0.301 1	0.883 1	0.266 1	0.039 1	0.640 1	0.311 2	0.739 2	0.463 1	1.000 1	0.000 1	0.287 2	0.715 2	0.313 2	0.600 1	1.000 1	0.027 1	0.076 4	0.502 5	0.500 1	0.409 1	0.000 1	0.194 1	0.125 2	0.500 1	0.491 1	0.748 1	0.050 4	0.042 2	0.776 2	0.352 1	0.008 1	0.000 1	0.033 1	0.254 1	0.000 1	0.005 2	0.552 1	0.008 2	0.020 2	0.750 1	0.500 1	0.409 2	0.065 3	0.511 1	0.107 1	0.178 2	0.000 1	1.000 1	0.400 1	0.016 1	0.000 1	0.400 1	0.571 1	0.000 1	0.060 2	0.044 2	0.000 1	0.514 1	0.278 1	1.000 1	0.258 1	0.017 3	0.125 5	0.000 1	0.792 3	0.399 3	1.000 1	0.000 1	0.013 2	0.265 1	0.018 2	0.000 2	1.000 1	0.335 1	0.381 1	0.500 1	0.250 1	0.004 2	0.000 1	0.727 1	0.000 1	0.497 1	0.000 1	0.188 1	0.677 2	0.000 1	0.708 2	0.000 1	0.000 1	0.945 1	0.391 1	0.123 4	0.000 1	0.028 1	0.000 1	1.000 1	0.000 1	0.099 1	0.451 1	0.400 1	0.668 1	0.573 1	0.606 1	0.077 5	0.003 4	0.004 1	0.000 1	0.042 3	0.000 1	0.000 1	1.000 1	1.000 1	0.000 1	0.042 1	0.000 2	0.200 2	0.302 1	0.000 2	1.000 1	0.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
LGround Inst.	0.246 3	0.413 3	0.170 3	0.130 3	0.754 3	0.541 3	0.682 4	0.903 1	0.264 4	0.164 2	0.234 3	0.000 3	0.681 4	0.452 3	0.464 5	0.541 4	0.399 3	1.000 1	0.637 2	0.772 3	0.588 4	0.190 2	0.589 5	0.081 3	0.857 3	0.426 3	0.373 3	0.318 3	0.135 2	0.690 2	0.653 4	0.000 3	0.159 4	0.500 2	0.000 3	0.581 3	0.387 4	1.000 1	0.046 3	0.000 3	0.402 3	0.003 5	0.455 5	0.196 4	0.571 2	1.000 1	0.270 3	0.003 5	0.530 5	0.748 3	0.000 2	0.744 4	0.575 3	0.511 3	0.112 3	0.815 2	0.067 3	0.000 3	0.400 3	0.167 3	0.667 3	0.241 2	1.000 1	0.000 1	0.208 4	0.660 3	0.125 4	0.317 2	0.000 5	0.000 2	0.100 2	0.561 4	0.000 3	0.000 2	0.000 1	0.000 2	1.000 1	0.500 1	0.344 2	0.568 4	0.167 3	0.000 3	0.706 3	0.068 3	0.000 2	0.000 1	0.000 3	0.063 3	0.000 1	0.000 3	0.056 4	0.000 3	0.000 3	0.500 2	0.000 2	0.143 5	0.017 4	0.125 3	0.097 2	0.164 3	0.000 1	0.582 4	0.400 1	0.000 2	0.000 1	0.000 4	0.083 4	0.000 1	0.000 3	0.000 3	0.000 1	0.025 3	0.156 3	0.533 3	0.250 2	0.200 2	0.500 1	0.000 1	1.000 1	0.333 4	1.000 1	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.333 2	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.400 3	0.000 1	0.364 2	0.000 1	0.000 3	0.500 3	0.000 1	0.511 4	0.000 1	0.000 1	0.286 3	0.333 2	0.000 5	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.034 3	0.111 5	0.000 3	0.333 4	0.031 5	0.000 4	0.750 1	0.125 2	0.000 2	0.000 1	0.151 2	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.000 5	0.000 2	0.000 2	0.000 2	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.	0.203 5	0.369 4	0.134 5	0.078 5	0.706 4	0.382 4	0.693 3	0.845 3	0.221 5	0.150 4	0.158 4	0.000 3	0.746 2	0.369 4	0.545 4	0.595 2	0.387 4	0.997 3	0.413 5	0.720 5	0.636 2	0.165 3	0.732 3	0.070 4	0.851 4	0.402 4	0.251 4	0.313 4	0.123 4	0.583 4	0.696 3	0.000 3	0.051 5	0.500 2	0.000 3	0.500 4	0.372 5	0.667 4	0.009 4	0.000 3	0.307 5	0.003 4	0.479 4	0.107 5	0.226 5	0.903 4	0.109 5	0.031 3	0.981 3	0.726 5	0.000 2	0.522 5	0.669 2	0.282 5	0.052 5	0.778 5	0.000 4	0.000 3	0.400 3	0.074 4	0.333 4	0.218 4	1.000 1	0.000 1	0.250 3	0.406 5	0.118 5	0.317 2	0.100 3	0.000 2	0.191 1	0.596 2	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.500 1	0.178 5	0.701 2	0.000 5	0.000 3	0.522 5	0.018 5	0.000 2	0.000 1	0.000 3	0.060 4	0.000 1	0.000 3	0.033 5	0.000 3	0.000 3	0.000 4	0.000 2	0.281 3	0.100 2	0.000 5	0.090 4	0.133 4	0.000 1	0.422 5	0.050 4	0.000 2	0.000 1	0.200 3	0.000 5	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.123 4	0.677 2	0.021 4	0.000 4	0.500 1	0.000 1	0.500 4	0.442 2	0.125 5	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.056 4	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.200 5	0.000 1	0.143 5	0.000 1	0.000 3	0.250 5	0.000 1	0.511 4	0.000 1	0.000 1	0.286 3	0.083 4	0.396 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.025 4	0.300 2	0.000 3	0.371 3	0.070 2	0.000 4	0.385 3	0.000 5	0.000 2	0.000 1	0.000 5	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.200 2	0.000 2	0.000 2	0.000 2	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.	0.209 4	0.361 5	0.157 4	0.085 4	0.700 5	0.248 5	0.634 5	0.776 5	0.322 2	0.135 5	0.103 5	0.000 3	0.524 5	0.364 5	0.618 2	0.592 3	0.381 5	0.997 3	0.589 4	0.747 4	0.340 5	0.109 5	0.768 2	0.059 5	0.702 5	0.448 2	0.188 5	0.149 5	0.091 5	0.636 3	0.573 5	0.000 3	0.246 3	0.500 2	0.000 3	0.450 5	0.405 3	0.667 4	0.006 5	0.000 3	0.356 4	0.007 3	0.506 2	0.420 3	0.340 3	0.667 5	0.294 2	0.004 4	0.571 4	0.748 2	0.000 2	1.000 1	0.573 4	0.502 4	0.094 4	0.807 3	0.000 4	0.000 3	0.400 3	0.000 5	0.278 5	0.228 3	1.000 1	0.000 1	0.115 5	0.432 4	0.198 3	0.050 5	0.125 2	0.000 2	0.000 5	0.573 3	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.125 4	0.312 4	0.610 3	0.221 1	0.000 3	0.667 4	0.050 4	0.000 2	0.000 1	0.000 3	0.032 5	0.000 1	0.000 3	0.083 3	0.000 3	0.000 3	0.000 4	0.000 2	0.220 4	0.000 5	0.125 3	0.000 5	0.111 5	0.000 1	0.667 2	0.200 3	0.000 2	0.000 1	0.000 4	0.110 3	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.053 5	0.500 4	0.000 5	0.000 4	0.500 1	0.000 1	0.500 4	0.333 4	0.500 4	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.000 5	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.600 2	0.000 1	0.364 2	0.000 1	0.000 3	0.750 1	0.000 1	0.833 1	0.000 1	0.000 1	0.143 5	0.000 5	0.396 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.021 5	0.221 4	0.000 3	0.093 5	0.055 4	0.451 2	0.677 2	0.125 2	0.000 2	0.000 1	0.028 4	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.050 4	0.000 2	0.000 2	0.000 2	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021

