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

PPT-SpUNet-F.T.	0.332 3	0.556 2	0.270 1	0.123 5	0.519 1	0.091 2	0.349 2	0.000 1	0.000 1	0.000 1	0.339 6	0.383 5	0.498 5	0.833 2	0.807 1	0.241 1	0.584 2	0.000 3	0.755 3	0.124 3	0.000 4	0.608 2	0.330 3	0.530 4	0.314 1	0.000 4	0.374 3	0.000 1	0.000 3	0.197 1	0.459 3	0.000 3	0.000 1	0.117 1	0.000 2	0.876 1	0.095 1	0.682 2	0.000 3	0.086 3	0.518 2	0.433 1	0.930 1	0.000 1	0.000 1	0.563 3	0.542 5	0.077 4	0.715 2	0.858 3	0.756 1	0.008 8	0.171 4	0.874 2	0.000 1	0.039 1	0.550 3	0.000 5	0.545 2	0.256 3	0.657 3	0.453 1	0.351 3	0.449 5	0.213 2	0.392 3	0.611 4	0.000 2	0.037 6	0.946 1	0.138 5	0.000 1	0.000 4	0.063 3	0.308 1	0.537 2	0.796 1	0.673 1	0.323 5	0.392 3	0.400 5	0.509 3	0.000 2	0.000 1	0.649 1	0.000 5	0.023 5	0.000 3	0.000 3	0.914 3	0.002 7	0.506 7	0.163 5	0.359 3	0.872 3	0.000 3	0.000 1	0.623 2	0.112 3	0.001 7	0.000 4	0.000 1	0.021 2	0.753 1	0.565 7	0.150 1	0.579 1	0.806 4	0.267 2	0.616 1	0.042 7	0.783 4	0.000 3	0.374 5	0.000 1	0.000 2	0.000 2	0.620 4	0.000 1	0.000 4	0.000 1	0.572 6	0.634 1	0.350 4	0.792 2	0.000 3	0.000 1	0.376 4	0.535 2	0.378 2	0.855 1	0.672 1	0.074 5	0.000 4	0.185 3	0.000 1	0.727 3	0.660 3	0.076 8	0.000 6	0.432 4	0.646 3	0.000 1	0.594 5	0.006 7	0.000 4	0.000 1	0.658 2	0.000 2	0.000 1	0.661 1	0.549 3	0.300 5	0.291 5	0.045 5	0.942 4	0.304 2	0.600 3	0.572 2	0.135 7	0.695 1	0.000 1	0.008 3	0.793 3	0.942 1	0.899 2	0.000 1	0.816 2	0.181 4	0.897 1	0.000 1	0.679 1	0.223 3	0.264 1	0.691 2	0.345 6
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training.
OctFormer ScanNet200	0.326 4	0.539 4	0.265 3	0.131 3	0.499 2	0.110 1	0.522 1	0.000 1	0.000 1	0.000 1	0.318 8	0.427 4	0.455 6	0.743 6	0.765 4	0.175 3	0.842 1	0.000 3	0.828 1	0.204 1	0.033 2	0.429 4	0.335 1	0.601 1	0.312 2	0.000 4	0.357 4	0.000 1	0.000 3	0.047 5	0.423 4	0.000 3	0.000 1	0.105 3	0.000 2	0.873 3	0.079 6	0.670 4	0.000 3	0.117 1	0.471 5	0.432 2	0.829 5	0.000 1	0.000 1	0.584 2	0.417 8	0.089 3	0.684 5	0.837 4	0.705 7	0.021 6	0.178 3	0.892 1	0.000 1	0.028 2	0.505 5	0.000 5	0.457 4	0.200 6	0.662 1	0.412 3	0.244 6	0.496 3	0.000 8	0.451 2	0.626 3	0.000 2	0.102 4	0.943 3	0.138 5	0.000 1	0.000 4	0.149 2	0.291 2	0.534 3	0.722 2	0.632 3	0.331 4	0.253 7	0.453 3	0.487 4	0.000 2	0.000 1	0.479 2	0.000 5	0.022 6	0.000 3	0.000 3	0.900 4	0.128 2	0.684 1	0.164 4	0.413 1	0.854 5	0.000 3	0.000 1	0.512 8	0.074 8	0.003 6	0.000 4	0.000 1	0.000 3	0.469 6	0.613 4	0.132 4	0.529 3	0.871 1	0.227 7	0.582 2	0.026 8	0.787 3	0.000 3	0.339 6	0.000 1	0.000 2	0.000 2	0.626 3	0.000 1	0.029 3	0.000 1	0.587 4	0.612 3	0.411 2	0.724 5	0.000 3	0.000 1	0.407 3	0.552 1	0.513 1	0.849 2	0.655 2	0.408 1	0.000 4	0.296 1	0.000 1	0.686 6	0.645 5	0.145 3	0.022 4	0.414 5	0.633 4	0.000 1	0.637 1	0.224 1	0.000 4	0.000 1	0.650 3	0.000 2	0.000 1	0.622 2	0.535 4	0.343 3	0.483 2	0.230 4	0.943 3	0.289 3	0.618 2	0.596 1	0.140 6	0.679 2	0.000 1	0.022 1	0.783 4	0.620 6	0.906 1	0.000 1	0.806 3	0.137 6	0.865 2	0.000 1	0.378 4	0.000 7	0.168 8	0.680 3	0.227 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
AWCS	0.305 5	0.508 5	0.225 5	0.142 2	0.463 5	0.063 6	0.195 5	0.000 1	0.000 1	0.000 1	0.467 2	0.551 1	0.504 3	0.773 3	0.764 5	0.142 5	0.029 8	0.000 3	0.626 6	0.100 5	0.000 4	0.360 5	0.179 6	0.507 6	0.137 6	0.006 3	0.300 5	0.000 1	0.000 3	0.172 3	0.364 6	0.512 2	0.000 1	0.056 5	0.000 2	0.865 5	0.093 3	0.634 8	0.000 3	0.071 6	0.396 6	0.296 7	0.876 4	0.000 1	0.000 1	0.373 5	0.436 7	0.063 6	0.749 1	0.877 2	0.721 3	0.131 2	0.124 5	0.804 6	0.000 1	0.000 4	0.515 4	0.010 4	0.452 5	0.252 4	0.578 5	0.417 2	0.179 8	0.484 4	0.171 3	0.337 5	0.606 5	0.000 2	0.115 3	0.937 5	0.142 4	0.000 1	0.008 3	0.000 7	0.157 7	0.484 5	0.402 8	0.501 6	0.339 3	0.553 1	0.529 2	0.478 5	0.000 2	0.000 1	0.404 3	0.001 4	0.022 6	0.077 2	0.000 3	0.894 6	0.219 1	0.628 2	0.093 6	0.305 5	0.886 1	0.233 2	0.000 1	0.603 3	0.112 3	0.023 5	0.000 4	0.000 1	0.000 3	0.741 2	0.664 2	0.097 6	0.253 5	0.782 5	0.264 3	0.523 4	0.154 1	0.707 7	0.000 3	0.411 3	0.000 1	0.000 2	0.000 2	0.332 7	0.000 1	0.000 4	0.000 1	0.602 2	0.595 4	0.185 7	0.656 7	0.159 1	0.000 1	0.355 5	0.424 6	0.154 6	0.729 6	0.516 5	0.220 4	0.620 2	0.084 5	0.000 1	0.707 5	0.651 4	0.173 1	0.014 5	0.381 8	0.582 7	0.000 1	0.619 2	0.049 6	0.000 4	0.000 1	0.702 1	0.000 2	0.000 1	0.302 7	0.489 6	0.317 4	0.334 4	0.392 1	0.922 5	0.254 5	0.533 6	0.394 4	0.129 8	0.613 6	0.000 1	0.000 6	0.820 1	0.649 5	0.749 5	0.000 1	0.782 5	0.282 3	0.863 3	0.000 1	0.288 7	0.006 5	0.220 5	0.633 5	0.542 1

CeCo	0.340 1	0.551 3	0.247 4	0.181 1	0.475 4	0.057 8	0.142 7	0.000 1	0.000 1	0.000 1	0.387 3	0.463 3	0.499 4	0.924 1	0.774 3	0.213 2	0.257 4	0.000 3	0.546 8	0.100 5	0.006 3	0.615 1	0.177 8	0.534 2	0.246 3	0.000 4	0.400 2	0.000 1	0.338 1	0.006 7	0.484 2	0.609 1	0.000 1	0.083 4	0.000 2	0.873 3	0.089 4	0.661 5	0.000 3	0.048 8	0.560 1	0.408 3	0.892 3	0.000 1	0.000 1	0.586 1	0.616 3	0.000 7	0.692 4	0.900 1	0.721 3	0.162 1	0.228 1	0.860 3	0.000 1	0.000 4	0.575 1	0.083 2	0.550 1	0.347 1	0.624 4	0.410 4	0.360 2	0.740 1	0.109 5	0.321 6	0.660 2	0.000 2	0.121 2	0.939 4	0.143 3	0.000 1	0.400 1	0.003 5	0.190 3	0.564 1	0.652 4	0.615 4	0.421 1	0.304 6	0.579 1	0.547 1	0.000 2	0.000 1	0.296 5	0.000 5	0.030 4	0.096 1	0.000 3	0.916 1	0.037 4	0.551 4	0.171 3	0.376 2	0.865 4	0.286 1	0.000 1	0.633 1	0.102 7	0.027 4	0.011 3	0.000 1	0.000 3	0.474 5	0.742 1	0.133 3	0.311 4	0.824 3	0.242 4	0.503 5	0.068 4	0.828 1	0.000 3	0.429 2	0.000 1	0.063 1	0.000 2	0.781 1	0.000 1	0.000 4	0.000 1	0.665 1	0.633 2	0.450 1	0.818 1	0.000 3	0.000 1	0.429 2	0.532 3	0.226 4	0.825 3	0.510 6	0.377 2	0.709 1	0.079 6	0.000 1	0.753 1	0.683 1	0.102 7	0.063 2	0.401 7	0.620 6	0.000 1	0.619 2	0.000 8	0.000 4	0.000 1	0.595 6	0.000 2	0.000 1	0.345 5	0.564 2	0.411 1	0.603 1	0.384 2	0.945 2	0.266 4	0.643 1	0.367 5	0.304 1	0.663 4	0.000 1	0.010 2	0.726 6	0.767 3	0.898 3	0.000 1	0.784 4	0.435 1	0.861 4	0.000 1	0.447 3	0.000 7	0.257 3	0.656 4	0.377 4
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
OA-CNN-L_ScanNet200	0.333 2	0.558 1	0.269 2	0.124 4	0.448 6	0.080 4	0.272 3	0.000 1	0.000 1	0.000 1	0.342 5	0.515 2	0.524 2	0.713 8	0.789 2	0.158 4	0.384 3	0.000 3	0.806 2	0.125 2	0.000 4	0.496 3	0.332 2	0.498 7	0.227 4	0.024 2	0.474 1	0.000 1	0.003 2	0.071 4	0.487 1	0.000 3	0.000 1	0.110 2	0.000 2	0.876 1	0.013 8	0.703 1	0.000 3	0.076 4	0.473 4	0.355 4	0.906 2	0.000 1	0.000 1	0.476 4	0.706 1	0.000 7	0.672 6	0.835 5	0.748 2	0.015 7	0.223 2	0.860 3	0.000 1	0.000 4	0.572 2	0.000 5	0.509 3	0.313 2	0.662 1	0.398 5	0.396 1	0.411 6	0.276 1	0.527 1	0.711 1	0.000 2	0.076 5	0.946 1	0.166 2	0.000 1	0.022 2	0.160 1	0.183 4	0.493 4	0.699 3	0.637 2	0.403 2	0.330 5	0.406 4	0.526 2	0.024 1	0.000 1	0.392 4	0.000 5	0.016 8	0.000 3	0.196 2	0.915 2	0.112 3	0.557 3	0.197 1	0.352 4	0.877 2	0.000 3	0.000 1	0.592 6	0.103 6	0.000 8	0.067 1	0.000 1	0.089 1	0.735 3	0.625 3	0.130 5	0.568 2	0.836 2	0.271 1	0.534 3	0.043 6	0.799 2	0.001 2	0.445 1	0.000 1	0.000 2	0.024 1	0.661 2	0.000 1	0.262 1	0.000 1	0.591 3	0.517 7	0.373 3	0.788 3	0.021 2	0.000 1	0.455 1	0.517 4	0.320 3	0.823 4	0.200 8	0.001 8	0.150 3	0.100 4	0.000 1	0.736 2	0.668 2	0.103 6	0.052 3	0.662 1	0.720 1	0.000 1	0.602 4	0.112 3	0.002 3	0.000 1	0.637 4	0.000 2	0.000 1	0.621 3	0.569 1	0.398 2	0.412 3	0.234 3	0.949 1	0.363 1	0.492 7	0.495 3	0.251 3	0.665 3	0.000 1	0.001 5	0.805 2	0.833 2	0.794 4	0.000 1	0.821 1	0.314 2	0.843 5	0.000 1	0.560 2	0.245 2	0.262 2	0.713 1	0.370 5