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

Swin3D	0.779 2	0.861 16	0.818 11	0.836 15	0.790 1	0.875 2	0.576 3	0.905 3	0.704 3	0.739 1	0.969 7	0.611 1	0.349 6	0.756 15	0.958 1	0.702 34	0.805 12	0.708 6	0.916 24	0.898 1	0.801 1

PPT-SpUNet-Joint	0.766 3	0.932 2	0.794 25	0.829 17	0.751 13	0.854 11	0.540 14	0.903 4	0.630 26	0.672 8	0.963 9	0.565 15	0.357 4	0.788 2	0.900 7	0.737 19	0.802 13	0.685 12	0.950 2	0.887 2	0.780 2

MSP	0.748 13	0.623 83	0.804 19	0.859 3	0.745 16	0.824 37	0.501 28	0.912 2	0.690 7	0.685 4	0.956 18	0.567 14	0.320 16	0.768 9	0.918 4	0.720 25	0.802 13	0.676 16	0.921 21	0.881 6	0.779 3

OctFormer	0.766 3	0.925 3	0.808 17	0.849 6	0.786 2	0.846 17	0.566 6	0.876 11	0.690 7	0.674 7	0.960 11	0.576 11	0.226 56	0.753 17	0.904 5	0.777 6	0.815 6	0.722 4	0.923 20	0.877 8	0.776 4
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
DMF-Net	0.752 9	0.906 8	0.793 27	0.802 33	0.689 30	0.825 35	0.556 8	0.867 13	0.681 10	0.602 34	0.960 11	0.555 20	0.365 3	0.779 4	0.859 20	0.747 16	0.795 21	0.717 5	0.917 23	0.856 22	0.764 5

OA-CNN-L_ScanNet20	0.756 8	0.783 38	0.826 5	0.858 4	0.776 4	0.837 24	0.548 11	0.896 7	0.649 19	0.675 6	0.962 10	0.586 9	0.335 10	0.771 8	0.802 39	0.770 9	0.787 25	0.691 10	0.936 12	0.880 7	0.761 6

EQ-Net	0.743 18	0.620 84	0.799 22	0.849 6	0.730 20	0.822 39	0.493 35	0.897 6	0.664 14	0.681 5	0.955 21	0.562 17	0.378 1	0.760 13	0.903 6	0.738 18	0.801 15	0.673 19	0.907 29	0.877 8	0.745 7
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
O-CNN	0.762 7	0.924 4	0.823 6	0.844 11	0.770 5	0.852 13	0.577 2	0.847 20	0.711 1	0.640 20	0.958 14	0.592 5	0.217 62	0.762 12	0.888 12	0.758 13	0.813 8	0.726 1	0.932 17	0.868 13	0.744 8
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
CU-Hybrid Net	0.764 5	0.924 4	0.819 9	0.840 13	0.757 9	0.853 12	0.580 1	0.848 18	0.709 2	0.643 16	0.958 14	0.587 8	0.295 26	0.753 17	0.884 15	0.758 13	0.815 6	0.725 2	0.927 19	0.867 14	0.743 9

StratifiedFormer	0.747 14	0.901 9	0.803 20	0.845 10	0.757 9	0.846 17	0.512 24	0.825 27	0.696 6	0.645 14	0.956 18	0.576 11	0.262 47	0.744 22	0.861 19	0.742 17	0.770 34	0.705 7	0.899 37	0.860 19	0.734 10
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
SAT	0.742 19	0.860 17	0.765 39	0.819 20	0.769 6	0.848 16	0.533 16	0.829 25	0.663 15	0.631 22	0.955 21	0.586 9	0.274 37	0.753 17	0.896 9	0.729 20	0.760 41	0.666 21	0.921 21	0.855 24	0.733 11

PicassoNet-II	0.692 35	0.732 61	0.772 35	0.786 37	0.677 34	0.866 6	0.517 23	0.848 18	0.509 69	0.626 23	0.952 33	0.536 25	0.225 58	0.545 64	0.704 56	0.689 40	0.810 10	0.564 60	0.903 33	0.854 26	0.729 12
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Virtual MVFusion	0.746 15	0.771 44	0.819 9	0.848 8	0.702 28	0.865 7	0.397 74	0.899 5	0.699 4	0.664 11	0.948 45	0.588 7	0.330 12	0.746 21	0.851 27	0.764 11	0.796 18	0.704 8	0.935 13	0.866 15	0.728 13
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
IPCA	0.731 24	0.890 10	0.837 3	0.864 2	0.726 22	0.873 3	0.530 19	0.824 28	0.489 77	0.647 13	0.978 2	0.609 3	0.336 9	0.624 41	0.733 49	0.758 13	0.776 29	0.570 56	0.949 3	0.877 8	0.728 13

OccuSeg+Semantic	0.764 5	0.758 50	0.796 23	0.839 14	0.746 15	0.907 1	0.562 7	0.850 17	0.680 11	0.672 8	0.978 2	0.610 2	0.335 10	0.777 5	0.819 35	0.847 1	0.830 1	0.691 10	0.972 1	0.885 4	0.727 15

MinkowskiNet	0.736 23	0.859 18	0.818 11	0.832 16	0.709 26	0.840 22	0.521 22	0.853 16	0.660 17	0.643 16	0.951 35	0.544 23	0.286 31	0.731 23	0.893 10	0.675 43	0.772 31	0.683 13	0.874 55	0.852 27	0.727 15
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
Mix3D	0.781 1	0.964 1	0.855 1	0.843 12	0.781 3	0.858 9	0.575 4	0.831 24	0.685 9	0.714 2	0.979 1	0.594 4	0.310 20	0.801 1	0.892 11	0.841 2	0.819 3	0.723 3	0.940 9	0.887 2	0.725 17
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
BPNet	0.749 11	0.909 6	0.818 11	0.811 25	0.752 11	0.839 23	0.485 37	0.842 21	0.673 12	0.644 15	0.957 17	0.528 29	0.305 22	0.773 7	0.859 20	0.788 4	0.818 5	0.693 9	0.916 24	0.856 22	0.723 18
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointTransformerV2	0.752 9	0.742 57	0.809 16	0.872 1	0.758 8	0.860 8	0.552 9	0.891 8	0.610 34	0.687 3	0.960 11	0.559 18	0.304 23	0.766 10	0.926 3	0.767 10	0.797 17	0.644 26	0.942 7	0.876 11	0.722 19
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
LRPNet	0.742 19	0.816 30	0.806 18	0.807 28	0.752 11	0.828 33	0.575 4	0.839 23	0.699 4	0.637 21	0.954 26	0.520 31	0.320 16	0.755 16	0.834 31	0.760 12	0.772 31	0.676 16	0.915 26	0.862 17	0.717 20

Retro-FPN	0.744 17	0.842 22	0.800 21	0.767 45	0.740 17	0.836 26	0.541 13	0.914 1	0.672 13	0.626 23	0.958 14	0.552 21	0.272 39	0.777 5	0.886 14	0.696 35	0.801 15	0.674 18	0.941 8	0.858 20	0.717 20