Minkowski 34D	0.253 7	0.463 7	0.154 8	0.102 7	0.381 8	0.084 3	0.134 8	0.000 1	0.000 1	0.000 1	0.386 4	0.141 8	0.279 8	0.737 7	0.703 7	0.014 8	0.164 6	0.000 3	0.663 4	0.092 7	0.000 4	0.224 7	0.291 4	0.531 3	0.056 8	0.000 4	0.242 7	0.000 1	0.000 3	0.013 6	0.331 7	0.000 3	0.000 1	0.035 8	0.001 1	0.858 6	0.059 7	0.650 7	0.000 3	0.056 7	0.353 7	0.299 6	0.670 7	0.000 1	0.000 1	0.284 7	0.484 6	0.071 5	0.594 7	0.720 7	0.710 6	0.027 5	0.068 8	0.813 5	0.000 1	0.005 3	0.492 6	0.164 1	0.274 7	0.111 8	0.571 7	0.307 8	0.293 5	0.307 8	0.150 4	0.163 8	0.531 7	0.002 1	0.545 1	0.932 6	0.093 8	0.000 1	0.000 4	0.002 6	0.159 6	0.368 8	0.581 7	0.440 8	0.228 8	0.406 2	0.282 8	0.294 7	0.000 2	0.000 1	0.189 7	0.060 1	0.036 3	0.000 3	0.000 3	0.897 5	0.000 8	0.525 6	0.025 8	0.205 8	0.771 8	0.000 3	0.000 1	0.593 5	0.108 5	0.044 3	0.000 4	0.000 1	0.000 3	0.282 8	0.589 6	0.094 7	0.169 7	0.466 8	0.227 7	0.419 8	0.125 3	0.757 5	0.002 1	0.334 7	0.000 1	0.000 2	0.000 2	0.357 6	0.000 1	0.000 4	0.000 1	0.582 5	0.513 8	0.337 5	0.612 8	0.000 3	0.000 1	0.250 7	0.352 8	0.136 8	0.724 7	0.655 2	0.280 3	0.000 4	0.046 8	0.000 1	0.606 8	0.559 6	0.159 2	0.102 1	0.445 2	0.655 2	0.000 1	0.310 8	0.117 2	0.000 4	0.000 1	0.581 8	0.026 1	0.000 1	0.265 8	0.483 7	0.084 8	0.097 8	0.044 6	0.865 8	0.142 8	0.588 4	0.351 6	0.272 2	0.596 8	0.000 1	0.003 4	0.622 7	0.720 4	0.096 8	0.000 1	0.771 7	0.016 7	0.772 6	0.000 1	0.302 6	0.194 4	0.214 6	0.621 7	0.197 8
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
LGround	0.272 6	0.485 6	0.184 6	0.106 6	0.476 3	0.077 5	0.218 4	0.000 1	0.000 1	0.000 1	0.547 1	0.295 6	0.540 1	0.746 5	0.745 6	0.058 7	0.112 7	0.005 1	0.658 5	0.077 8	0.000 4	0.322 6	0.178 7	0.512 5	0.190 5	0.199 1	0.277 6	0.000 1	0.000 3	0.173 2	0.399 5	0.000 3	0.000 1	0.039 7	0.000 2	0.858 6	0.085 5	0.676 3	0.002 1	0.103 2	0.498 3	0.323 5	0.703 6	0.000 1	0.000 1	0.296 6	0.549 4	0.216 1	0.702 3	0.768 6	0.718 5	0.028 4	0.092 7	0.786 7	0.000 1	0.000 4	0.453 7	0.022 3	0.251 8	0.252 4	0.572 6	0.348 6	0.321 4	0.514 2	0.063 6	0.279 7	0.552 6	0.000 2	0.019 7	0.932 6	0.132 7	0.000 1	0.000 4	0.000 7	0.156 8	0.457 6	0.623 5	0.518 5	0.265 7	0.358 4	0.381 6	0.395 6	0.000 2	0.000 1	0.127 8	0.012 3	0.051 1	0.000 3	0.000 3	0.886 7	0.014 5	0.437 8	0.179 2	0.244 6	0.826 6	0.000 3	0.000 1	0.599 4	0.136 1	0.085 2	0.000 4	0.000 1	0.000 3	0.565 4	0.612 5	0.143 2	0.207 6	0.566 6	0.232 6	0.446 6	0.127 2	0.708 6	0.000 3	0.384 4	0.000 1	0.000 2	0.000 2	0.402 5	0.000 1	0.059 2	0.000 1	0.525 8	0.566 5	0.229 6	0.659 6	0.000 3	0.000 1	0.265 6	0.446 5	0.147 7	0.720 8	0.597 4	0.066 6	0.000 4	0.187 2	0.000 1	0.726 4	0.467 8	0.134 5	0.000 6	0.413 6	0.629 5	0.000 1	0.363 7	0.055 5	0.022 2	0.000 1	0.626 5	0.000 2	0.000 1	0.323 6	0.479 8	0.154 7	0.117 6	0.028 7	0.901 6	0.243 6	0.415 8	0.295 8	0.143 5	0.610 7	0.000 1	0.000 6	0.777 5	0.397 8	0.324 7	0.000 1	0.778 6	0.179 5	0.702 7	0.000 1	0.274 8	0.404 1	0.233 4	0.622 6	0.398 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrain	0.249 8	0.455 8	0.171 7	0.079 8	0.418 7	0.059 7	0.186 6	0.000 1	0.000 1	0.000 1	0.335 7	0.250 7	0.316 7	0.766 4	0.697 8	0.142 5	0.170 5	0.003 2	0.553 7	0.112 4	0.097 1	0.201 8	0.186 5	0.476 8	0.081 7	0.000 4	0.216 8	0.000 1	0.000 3	0.001 8	0.314 8	0.000 3	0.000 1	0.055 6	0.000 2	0.832 8	0.094 2	0.659 6	0.002 1	0.076 4	0.310 8	0.293 8	0.664 8	0.000 1	0.000 1	0.175 8	0.634 2	0.130 2	0.552 8	0.686 8	0.700 8	0.076 3	0.110 6	0.770 8	0.000 1	0.000 4	0.430 8	0.000 5	0.319 6	0.166 7	0.542 8	0.327 7	0.205 7	0.332 7	0.052 7	0.375 4	0.444 8	0.000 2	0.012 8	0.930 8	0.203 1	0.000 1	0.000 4	0.046 4	0.175 5	0.413 7	0.592 6	0.471 7	0.299 6	0.152 8	0.340 7	0.247 8	0.000 2	0.000 1	0.225 6	0.058 2	0.037 2	0.000 3	0.207 1	0.862 8	0.014 5	0.548 5	0.033 7	0.233 7	0.816 7	0.000 3	0.000 1	0.542 7	0.123 2	0.121 1	0.019 2	0.000 1	0.000 3	0.463 7	0.454 8	0.045 8	0.128 8	0.557 7	0.235 5	0.441 7	0.063 5	0.484 8	0.000 3	0.308 8	0.000 1	0.000 2	0.000 2	0.318 8	0.000 1	0.000 4	0.000 1	0.545 7	0.543 6	0.164 8	0.734 4	0.000 3	0.000 1	0.215 8	0.371 7	0.198 5	0.743 5	0.205 7	0.062 7	0.000 4	0.079 6	0.000 1	0.683 7	0.547 7	0.142 4	0.000 6	0.441 3	0.579 8	0.000 1	0.464 6	0.098 4	0.041 1	0.000 1	0.590 7	0.000 2	0.000 1	0.373 4	0.494 5	0.174 6	0.105 7	0.001 8	0.895 7	0.222 7	0.537 5	0.307 7	0.180 4	0.625 5	0.000 1	0.000 6	0.591 8	0.609 7	0.398 6	0.000 1	0.766 8	0.014 8	0.638 8	0.000 1	0.377 5	0.004 6	0.206 7	0.609 8	0.465 2
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021

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

Method	avg	alarm clock	armchair	backpack	bar	basket	bathroom cabinet	bathroom counter	bathroom stall	bathroom stall door	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	cd case	ceiling	ceiling light	clock	closet	closet door	closet rod	closet wall	clothes dryer	coffee kettle	coffee maker	coffee table	column	computer tower	container	copier	couch	crate	cup	curtain	decoration	desk	dining table	dish rack	dishwasher	divider	door	doorframe	dumbbell	dustpan	end table	fan	fire alarm	fire extinguisher	fireplace	folded chair	furniture	guitar case	jacket	keyboard	keyboard piano	kitchen cabinet	kitchen counter	ladder	lamp	laptop	laundry basket	laundry detergent	laundry hamper	ledge	light	light switch	luggage	machine	mailbox	mattress	microwave	mini fridge	monitor	mouse	music stand	nightstand	object	office chair	ottoman	oven	paper	paper cutter	paper towel dispenser	paper towel roll	person	piano	picture	pillar	pillow	pipe	plate	plunger	poster	potted plant	power outlet	power strip	projector	projector screen	purse	rack	radiator	range hood	recycling bin	scale	seat	shelf	shoe	shower	shower curtain	shower curtain rod	shower door	shower floor	shower head	sign	sink	soap dish	soap dispenser	sofa chair	stair rail	stairs	stand	stool	storage bin	storage container	storage organizer	stove	structure	stuffed animal	suitcase	table	telephone	tissue box	toaster	toaster oven	toilet	toilet paper	toilet paper holder	toilet seat cover dispenser	towel	tray	tube	tv	tv stand	vacuum cleaner	vent	wardrobe	washing machine	water bottle	water cooler	water pitcher	whiteboard	window	windowsill