PointConvFormer	0.749 11	0.793 35	0.790 28	0.807 28	0.750 14	0.856 10	0.524 20	0.881 10	0.588 45	0.642 19	0.977 4	0.591 6	0.274 37	0.781 3	0.929 2	0.804 3	0.796 18	0.642 27	0.947 4	0.885 4	0.715 22
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
VMNet	0.746 15	0.870 14	0.838 2	0.858 4	0.729 21	0.850 15	0.501 28	0.874 12	0.587 46	0.658 12	0.956 18	0.564 16	0.299 24	0.765 11	0.900 7	0.716 28	0.812 9	0.631 32	0.939 10	0.858 20	0.709 23
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
PointTransformer++	0.725 25	0.727 65	0.811 15	0.819 20	0.765 7	0.841 21	0.502 27	0.814 33	0.621 29	0.623 25	0.955 21	0.556 19	0.284 32	0.620 42	0.866 17	0.781 5	0.757 44	0.648 24	0.932 17	0.862 17	0.709 23

PointASNL	0.666 47	0.703 71	0.781 32	0.751 51	0.655 39	0.830 30	0.471 41	0.769 43	0.474 80	0.537 51	0.951 35	0.475 44	0.279 35	0.635 36	0.698 59	0.675 43	0.751 46	0.553 66	0.816 77	0.806 49	0.703 25
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
MatchingNet	0.724 27	0.812 32	0.812 14	0.810 26	0.735 19	0.834 28	0.495 34	0.860 15	0.572 52	0.602 34	0.954 26	0.512 33	0.280 34	0.757 14	0.845 29	0.725 22	0.780 27	0.606 42	0.937 11	0.851 28	0.700 26

TXC	0.740 21	0.842 22	0.832 4	0.805 32	0.715 25	0.846 17	0.473 39	0.885 9	0.615 30	0.671 10	0.971 6	0.547 22	0.320 16	0.697 26	0.799 41	0.777 6	0.819 3	0.682 14	0.946 5	0.871 12	0.696 27

LargeKernel3D	0.739 22	0.909 6	0.820 8	0.806 30	0.740 17	0.852 13	0.545 12	0.826 26	0.594 44	0.643 16	0.955 21	0.541 24	0.263 46	0.723 24	0.858 22	0.775 8	0.767 35	0.678 15	0.933 15	0.848 29	0.694 28
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
contrastBoundary	0.705 30	0.769 47	0.775 34	0.809 27	0.687 31	0.820 42	0.439 62	0.812 34	0.661 16	0.591 40	0.945 53	0.515 32	0.171 80	0.633 38	0.856 23	0.720 25	0.796 18	0.668 20	0.889 44	0.847 30	0.689 29
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
DGNet	0.684 39	0.712 69	0.784 30	0.782 41	0.658 37	0.835 27	0.499 32	0.823 29	0.641 21	0.597 37	0.950 39	0.487 39	0.281 33	0.575 52	0.619 68	0.647 56	0.764 37	0.620 37	0.871 61	0.846 32	0.688 30

SparseConvNet	0.725 25	0.647 80	0.821 7	0.846 9	0.721 23	0.869 4	0.533 16	0.754 47	0.603 40	0.614 27	0.955 21	0.572 13	0.325 14	0.710 25	0.870 16	0.724 23	0.823 2	0.628 33	0.934 14	0.865 16	0.683 31

One Thing One Click	0.701 32	0.825 28	0.796 23	0.723 52	0.716 24	0.832 29	0.433 64	0.816 30	0.634 24	0.609 29	0.969 7	0.418 71	0.344 7	0.559 58	0.833 32	0.715 29	0.808 11	0.560 61	0.902 34	0.847 30	0.680 32

PointMetaBase	0.714 29	0.835 24	0.785 29	0.821 18	0.684 32	0.846 17	0.531 18	0.865 14	0.614 31	0.596 38	0.953 29	0.500 36	0.246 52	0.674 27	0.888 12	0.692 36	0.764 37	0.624 34	0.849 70	0.844 34	0.675 33

PointContrast_LA_SEM	0.683 42	0.757 51	0.784 30	0.786 37	0.639 46	0.824 37	0.408 69	0.775 41	0.604 39	0.541 49	0.934 77	0.532 27	0.269 42	0.552 61	0.777 42	0.645 59	0.793 22	0.640 28	0.913 27	0.824 39	0.671 34

VI-PointConv	0.676 44	0.770 46	0.754 46	0.783 40	0.621 50	0.814 49	0.552 9	0.758 45	0.571 54	0.557 45	0.954 26	0.529 28	0.268 44	0.530 67	0.682 60	0.675 43	0.719 56	0.603 43	0.888 45	0.833 35	0.665 35
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
INS-Conv-semantic	0.717 28	0.751 53	0.759 42	0.812 24	0.704 27	0.868 5	0.537 15	0.842 21	0.609 36	0.608 30	0.953 29	0.534 26	0.293 27	0.616 43	0.864 18	0.719 27	0.793 22	0.640 28	0.933 15	0.845 33	0.663 36

FusionNet	0.688 37	0.704 70	0.741 55	0.754 49	0.656 38	0.829 31	0.501 28	0.741 52	0.609 36	0.548 47	0.950 39	0.522 30	0.371 2	0.633 38	0.756 44	0.715 29	0.771 33	0.623 35	0.861 66	0.814 45	0.658 37
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
PPCNN++	0.663 49	0.746 54	0.708 63	0.722 53	0.638 47	0.820 42	0.451 48	0.566 84	0.599 42	0.541 49	0.950 39	0.510 34	0.313 19	0.648 33	0.819 35	0.616 67	0.682 72	0.590 50	0.869 62	0.810 48	0.656 38
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
VACNN++	0.684 39	0.728 64	0.757 45	0.776 42	0.690 29	0.804 56	0.464 45	0.816 30	0.577 51	0.587 41	0.945 53	0.508 35	0.276 36	0.671 28	0.710 54	0.663 48	0.750 47	0.589 51	0.881 49	0.832 36	0.653 39

JSENet	0.699 33	0.881 13	0.762 40	0.821 18	0.667 36	0.800 59	0.522 21	0.792 39	0.613 32	0.607 31	0.935 73	0.492 38	0.205 67	0.576 51	0.853 25	0.691 37	0.758 43	0.652 23	0.872 58	0.828 37	0.649 40
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click	0.693 34	0.743 56	0.794 25	0.655 76	0.684 32	0.822 39	0.497 33	0.719 57	0.622 28	0.617 26	0.977 4	0.447 58	0.339 8	0.750 20	0.664 65	0.703 33	0.790 24	0.596 46	0.946 5	0.855 24	0.647 41
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SAFNet-seg	0.654 52	0.752 52	0.734 57	0.664 74	0.583 63	0.815 48	0.399 73	0.754 47	0.639 22	0.535 53	0.942 62	0.470 46	0.309 21	0.665 29	0.539 74	0.650 52	0.708 61	0.635 30	0.857 68	0.793 59	0.642 42
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
PointConv	0.666 47	0.781 39	0.759 42	0.699 61	0.644 45	0.822 39	0.475 38	0.779 40	0.564 57	0.504 65	0.953 29	0.428 65	0.203 69	0.586 49	0.754 45	0.661 49	0.753 45	0.588 52	0.902 34	0.813 47	0.642 42
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
DCM-Net	0.658 50	0.778 40	0.702 66	0.806 30	0.619 51	0.813 52	0.468 42	0.693 65	0.494 72	0.524 57	0.941 64	0.449 57	0.298 25	0.510 71	0.821 34	0.675 43	0.727 55	0.568 58	0.826 75	0.803 51	0.637 44
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
DenSeR	0.628 68	0.800 33	0.625 89	0.719 55	0.545 74	0.806 54	0.445 55	0.597 79	0.448 86	0.519 60	0.938 69	0.481 41	0.328 13	0.489 77	0.499 81	0.657 51	0.759 42	0.592 49	0.881 49	0.797 55	0.634 45