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

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

ScanNet Benchmark

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

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

Mix3D	0.781 1	0.964 1	0.855 1	0.843 13	0.781 4	0.858 9	0.575 4	0.831 26	0.685 9	0.714 2	0.979 1	0.594 4	0.310 20	0.801 1	0.892 12	0.841 2	0.819 3	0.723 3	0.940 8	0.887 2	0.725 18
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3D	0.779 2	0.861 17	0.818 11	0.836 16	0.790 2	0.875 2	0.576 3	0.905 3	0.704 3	0.739 1	0.969 6	0.611 1	0.349 6	0.756 17	0.958 1	0.702 36	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 26	0.829 20	0.751 16	0.854 11	0.540 14	0.903 4	0.630 27	0.672 10	0.963 9	0.565 16	0.357 4	0.788 2	0.900 8	0.737 19	0.802 13	0.685 12	0.950 2	0.887 2	0.780 2
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training.
OctFormer	0.766 3	0.925 4	0.808 17	0.849 7	0.786 3	0.846 20	0.566 6	0.876 10	0.690 7	0.674 9	0.960 11	0.576 12	0.226 59	0.753 19	0.904 6	0.777 7	0.815 5	0.722 4	0.923 20	0.877 8	0.776 4
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
OccuSeg+Semantic	0.764 5	0.758 53	0.796 24	0.839 15	0.746 18	0.907 1	0.562 7	0.850 19	0.680 11	0.672 10	0.978 2	0.610 2	0.335 11	0.777 5	0.819 38	0.847 1	0.830 1	0.691 10	0.972 1	0.885 4	0.727 16

CU-Hybrid Net	0.764 5	0.924 5	0.819 9	0.840 14	0.757 11	0.853 13	0.580 1	0.848 20	0.709 2	0.643 17	0.958 14	0.587 8	0.295 26	0.753 19	0.884 16	0.758 13	0.815 5	0.725 2	0.927 18	0.867 15	0.743 10

O-CNN	0.762 7	0.924 5	0.823 5	0.844 12	0.770 6	0.852 14	0.577 2	0.847 22	0.711 1	0.640 21	0.958 14	0.592 5	0.217 65	0.762 13	0.888 13	0.758 13	0.813 8	0.726 1	0.932 16	0.868 14	0.744 9
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
OA-CNN-L_ScanNet20	0.756 8	0.783 39	0.826 4	0.858 4	0.776 5	0.837 26	0.548 11	0.896 7	0.649 20	0.675 8	0.962 10	0.586 9	0.335 11	0.771 8	0.802 42	0.770 9	0.787 28	0.691 10	0.936 11	0.880 7	0.761 6

ConDaFormer	0.755 9	0.927 3	0.822 6	0.836 16	0.801 1	0.849 17	0.516 24	0.864 15	0.651 19	0.680 7	0.958 14	0.584 11	0.282 33	0.759 15	0.855 26	0.728 21	0.802 13	0.678 14	0.880 52	0.873 13	0.756 7
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
PointTransformerV2	0.752 10	0.742 60	0.809 16	0.872 1	0.758 10	0.860 8	0.552 9	0.891 8	0.610 35	0.687 3	0.960 11	0.559 19	0.304 23	0.766 10	0.926 3	0.767 10	0.797 19	0.644 26	0.942 6	0.876 11	0.722 20
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net	0.752 10	0.906 9	0.793 28	0.802 35	0.689 33	0.825 37	0.556 8	0.867 12	0.681 10	0.602 37	0.960 11	0.555 21	0.365 3	0.779 4	0.859 22	0.747 16	0.795 23	0.717 5	0.917 23	0.856 23	0.764 5
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointConvFormer	0.749 12	0.793 36	0.790 29	0.807 31	0.750 17	0.856 10	0.524 20	0.881 9	0.588 47	0.642 20	0.977 4	0.591 6	0.274 38	0.781 3	0.929 2	0.804 3	0.796 20	0.642 27	0.947 4	0.885 4	0.715 23
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNet	0.749 12	0.909 7	0.818 11	0.811 28	0.752 14	0.839 25	0.485 40	0.842 23	0.673 12	0.644 16	0.957 18	0.528 30	0.305 22	0.773 7	0.859 22	0.788 4	0.818 4	0.693 9	0.916 24	0.856 23	0.723 19
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP	0.748 14	0.623 86	0.804 19	0.859 3	0.745 19	0.824 39	0.501 30	0.912 2	0.690 7	0.685 5	0.956 20	0.567 15	0.320 17	0.768 9	0.918 4	0.720 26	0.802 13	0.676 16	0.921 21	0.881 6	0.779 3