KP-FCNN	0.684 39	0.847 21	0.758 44	0.784 39	0.647 42	0.814 49	0.473 39	0.772 42	0.605 38	0.594 39	0.935 73	0.450 56	0.181 78	0.587 47	0.805 38	0.690 38	0.785 26	0.614 38	0.882 48	0.819 44	0.632 46
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
Superpoint Network	0.683 42	0.851 20	0.728 59	0.800 35	0.653 40	0.806 54	0.468 42	0.804 35	0.572 52	0.602 34	0.946 50	0.453 55	0.239 55	0.519 69	0.822 33	0.689 40	0.762 40	0.595 48	0.895 40	0.827 38	0.630 47

FusionAwareConv	0.630 67	0.604 87	0.741 55	0.766 46	0.590 59	0.747 81	0.501 28	0.734 54	0.503 71	0.527 55	0.919 87	0.454 53	0.323 15	0.550 63	0.420 86	0.678 42	0.688 70	0.544 69	0.896 39	0.795 56	0.627 48
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
RandLA-Net	0.645 53	0.778 40	0.731 58	0.699 61	0.577 64	0.829 31	0.446 53	0.736 53	0.477 79	0.523 59	0.945 53	0.454 53	0.269 42	0.484 78	0.749 48	0.618 65	0.738 49	0.599 45	0.827 74	0.792 62	0.621 49

RFCR	0.702 31	0.889 11	0.745 51	0.813 23	0.672 35	0.818 46	0.493 35	0.815 32	0.623 27	0.610 28	0.947 47	0.470 46	0.249 51	0.594 46	0.848 28	0.705 32	0.779 28	0.646 25	0.892 42	0.823 40	0.611 50
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
Supervoxel-CNN	0.635 61	0.656 78	0.711 62	0.719 55	0.613 52	0.757 79	0.444 58	0.765 44	0.534 62	0.566 43	0.928 81	0.478 43	0.272 39	0.636 35	0.531 76	0.664 47	0.645 82	0.508 80	0.864 65	0.792 62	0.611 50

LAP-D	0.594 76	0.720 66	0.692 74	0.637 83	0.456 86	0.773 73	0.391 78	0.730 55	0.587 46	0.445 79	0.940 67	0.381 80	0.288 29	0.434 83	0.453 84	0.591 75	0.649 80	0.581 54	0.777 81	0.749 81	0.610 52

MVPNet	0.641 54	0.831 25	0.715 61	0.671 71	0.590 59	0.781 68	0.394 75	0.679 67	0.642 20	0.553 46	0.937 70	0.462 49	0.256 48	0.649 32	0.406 87	0.626 63	0.691 69	0.666 21	0.877 51	0.792 62	0.608 53
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
ROSMRF3D	0.673 45	0.789 36	0.748 48	0.763 47	0.635 48	0.814 49	0.407 71	0.747 49	0.581 50	0.573 42	0.950 39	0.484 40	0.271 41	0.607 44	0.754 45	0.649 53	0.774 30	0.596 46	0.883 47	0.823 40	0.606 54

Feature_GeometricNet	0.690 36	0.884 12	0.754 46	0.795 36	0.647 42	0.818 46	0.422 66	0.802 37	0.612 33	0.604 32	0.945 53	0.462 49	0.189 75	0.563 57	0.853 25	0.726 21	0.765 36	0.632 31	0.904 31	0.821 43	0.606 54
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
HPEIN	0.618 71	0.729 63	0.668 80	0.647 79	0.597 57	0.766 75	0.414 68	0.680 66	0.520 65	0.525 56	0.946 50	0.432 61	0.215 63	0.493 76	0.599 70	0.638 60	0.617 87	0.570 56	0.897 38	0.806 49	0.605 56
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
Feature-Geometry Net	0.685 38	0.866 15	0.748 48	0.819 20	0.645 44	0.794 62	0.450 51	0.802 37	0.587 46	0.604 32	0.945 53	0.464 48	0.201 70	0.554 60	0.840 30	0.723 24	0.732 53	0.602 44	0.907 29	0.822 42	0.603 57

CCRFNet	0.589 78	0.766 48	0.659 84	0.683 66	0.470 85	0.740 83	0.387 79	0.620 78	0.490 75	0.476 71	0.922 85	0.355 85	0.245 53	0.511 70	0.511 79	0.571 80	0.643 83	0.493 84	0.872 58	0.762 76	0.600 58

joint point-based	0.634 62	0.614 85	0.778 33	0.667 73	0.633 49	0.825 35	0.420 67	0.804 35	0.467 82	0.561 44	0.951 35	0.494 37	0.291 28	0.566 55	0.458 82	0.579 79	0.764 37	0.559 63	0.838 72	0.814 45	0.598 59
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
HPGCNN	0.656 51	0.698 72	0.743 53	0.650 77	0.564 68	0.820 42	0.505 26	0.758 45	0.631 25	0.479 70	0.945 53	0.480 42	0.226 56	0.572 53	0.774 43	0.690 38	0.735 51	0.614 38	0.853 69	0.776 72	0.597 60
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SConv	0.636 60	0.830 26	0.697 70	0.752 50	0.572 67	0.780 70	0.445 55	0.716 58	0.529 63	0.530 54	0.951 35	0.446 59	0.170 81	0.507 73	0.666 64	0.636 61	0.682 72	0.541 72	0.886 46	0.799 52	0.594 61

DPC	0.592 77	0.720 66	0.700 68	0.602 87	0.480 82	0.762 78	0.380 81	0.713 61	0.585 49	0.437 80	0.940 67	0.369 82	0.288 29	0.434 83	0.509 80	0.590 77	0.639 85	0.567 59	0.772 82	0.755 79	0.592 62
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
APCF-Net	0.631 64	0.742 57	0.687 79	0.672 69	0.557 71	0.792 65	0.408 69	0.665 70	0.545 60	0.508 62	0.952 33	0.428 65	0.186 76	0.634 37	0.702 57	0.620 64	0.706 62	0.555 65	0.873 56	0.798 54	0.581 63
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointMTL	0.632 63	0.731 62	0.688 77	0.675 68	0.591 58	0.784 67	0.444 58	0.565 85	0.610 34	0.492 67	0.949 43	0.456 52	0.254 49	0.587 47	0.706 55	0.599 71	0.665 78	0.612 41	0.868 63	0.791 66	0.579 64

PointMRNet	0.640 56	0.717 68	0.701 67	0.692 64	0.576 65	0.801 58	0.467 44	0.716 58	0.563 58	0.459 75	0.953 29	0.429 64	0.169 82	0.581 50	0.854 24	0.605 68	0.710 58	0.550 67	0.894 41	0.793 59	0.575 65

SIConv	0.625 70	0.830 26	0.694 72	0.757 48	0.563 69	0.772 74	0.448 52	0.647 73	0.520 65	0.509 61	0.949 43	0.431 63	0.191 74	0.496 75	0.614 69	0.647 56	0.672 76	0.535 75	0.876 52	0.783 68	0.571 66

SALANet	0.670 46	0.816 30	0.770 37	0.768 44	0.652 41	0.807 53	0.451 48	0.747 49	0.659 18	0.545 48	0.924 83	0.473 45	0.149 90	0.571 54	0.811 37	0.635 62	0.746 48	0.623 35	0.892 42	0.794 57	0.570 67