StratifiedFormer	0.747 15	0.901 10	0.803 20	0.845 11	0.757 11	0.846 20	0.512 25	0.825 29	0.696 6	0.645 15	0.956 20	0.576 12	0.262 50	0.744 24	0.861 21	0.742 17	0.770 37	0.705 7	0.899 37	0.860 20	0.734 11
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
VMNet	0.746 16	0.870 15	0.838 2	0.858 4	0.729 24	0.850 16	0.501 30	0.874 11	0.587 48	0.658 13	0.956 20	0.564 17	0.299 24	0.765 11	0.900 8	0.716 29	0.812 9	0.631 32	0.939 9	0.858 21	0.709 24
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion	0.746 16	0.771 47	0.819 9	0.848 9	0.702 30	0.865 7	0.397 77	0.899 5	0.699 4	0.664 12	0.948 48	0.588 7	0.330 13	0.746 23	0.851 30	0.764 11	0.796 20	0.704 8	0.935 12	0.866 16	0.728 14
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
Retro-FPN	0.744 18	0.842 23	0.800 21	0.767 48	0.740 20	0.836 28	0.541 13	0.914 1	0.672 13	0.626 26	0.958 14	0.552 22	0.272 40	0.777 5	0.886 15	0.696 37	0.801 17	0.674 18	0.941 7	0.858 21	0.717 21
Peng Xiang, Xin Wen, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
EQ-Net	0.743 19	0.620 87	0.799 23	0.849 7	0.730 23	0.822 42	0.493 37	0.897 6	0.664 14	0.681 6	0.955 23	0.562 18	0.378 1	0.760 14	0.903 7	0.738 18	0.801 17	0.673 19	0.907 29	0.877 8	0.745 8
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet	0.742 20	0.816 31	0.806 18	0.807 31	0.752 14	0.828 35	0.575 4	0.839 25	0.699 4	0.637 22	0.954 29	0.520 32	0.320 17	0.755 18	0.834 34	0.760 12	0.772 34	0.676 16	0.915 26	0.862 18	0.717 21

SAT	0.742 20	0.860 18	0.765 42	0.819 23	0.769 7	0.848 18	0.533 16	0.829 27	0.663 15	0.631 24	0.955 23	0.586 9	0.274 38	0.753 19	0.896 10	0.729 20	0.760 44	0.666 21	0.921 21	0.855 25	0.733 12

LargeKernel3D	0.739 22	0.909 7	0.820 8	0.806 33	0.740 20	0.852 14	0.545 12	0.826 28	0.594 46	0.643 17	0.955 23	0.541 24	0.263 49	0.723 27	0.858 24	0.775 8	0.767 38	0.678 14	0.933 14	0.848 31	0.694 30
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 23	0.776 43	0.790 29	0.851 6	0.754 13	0.854 11	0.491 39	0.866 13	0.596 45	0.686 4	0.955 23	0.536 25	0.342 8	0.624 43	0.869 18	0.787 5	0.802 13	0.628 33	0.927 18	0.875 12	0.704 27

MinkowskiNet	0.736 23	0.859 19	0.818 11	0.832 19	0.709 28	0.840 24	0.521 22	0.853 18	0.660 17	0.643 17	0.951 38	0.544 23	0.286 31	0.731 25	0.893 11	0.675 45	0.772 34	0.683 13	0.874 58	0.852 29	0.727 16
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 25	0.890 11	0.837 3	0.864 2	0.726 25	0.873 3	0.530 19	0.824 30	0.489 79	0.647 14	0.978 2	0.609 3	0.336 10	0.624 43	0.733 52	0.758 13	0.776 32	0.570 58	0.949 3	0.877 8	0.728 14

SparseConvNet	0.725 26	0.647 83	0.821 7	0.846 10	0.721 26	0.869 4	0.533 16	0.754 50	0.603 41	0.614 30	0.955 23	0.572 14	0.325 15	0.710 28	0.870 17	0.724 24	0.823 2	0.628 33	0.934 13	0.865 17	0.683 33

PointTransformer++	0.725 26	0.727 68	0.811 15	0.819 23	0.765 8	0.841 23	0.502 29	0.814 35	0.621 30	0.623 28	0.955 23	0.556 20	0.284 32	0.620 45	0.866 19	0.781 6	0.757 47	0.648 24	0.932 16	0.862 18	0.709 24

MatchingNet	0.724 28	0.812 33	0.812 14	0.810 29	0.735 22	0.834 30	0.495 36	0.860 17	0.572 54	0.602 37	0.954 29	0.512 34	0.280 35	0.757 16	0.845 32	0.725 23	0.780 30	0.606 44	0.937 10	0.851 30	0.700 29

PNE	0.721 29	0.840 24	0.789 31	0.833 18	0.690 31	0.823 41	0.509 26	0.864 15	0.618 31	0.629 25	0.957 18	0.500 37	0.266 47	0.763 12	0.797 44	0.674 49	0.791 26	0.621 38	0.892 42	0.855 25	0.708 26

INS-Conv-semantic	0.717 30	0.751 56	0.759 45	0.812 27	0.704 29	0.868 5	0.537 15	0.842 23	0.609 37	0.608 33	0.953 32	0.534 27	0.293 27	0.616 46	0.864 20	0.719 28	0.793 24	0.640 28	0.933 14	0.845 35	0.663 38