TextureNet	0.566 82	0.672 77	0.664 82	0.671 71	0.494 80	0.719 84	0.445 55	0.678 68	0.411 92	0.396 85	0.935 73	0.356 84	0.225 58	0.412 86	0.535 75	0.565 81	0.636 86	0.464 88	0.794 80	0.680 92	0.568 68
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
FPConv	0.639 57	0.785 37	0.760 41	0.713 59	0.603 54	0.798 60	0.392 76	0.534 89	0.603 40	0.524 57	0.948 45	0.457 51	0.250 50	0.538 65	0.723 52	0.598 72	0.696 67	0.614 38	0.872 58	0.799 52	0.567 69
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PointSPNet	0.637 59	0.734 60	0.692 74	0.714 58	0.576 65	0.797 61	0.446 53	0.743 51	0.598 43	0.437 80	0.942 62	0.403 75	0.150 89	0.626 40	0.800 40	0.649 53	0.697 66	0.557 64	0.846 71	0.777 71	0.563 70

PanopticFusion-label	0.529 86	0.491 96	0.688 77	0.604 86	0.386 91	0.632 95	0.225 100	0.705 64	0.434 89	0.293 95	0.815 98	0.348 86	0.241 54	0.499 74	0.669 62	0.507 85	0.649 80	0.442 94	0.796 79	0.602 98	0.561 71
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
PointConv-SFPN	0.641 54	0.776 42	0.703 65	0.721 54	0.557 71	0.826 34	0.451 48	0.672 69	0.563 58	0.483 69	0.943 61	0.425 68	0.162 85	0.644 34	0.726 50	0.659 50	0.709 60	0.572 55	0.875 53	0.786 67	0.559 72

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

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

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

SegGroup_sem	0.627 69	0.818 29	0.747 50	0.701 60	0.602 55	0.764 76	0.385 80	0.629 76	0.490 75	0.508 62	0.931 80	0.409 73	0.201 70	0.564 56	0.725 51	0.618 65	0.692 68	0.539 73	0.873 56	0.794 57	0.548 76
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3DMV	0.484 91	0.484 97	0.538 98	0.643 81	0.424 89	0.606 99	0.310 88	0.574 83	0.433 90	0.378 86	0.796 99	0.301 91	0.214 64	0.537 66	0.208 98	0.472 91	0.507 99	0.413 97	0.693 90	0.602 98	0.539 77
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
SD-DETR	0.576 80	0.746 54	0.609 93	0.445 98	0.517 78	0.643 94	0.366 82	0.714 60	0.456 84	0.468 73	0.870 96	0.432 61	0.264 45	0.558 59	0.674 61	0.586 78	0.688 70	0.482 86	0.739 86	0.733 84	0.537 78

DVVNet	0.562 83	0.648 79	0.700 68	0.770 43	0.586 62	0.687 88	0.333 87	0.650 71	0.514 68	0.475 72	0.906 91	0.359 83	0.223 60	0.340 90	0.442 85	0.422 94	0.668 77	0.501 81	0.708 89	0.779 69	0.534 79

SPH3D-GCN	0.610 72	0.858 19	0.772 35	0.489 94	0.532 75	0.792 65	0.404 72	0.643 75	0.570 55	0.507 64	0.935 73	0.414 72	0.046 99	0.510 71	0.702 57	0.602 70	0.705 63	0.549 68	0.859 67	0.773 73	0.534 79
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
ROSMRF	0.580 79	0.772 43	0.707 64	0.681 67	0.563 69	0.764 76	0.362 83	0.515 90	0.465 83	0.465 74	0.936 72	0.427 67	0.207 65	0.438 81	0.577 71	0.536 83	0.675 75	0.486 85	0.723 88	0.779 69	0.524 81

dtc_net	0.596 75	0.683 73	0.725 60	0.715 57	0.549 73	0.803 57	0.444 58	0.647 73	0.493 73	0.495 66	0.941 64	0.409 73	0.000 103	0.424 85	0.544 73	0.598 72	0.703 65	0.522 77	0.912 28	0.792 62	0.520 82

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

Online SegFusion	0.515 88	0.607 86	0.644 87	0.579 89	0.434 88	0.630 96	0.353 84	0.628 77	0.440 87	0.410 83	0.762 101	0.307 90	0.167 83	0.520 68	0.403 88	0.516 84	0.565 90	0.447 92	0.678 92	0.701 89	0.514 84
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
PCNN	0.498 90	0.559 90	0.644 87	0.560 91	0.420 90	0.711 86	0.229 98	0.414 92	0.436 88	0.352 91	0.941 64	0.324 89	0.155 87	0.238 97	0.387 89	0.493 86	0.529 96	0.509 78	0.813 78	0.751 80	0.504 85

3DSM_DMMF	0.631 64	0.626 82	0.745 51	0.801 34	0.607 53	0.751 80	0.506 25	0.729 56	0.565 56	0.491 68	0.866 97	0.434 60	0.197 73	0.595 45	0.630 67	0.709 31	0.705 63	0.560 61	0.875 53	0.740 82	0.491 86

AttAN	0.609 73	0.760 49	0.667 81	0.649 78	0.521 77	0.793 63	0.457 47	0.648 72	0.528 64	0.434 82	0.947 47	0.401 76	0.153 88	0.454 80	0.721 53	0.648 55	0.717 57	0.536 74	0.904 31	0.765 75	0.485 87
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer	0.600 74	0.634 81	0.743 53	0.697 63	0.601 56	0.781 68	0.437 63	0.585 82	0.493 73	0.446 77	0.933 78	0.394 77	0.011 101	0.654 31	0.661 66	0.603 69	0.733 52	0.526 76	0.832 73	0.761 77	0.480 88

3DMV, FTSDF	0.501 89	0.558 91	0.608 94	0.424 100	0.478 83	0.690 87	0.246 96	0.586 81	0.468 81	0.450 76	0.911 89	0.394 77	0.160 86	0.438 81	0.212 97	0.432 93	0.541 95	0.475 87	0.742 85	0.727 85	0.477 89

PointCNN with RGB	0.458 92	0.577 89	0.611 92	0.356 102	0.321 99	0.715 85	0.299 92	0.376 96	0.328 99	0.319 93	0.944 59	0.285 93	0.164 84	0.216 100	0.229 95	0.484 88	0.545 94	0.456 90	0.755 83	0.709 88	0.475 90
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
PD-Net	0.638 58	0.797 34	0.769 38	0.641 82	0.590 59	0.820 42	0.461 46	0.537 88	0.637 23	0.536 52	0.947 47	0.388 79	0.206 66	0.656 30	0.668 63	0.647 56	0.732 53	0.585 53	0.868 63	0.793 59	0.473 91

PNET2	0.442 95	0.548 92	0.548 97	0.597 88	0.363 95	0.628 97	0.300 90	0.292 97	0.374 94	0.307 94	0.881 95	0.268 96	0.186 76	0.238 97	0.204 99	0.407 95	0.506 100	0.449 91	0.667 93	0.620 97	0.462 92

SurfaceConvPF	0.442 95	0.505 94	0.622 91	0.380 101	0.342 97	0.654 91	0.227 99	0.397 94	0.367 95	0.276 97	0.924 83	0.240 98	0.198 72	0.359 89	0.262 93	0.366 96	0.581 88	0.435 95	0.640 94	0.668 93	0.398 93
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
DGCNN_reproduce	0.446 94	0.474 98	0.623 90	0.463 96	0.366 94	0.651 92	0.310 88	0.389 95	0.349 97	0.330 92	0.937 70	0.271 95	0.126 92	0.285 93	0.224 96	0.350 99	0.577 89	0.445 93	0.625 95	0.723 86	0.394 94
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
ScanNet+FTSDF	0.383 100	0.297 102	0.491 100	0.432 99	0.358 96	0.612 98	0.274 94	0.116 101	0.411 92	0.265 98	0.904 92	0.229 99	0.079 97	0.250 95	0.185 100	0.320 100	0.510 97	0.385 99	0.548 99	0.597 101	0.394 94

3DWSSS	0.425 98	0.525 93	0.647 85	0.522 92	0.324 98	0.488 102	0.077 103	0.712 62	0.353 96	0.401 84	0.636 103	0.281 94	0.176 79	0.340 90	0.565 72	0.175 103	0.551 93	0.398 98	0.370 103	0.602 98	0.361 96

Tangent Convolutions	0.438 97	0.437 100	0.646 86	0.474 95	0.369 93	0.645 93	0.353 84	0.258 99	0.282 101	0.279 96	0.918 88	0.298 92	0.147 91	0.283 94	0.294 92	0.487 87	0.562 91	0.427 96	0.619 96	0.633 96	0.352 97
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
subcloud_weak	0.516 87	0.676 75	0.591 96	0.609 84	0.442 87	0.774 72	0.335 86	0.597 79	0.422 91	0.357 90	0.932 79	0.341 87	0.094 95	0.298 92	0.528 78	0.473 90	0.676 74	0.495 83	0.602 97	0.721 87	0.349 98

SSC-UNet	0.308 102	0.353 101	0.290 103	0.278 103	0.166 103	0.553 100	0.169 102	0.286 98	0.147 103	0.148 103	0.908 90	0.182 102	0.064 98	0.023 103	0.018 104	0.354 98	0.363 101	0.345 101	0.546 101	0.685 91	0.278 99

SPLAT Net	0.393 99	0.472 99	0.511 99	0.606 85	0.311 100	0.656 90	0.245 97	0.405 93	0.328 99	0.197 101	0.927 82	0.227 100	0.000 103	0.001 104	0.249 94	0.271 102	0.510 97	0.383 100	0.593 98	0.699 90	0.267 100
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
PointNet++	0.339 101	0.584 88	0.478 101	0.458 97	0.256 102	0.360 103	0.250 95	0.247 100	0.278 102	0.261 99	0.677 102	0.183 101	0.117 93	0.212 101	0.145 102	0.364 97	0.346 103	0.232 103	0.548 99	0.523 102	0.252 101
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
FCPN	0.447 93	0.679 74	0.604 95	0.578 90	0.380 92	0.682 89	0.291 93	0.106 102	0.483 78	0.258 100	0.920 86	0.258 97	0.025 100	0.231 99	0.325 91	0.480 89	0.560 92	0.463 89	0.725 87	0.666 94	0.231 102
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
ScanNet	0.306 103	0.203 103	0.366 102	0.501 93	0.311 100	0.524 101	0.211 101	0.002 104	0.342 98	0.189 102	0.786 100	0.145 103	0.102 94	0.245 96	0.152 101	0.318 101	0.348 102	0.300 102	0.460 102	0.437 103	0.182 103
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 104	0.000 104	0.041 104	0.172 104	0.030 104	0.062 104	0.001 104	0.035 103	0.004 104	0.051 104	0.143 104	0.019 104	0.003 102	0.041 102	0.050 103	0.003 104	0.054 104	0.018 104	0.005 104	0.264 104	0.082 104

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

Method	avg ap 50%	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

SoftGroup++	0.769 4	1.000 1	0.803 20	0.937 1	0.684 6	0.865 7	0.213 20	0.870 2	0.664 9	0.571 10	0.758 1	0.702 4	0.807 4	1.000 1	0.653 16	0.902 1	0.792 3	1.000 1	0.626 1

Queryformer	0.787 1	1.000 1	0.933 1	0.601 34	0.754 1	0.886 4	0.558 2	0.661 25	0.767 3	0.665 4	0.716 3	0.639 11	0.808 3	1.000 1	0.844 1	0.897 2	0.804 2	1.000 1	0.624 2

DKNet	0.718 12	1.000 1	0.814 14	0.782 16	0.619 14	0.872 6	0.224 18	0.751 15	0.569 18	0.677 2	0.585 16	0.724 1	0.633 23	0.981 25	0.515 32	0.819 12	0.736 13	1.000 1	0.617 3
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
TD3D	0.751 7	1.000 1	0.774 28	0.867 7	0.621 13	0.934 1	0.404 7	0.706 19	0.812 1	0.605 8	0.633 11	0.626 12	0.690 10	1.000 1	0.640 18	0.820 11	0.777 5	1.000 1	0.612 4

Mask-Group	0.664 20	1.000 1	0.822 12	0.764 21	0.616 16	0.815 18	0.139 27	0.694 21	0.597 15	0.459 25	0.566 21	0.599 14	0.600 25	0.516 48	0.715 9	0.819 13	0.635 23	1.000 1	0.603 5
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Mask3D	0.780 2	1.000 1	0.786 27	0.716 25	0.696 5	0.885 5	0.500 4	0.714 18	0.810 2	0.672 3	0.715 4	0.679 7	0.809 2	1.000 1	0.831 2	0.833 8	0.787 4	1.000 1	0.602 6
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SphereSeg	0.680 17	1.000 1	0.856 6	0.744 22	0.618 15	0.893 2	0.151 23	0.651 27	0.713 5	0.537 15	0.579 19	0.430 29	0.651 12	1.000 1	0.389 41	0.744 26	0.697 14	0.991 37	0.601 7

PBNet	0.747 8	1.000 1	0.818 13	0.837 10	0.713 3	0.844 12	0.457 6	0.647 28	0.711 6	0.614 7	0.617 13	0.657 9	0.650 13	1.000 1	0.692 10	0.822 10	0.765 10	1.000 1	0.595 8

SoftGroup	0.761 6	1.000 1	0.808 17	0.845 8	0.716 2	0.862 9	0.243 17	0.824 4	0.655 11	0.620 6	0.734 2	0.699 5	0.791 6	0.981 25	0.716 8	0.844 5	0.769 8	1.000 1	0.594 9
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
SSEC	0.707 13	1.000 1	0.850 7	0.924 3	0.648 10	0.747 33	0.162 22	0.862 3	0.572 17	0.520 16	0.624 12	0.549 16	0.649 21	1.000 1	0.560 27	0.706 33	0.768 9	1.000 1	0.591 10

SPFormer	0.770 3	0.903 39	0.903 2	0.806 13	0.609 17	0.886 3	0.568 1	0.815 6	0.705 7	0.711 1	0.655 6	0.652 10	0.685 11	1.000 1	0.789 4	0.809 14	0.776 7	1.000 1	0.583 11
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
TopoSeg	0.725 11	1.000 1	0.806 19	0.933 2	0.668 8	0.758 30	0.272 14	0.734 17	0.630 13	0.549 14	0.654 7	0.606 13	0.697 9	0.966 27	0.612 22	0.839 6	0.754 11	1.000 1	0.573 12

Dyco3D	0.641 25	1.000 1	0.841 10	0.893 4	0.531 27	0.802 23	0.115 31	0.588 35	0.448 24	0.438 29	0.537 26	0.430 30	0.550 33	0.857 30	0.534 30	0.764 21	0.657 18	0.987 38	0.568 13
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
DENet	0.629 29	1.000 1	0.797 23	0.608 33	0.589 18	0.627 41	0.219 19	0.882 1	0.310 36	0.402 38	0.383 41	0.396 33	0.650 13	1.000 1	0.663 13	0.543 49	0.691 16	1.000 1	0.568 14