PointMetaBase	0.714 31	0.835 25	0.785 32	0.821 21	0.684 35	0.846 20	0.531 18	0.865 14	0.614 32	0.596 41	0.953 32	0.500 37	0.246 55	0.674 29	0.888 13	0.692 38	0.764 40	0.624 35	0.849 73	0.844 36	0.675 35

contrastBoundary	0.705 32	0.769 50	0.775 37	0.809 30	0.687 34	0.820 45	0.439 65	0.812 36	0.661 16	0.591 43	0.945 56	0.515 33	0.171 83	0.633 40	0.856 25	0.720 26	0.796 20	0.668 20	0.889 45	0.847 32	0.689 31
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 33	0.774 45	0.800 21	0.793 39	0.760 9	0.847 19	0.471 43	0.802 39	0.463 86	0.634 23	0.968 8	0.491 41	0.271 42	0.726 26	0.910 5	0.706 33	0.815 5	0.551 69	0.878 53	0.833 37	0.570 69

RFCR	0.702 34	0.889 12	0.745 54	0.813 26	0.672 38	0.818 49	0.493 37	0.815 34	0.623 28	0.610 31	0.947 50	0.470 49	0.249 54	0.594 49	0.848 31	0.705 34	0.779 31	0.646 25	0.892 42	0.823 43	0.611 52
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click	0.701 35	0.825 29	0.796 24	0.723 55	0.716 27	0.832 31	0.433 67	0.816 32	0.634 25	0.609 32	0.969 6	0.418 74	0.344 7	0.559 61	0.833 35	0.715 30	0.808 11	0.560 63	0.902 34	0.847 32	0.680 34

JSENet	0.699 36	0.881 14	0.762 43	0.821 21	0.667 39	0.800 62	0.522 21	0.792 42	0.613 33	0.607 34	0.935 76	0.492 40	0.205 70	0.576 54	0.853 28	0.691 39	0.758 46	0.652 23	0.872 61	0.828 40	0.649 42
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click	0.693 37	0.743 59	0.794 26	0.655 79	0.684 35	0.822 42	0.497 35	0.719 60	0.622 29	0.617 29	0.977 4	0.447 61	0.339 9	0.750 22	0.664 68	0.703 35	0.790 27	0.596 48	0.946 5	0.855 25	0.647 43
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-II	0.692 38	0.732 64	0.772 38	0.786 40	0.677 37	0.866 6	0.517 23	0.848 20	0.509 71	0.626 26	0.952 36	0.536 25	0.225 61	0.545 67	0.704 59	0.689 42	0.810 10	0.564 62	0.903 33	0.854 28	0.729 13
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 39	0.884 13	0.754 49	0.795 38	0.647 45	0.818 49	0.422 69	0.802 39	0.612 34	0.604 35	0.945 56	0.462 52	0.189 78	0.563 60	0.853 28	0.726 22	0.765 39	0.632 31	0.904 31	0.821 46	0.606 56
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 40	0.704 73	0.741 58	0.754 52	0.656 41	0.829 33	0.501 30	0.741 55	0.609 37	0.548 50	0.950 42	0.522 31	0.371 2	0.633 40	0.756 47	0.715 30	0.771 36	0.623 36	0.861 69	0.814 48	0.658 39
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 41	0.866 16	0.748 51	0.819 23	0.645 47	0.794 65	0.450 54	0.802 39	0.587 48	0.604 35	0.945 56	0.464 51	0.201 73	0.554 63	0.840 33	0.723 25	0.732 56	0.602 46	0.907 29	0.822 45	0.603 59

KP-FCNN	0.684 42	0.847 22	0.758 47	0.784 42	0.647 45	0.814 52	0.473 42	0.772 45	0.605 39	0.594 42	0.935 76	0.450 59	0.181 81	0.587 50	0.805 41	0.690 40	0.785 29	0.614 40	0.882 49	0.819 47	0.632 48
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet	0.684 42	0.712 72	0.784 33	0.782 44	0.658 40	0.835 29	0.499 34	0.823 31	0.641 22	0.597 40	0.950 42	0.487 42	0.281 34	0.575 55	0.619 71	0.647 59	0.764 40	0.620 39	0.871 64	0.846 34	0.688 32

VACNN++	0.684 42	0.728 67	0.757 48	0.776 45	0.690 31	0.804 59	0.464 48	0.816 32	0.577 53	0.587 44	0.945 56	0.508 36	0.276 37	0.671 30	0.710 57	0.663 51	0.750 50	0.589 53	0.881 50	0.832 39	0.653 41

Superpoint Network	0.683 45	0.851 21	0.728 62	0.800 37	0.653 43	0.806 57	0.468 45	0.804 37	0.572 54	0.602 37	0.946 53	0.453 58	0.239 58	0.519 72	0.822 36	0.689 42	0.762 43	0.595 50	0.895 40	0.827 41	0.630 49

PointContrast_LA_SEM	0.683 45	0.757 54	0.784 33	0.786 40	0.639 49	0.824 39	0.408 72	0.775 44	0.604 40	0.541 52	0.934 80	0.532 28	0.269 44	0.552 64	0.777 45	0.645 62	0.793 24	0.640 28	0.913 27	0.824 42	0.671 36

VI-PointConv	0.676 47	0.770 49	0.754 49	0.783 43	0.621 53	0.814 52	0.552 9	0.758 48	0.571 56	0.557 48	0.954 29	0.529 29	0.268 46	0.530 70	0.682 63	0.675 45	0.719 59	0.603 45	0.888 46	0.833 37	0.665 37
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D	0.673 48	0.789 37	0.748 51	0.763 50	0.635 51	0.814 52	0.407 74	0.747 52	0.581 52	0.573 45	0.950 42	0.484 43	0.271 42	0.607 47	0.754 48	0.649 56	0.774 33	0.596 48	0.883 48	0.823 43	0.606 56