IPCA-Inst	0.731 10	1.000 1	0.788 26	0.884 6	0.698 4	0.788 27	0.252 15	0.760 13	0.646 12	0.511 18	0.637 10	0.665 8	0.804 5	1.000 1	0.644 17	0.778 17	0.747 12	1.000 1	0.561 15

HAIS	0.699 14	1.000 1	0.849 8	0.820 11	0.675 7	0.808 21	0.279 12	0.757 14	0.465 23	0.517 17	0.596 14	0.559 15	0.600 25	1.000 1	0.654 15	0.767 19	0.676 17	0.994 35	0.560 16
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
ISBNet	0.763 5	1.000 1	0.873 5	0.717 24	0.666 9	0.858 11	0.508 3	0.667 23	0.764 4	0.643 5	0.676 5	0.688 6	0.825 1	1.000 1	0.773 5	0.741 27	0.777 6	1.000 1	0.556 17
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
OSIS	0.605 31	1.000 1	0.801 21	0.599 35	0.535 26	0.728 35	0.286 11	0.436 45	0.679 8	0.491 19	0.433 34	0.256 38	0.404 45	0.857 30	0.620 21	0.724 30	0.510 43	1.000 1	0.539 18

DualGroup	0.694 16	1.000 1	0.799 22	0.811 12	0.622 12	0.817 16	0.376 9	0.805 9	0.590 16	0.487 21	0.568 20	0.525 20	0.650 13	0.835 38	0.600 23	0.829 9	0.655 19	1.000 1	0.526 19

DD-UNet+Group	0.635 28	0.667 40	0.797 24	0.714 26	0.562 22	0.774 29	0.146 24	0.810 8	0.429 26	0.476 22	0.546 25	0.399 32	0.633 23	1.000 1	0.632 19	0.722 31	0.609 25	1.000 1	0.514 20
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
PointGroup	0.636 27	1.000 1	0.765 31	0.624 32	0.505 33	0.797 24	0.116 30	0.696 20	0.384 31	0.441 27	0.559 22	0.476 22	0.596 28	1.000 1	0.666 11	0.756 23	0.556 38	0.997 30	0.513 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]
SSTNet	0.698 15	1.000 1	0.697 44	0.888 5	0.556 23	0.803 22	0.387 8	0.626 30	0.417 27	0.556 13	0.585 17	0.702 3	0.600 25	1.000 1	0.824 3	0.720 32	0.692 15	1.000 1	0.509 22
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GraphCut	0.732 9	1.000 1	0.788 25	0.724 23	0.642 11	0.859 10	0.248 16	0.787 11	0.618 14	0.596 9	0.653 8	0.722 2	0.583 31	1.000 1	0.766 6	0.861 3	0.825 1	1.000 1	0.504 23

PCJC	0.578 33	1.000 1	0.810 16	0.583 39	0.449 38	0.813 20	0.042 39	0.603 33	0.341 35	0.490 20	0.465 31	0.410 31	0.650 13	0.835 38	0.264 47	0.694 37	0.561 35	0.889 45	0.504 24

PE	0.645 23	1.000 1	0.773 30	0.798 15	0.538 25	0.786 28	0.088 34	0.799 10	0.350 34	0.435 32	0.547 24	0.545 17	0.646 22	0.933 28	0.562 26	0.761 22	0.556 39	0.997 30	0.501 25
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 24	1.000 1	0.758 35	0.582 40	0.539 24	0.826 15	0.046 38	0.765 12	0.372 32	0.436 31	0.588 15	0.539 19	0.650 13	1.000 1	0.577 24	0.750 24	0.653 21	0.997 30	0.495 26
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Box2Mask	0.677 18	1.000 1	0.847 9	0.771 18	0.509 31	0.816 17	0.277 13	0.558 37	0.482 20	0.562 12	0.640 9	0.448 25	0.700 7	1.000 1	0.666 11	0.852 4	0.578 31	0.997 30	0.488 27
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
NeuralBF	0.555 36	0.667 40	0.896 3	0.843 9	0.517 30	0.751 32	0.029 40	0.519 39	0.414 28	0.439 28	0.465 30	0.000 56	0.484 36	0.857 30	0.287 45	0.693 38	0.651 22	1.000 1	0.485 28
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
GICN	0.638 26	1.000 1	0.895 4	0.800 14	0.480 35	0.676 37	0.144 25	0.737 16	0.354 33	0.447 26	0.400 39	0.365 35	0.700 7	1.000 1	0.569 25	0.836 7	0.599 26	1.000 1	0.473 29

INS-Conv-instance	0.657 21	1.000 1	0.760 34	0.667 30	0.581 19	0.863 8	0.323 10	0.655 26	0.477 21	0.473 23	0.549 23	0.432 28	0.650 13	1.000 1	0.655 14	0.738 28	0.585 30	0.944 41	0.472 30

OccuSeg+instance	0.672 19	1.000 1	0.758 36	0.682 28	0.576 21	0.842 13	0.477 5	0.504 41	0.524 19	0.567 11	0.585 18	0.451 24	0.557 32	1.000 1	0.751 7	0.797 15	0.563 34	1.000 1	0.467 31
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
3D-BoNet	0.488 41	1.000 1	0.672 46	0.590 37	0.301 45	0.484 52	0.098 32	0.620 31	0.306 37	0.341 44	0.259 47	0.125 45	0.434 42	0.796 40	0.402 39	0.499 51	0.513 42	0.909 44	0.439 32
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
3D-MPA	0.611 30	1.000 1	0.833 11	0.765 20	0.526 28	0.756 31	0.136 29	0.588 35	0.470 22	0.438 30	0.432 36	0.358 36	0.650 13	0.857 30	0.429 37	0.765 20	0.557 37	1.000 1	0.430 33
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
RWSeg	0.567 35	0.528 50	0.708 43	0.626 31	0.580 20	0.745 34	0.063 36	0.627 29	0.240 40	0.400 39	0.497 28	0.464 23	0.515 34	1.000 1	0.475 34	0.745 25	0.571 32	1.000 1	0.429 34

AOIA	0.601 32	1.000 1	0.761 33	0.687 27	0.485 34	0.828 14	0.008 44	0.663 24	0.405 29	0.405 37	0.425 37	0.490 21	0.596 28	0.714 41	0.553 29	0.779 16	0.597 27	0.992 36	0.424 35

MTML	0.549 37	1.000 1	0.807 18	0.588 38	0.327 43	0.647 39	0.004 46	0.815 7	0.180 42	0.418 34	0.364 43	0.182 41	0.445 39	1.000 1	0.442 36	0.688 40	0.571 33	1.000 1	0.396 36
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Occipital-SCS	0.512 40	1.000 1	0.716 40	0.509 41	0.506 32	0.611 42	0.092 33	0.602 34	0.177 43	0.346 43	0.383 40	0.165 43	0.442 40	0.850 37	0.386 42	0.618 45	0.543 40	0.889 45	0.389 37

CSC-Pretrained	0.648 22	1.000 1	0.810 15	0.768 19	0.523 29	0.813 19	0.143 26	0.819 5	0.389 30	0.422 33	0.511 27	0.443 26	0.650 13	1.000 1	0.624 20	0.732 29	0.634 24	1.000 1	0.375 38

PanopticFusion-inst	0.478 42	0.667 40	0.712 42	0.595 36	0.259 48	0.550 48	0.000 49	0.613 32	0.175 44	0.250 49	0.434 33	0.437 27	0.411 44	0.857 30	0.485 33	0.591 48	0.267 54	0.944 41	0.359 39
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SSEN	0.575 34	1.000 1	0.761 32	0.473 42	0.477 36	0.795 25	0.066 35	0.529 38	0.658 10	0.460 24	0.461 32	0.380 34	0.331 47	0.859 29	0.401 40	0.692 39	0.653 20	1.000 1	0.348 40
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Sparse R-CNN	0.515 39	1.000 1	0.538 51	0.282 45	0.468 37	0.790 26	0.173 21	0.345 47	0.429 25	0.413 36	0.484 29	0.176 42	0.595 30	0.591 46	0.522 31	0.668 41	0.476 44	0.986 39	0.327 41

SALoss-ResNet	0.459 44	1.000 1	0.737 38	0.159 55	0.259 47	0.587 44	0.138 28	0.475 43	0.217 41	0.416 35	0.408 38	0.128 44	0.315 48	0.714 41	0.411 38	0.536 50	0.590 29	0.873 48	0.304 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)
SPG_WSIS	0.470 43	0.667 40	0.685 45	0.677 29	0.372 41	0.562 46	0.000 49	0.482 42	0.244 39	0.316 46	0.298 44	0.052 51	0.442 41	0.857 30	0.267 46	0.702 34	0.559 36	1.000 1	0.287 43

MASC	0.447 45	0.528 50	0.555 49	0.381 43	0.382 40	0.633 40	0.002 47	0.509 40	0.260 38	0.361 42	0.432 35	0.327 37	0.451 38	0.571 47	0.367 43	0.639 43	0.386 45	0.980 40	0.276 44
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
One_Thing_One_Click	0.529 38	0.667 40	0.718 39	0.777 17	0.399 39	0.683 36	0.000 49	0.669 22	0.138 45	0.391 40	0.374 42	0.539 18	0.360 46	0.641 45	0.556 28	0.774 18	0.593 28	0.997 30	0.251 45
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
R-PointNet	0.306 50	0.500 52	0.405 54	0.311 44	0.348 42	0.589 43	0.054 37	0.068 55	0.126 47	0.283 47	0.290 45	0.028 52	0.219 51	0.214 52	0.331 44	0.396 55	0.275 51	0.821 50	0.245 46

3D-SIS	0.382 47	1.000 1	0.432 53	0.245 47	0.190 49	0.577 45	0.013 43	0.263 49	0.033 52	0.320 45	0.240 48	0.075 47	0.422 43	0.857 30	0.117 51	0.699 35	0.271 53	0.883 47	0.235 47
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
SegGroup_ins	0.445 46	0.667 40	0.773 29	0.185 52	0.317 44	0.656 38	0.000 49	0.407 46	0.134 46	0.381 41	0.267 46	0.217 40	0.476 37	0.714 41	0.452 35	0.629 44	0.514 41	1.000 1	0.222 48
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
Hier3D	0.323 48	0.667 40	0.542 50	0.264 46	0.157 52	0.550 47	0.000 49	0.205 52	0.009 53	0.270 48	0.218 49	0.075 47	0.500 35	0.688 44	0.007 57	0.698 36	0.301 50	0.459 54	0.200 49
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class	0.284 51	0.250 56	0.751 37	0.228 50	0.270 46	0.521 49	0.000 49	0.468 44	0.008 55	0.205 50	0.127 51	0.000 56	0.068 55	0.070 55	0.262 48	0.652 42	0.323 48	0.740 51	0.173 50

3D-BEVIS	0.248 53	0.667 40	0.566 48	0.076 56	0.035 57	0.394 55	0.027 42	0.035 56	0.098 49	0.099 55	0.030 56	0.025 53	0.098 54	0.375 51	0.126 50	0.604 47	0.181 55	0.854 49	0.171 51
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
SemRegionNet-20cls	0.250 52	0.333 53	0.613 47	0.229 49	0.163 51	0.493 50	0.000 49	0.304 48	0.107 48	0.147 53	0.100 52	0.052 50	0.231 49	0.119 53	0.039 53	0.445 53	0.325 47	0.654 52	0.141 52

tmp	0.248 53	0.667 40	0.437 52	0.188 51	0.153 53	0.491 51	0.000 49	0.208 51	0.094 50	0.153 52	0.099 53	0.057 49	0.217 52	0.119 53	0.039 53	0.466 52	0.302 49	0.640 53	0.140 53

Sgpn_scannet	0.143 56	0.208 57	0.390 55	0.169 53	0.065 55	0.275 56	0.029 41	0.069 54	0.000 56	0.087 56	0.043 55	0.014 54	0.027 57	0.000 56	0.112 52	0.351 56	0.168 56	0.438 55	0.138 54

ASIS	0.199 55	0.333 53	0.253 56	0.167 54	0.140 54	0.438 54	0.000 49	0.177 53	0.008 54	0.121 54	0.069 54	0.004 55	0.231 50	0.429 50	0.036 55	0.445 54	0.273 52	0.333 56	0.119 55

UNet-backbone	0.319 49	0.667 40	0.715 41	0.233 48	0.189 50	0.479 53	0.008 44	0.218 50	0.067 51	0.201 51	0.173 50	0.107 46	0.123 53	0.438 49	0.150 49	0.615 46	0.355 46	0.916 43	0.093 56

MaskRCNN 2d->3d Proj	0.058 57	0.333 53	0.002 57	0.000 57	0.053 56	0.002 57	0.002 48	0.021 57	0.000 56	0.045 57	0.024 57	0.238 39	0.065 56	0.000 56	0.014 56	0.107 57	0.020 57	0.110 57	0.006 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
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

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)
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

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

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

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

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

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

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
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

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

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

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
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

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
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

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

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.
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
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
ScanNet (2d proj)	0.330 23	0.293 22	0.521 22	0.657 6	0.361 22	0.161 22	0.250 22	0.004 23	0.440 20	0.183 23	0.836 20	0.125 22	0.060 23	0.319 23	0.132 22	0.417 22	0.412 22	0.344 22	0.541 23	0.427 23	0.109 23
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
DMMF	0.003 24	0.000 24	0.005 24	0.000 24	0.000 24	0.037 24	0.001 24	0.000 24	0.001 24	0.005 24	0.003 24	0.000 24	0.000 24	0.000 24	0.000 24	0.000 24	0.002 24	0.001 24	0.000 24	0.006 24	0.000 24

Method	avg ap 50%	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

UniDet_RVC	0.358 1	0.554 1	0.543 1	0.128 1	0.402 1	0.381 1	0.200 1	0.461 1	0.328 1	0.138 1	0.232 1	0.148 2	0.466 1	0.109 1	0.538 1	0.506 1	0.294 1	0.862 1	0.159 1

MaskRCNN_ScanNet	0.227 2	0.228 2	0.381 2	0.013 2	0.237 2	0.339 2	0.089 2	0.339 2	0.150 2	0.134 2	0.143 2	0.179 1	0.255 2	0.053 2	0.331 2	0.244 2	0.154 2	0.687 2	0.127 2
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17

Presenting the ScanNet200 Benchmark

ScanNet200 Benchmark

ScanNet200 3D Semantic Label Benchmark

ScanNet200 3D Semantic Instance Benchmark

ScanNet Benchmark

3D Semantic Label Benchmark

3D Semantic Instance Benchmark

2D Semantic Label Benchmark

2D Semantic Instance Benchmark

Scene Type Classification Benchmark

Method	avg recall	apartment	bathroom	bedroom / hotel	bookstore / library	conference room	copy/mail room	hallway	kitchen	laundry room	living room / lounge	misc	office	storage / basement / garage

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

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