SALANet	0.670 49	0.816 31	0.770 40	0.768 47	0.652 44	0.807 56	0.451 51	0.747 52	0.659 18	0.545 51	0.924 86	0.473 48	0.149 93	0.571 57	0.811 40	0.635 65	0.746 51	0.623 36	0.892 42	0.794 60	0.570 69

PointConv	0.666 50	0.781 40	0.759 45	0.699 64	0.644 48	0.822 42	0.475 41	0.779 43	0.564 59	0.504 68	0.953 32	0.428 68	0.203 72	0.586 52	0.754 48	0.661 52	0.753 48	0.588 54	0.902 34	0.813 50	0.642 44
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 50	0.703 74	0.781 35	0.751 54	0.655 42	0.830 32	0.471 43	0.769 46	0.474 82	0.537 54	0.951 38	0.475 47	0.279 36	0.635 38	0.698 62	0.675 45	0.751 49	0.553 68	0.816 80	0.806 52	0.703 28
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++	0.663 52	0.746 57	0.708 66	0.722 56	0.638 50	0.820 45	0.451 51	0.566 87	0.599 43	0.541 52	0.950 42	0.510 35	0.313 19	0.648 35	0.819 38	0.616 70	0.682 75	0.590 52	0.869 65	0.810 51	0.656 40
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net	0.658 53	0.778 41	0.702 69	0.806 33	0.619 54	0.813 55	0.468 45	0.693 68	0.494 74	0.524 60	0.941 67	0.449 60	0.298 25	0.510 74	0.821 37	0.675 45	0.727 58	0.568 60	0.826 78	0.803 54	0.637 46
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 54	0.698 75	0.743 56	0.650 80	0.564 71	0.820 45	0.505 28	0.758 48	0.631 26	0.479 73	0.945 56	0.480 45	0.226 59	0.572 56	0.774 46	0.690 40	0.735 54	0.614 40	0.853 72	0.776 75	0.597 62
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 55	0.752 55	0.734 60	0.664 77	0.583 66	0.815 51	0.399 76	0.754 50	0.639 23	0.535 56	0.942 65	0.470 49	0.309 21	0.665 31	0.539 77	0.650 55	0.708 64	0.635 30	0.857 71	0.793 62	0.642 44
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 56	0.778 41	0.731 61	0.699 64	0.577 67	0.829 33	0.446 56	0.736 56	0.477 81	0.523 62	0.945 56	0.454 56	0.269 44	0.484 81	0.749 51	0.618 68	0.738 52	0.599 47	0.827 77	0.792 65	0.621 51

MVPNet	0.641 57	0.831 26	0.715 64	0.671 74	0.590 62	0.781 71	0.394 78	0.679 70	0.642 21	0.553 49	0.937 73	0.462 52	0.256 51	0.649 34	0.406 90	0.626 66	0.691 72	0.666 21	0.877 54	0.792 65	0.608 55
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN	0.641 57	0.776 43	0.703 68	0.721 57	0.557 74	0.826 36	0.451 51	0.672 72	0.563 60	0.483 72	0.943 64	0.425 71	0.162 88	0.644 36	0.726 53	0.659 53	0.709 63	0.572 57	0.875 56	0.786 70	0.559 75

PointMRNet	0.640 59	0.717 71	0.701 70	0.692 67	0.576 68	0.801 61	0.467 47	0.716 61	0.563 60	0.459 78	0.953 32	0.429 67	0.169 85	0.581 53	0.854 27	0.605 71	0.710 61	0.550 70	0.894 41	0.793 62	0.575 67

FPConv	0.639 60	0.785 38	0.760 44	0.713 62	0.603 57	0.798 63	0.392 79	0.534 92	0.603 41	0.524 60	0.948 48	0.457 54	0.250 53	0.538 68	0.723 55	0.598 75	0.696 70	0.614 40	0.872 61	0.799 55	0.567 72
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net	0.638 61	0.797 35	0.769 41	0.641 85	0.590 62	0.820 45	0.461 49	0.537 91	0.637 24	0.536 55	0.947 50	0.388 82	0.206 69	0.656 32	0.668 66	0.647 59	0.732 56	0.585 55	0.868 66	0.793 62	0.473 94

PointSPNet	0.637 62	0.734 63	0.692 77	0.714 61	0.576 68	0.797 64	0.446 56	0.743 54	0.598 44	0.437 83	0.942 65	0.403 78	0.150 92	0.626 42	0.800 43	0.649 56	0.697 69	0.557 66	0.846 74	0.777 74	0.563 73

SConv	0.636 63	0.830 27	0.697 73	0.752 53	0.572 70	0.780 73	0.445 58	0.716 61	0.529 65	0.530 57	0.951 38	0.446 62	0.170 84	0.507 76	0.666 67	0.636 64	0.682 75	0.541 75	0.886 47	0.799 55	0.594 63

Supervoxel-CNN	0.635 64	0.656 81	0.711 65	0.719 58	0.613 55	0.757 82	0.444 61	0.765 47	0.534 64	0.566 46	0.928 84	0.478 46	0.272 40	0.636 37	0.531 79	0.664 50	0.645 85	0.508 83	0.864 68	0.792 65	0.611 52

joint point-based	0.634 65	0.614 88	0.778 36	0.667 76	0.633 52	0.825 37	0.420 70	0.804 37	0.467 84	0.561 47	0.951 38	0.494 39	0.291 28	0.566 58	0.458 85	0.579 82	0.764 40	0.559 65	0.838 75	0.814 48	0.598 61
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL	0.632 66	0.731 65	0.688 80	0.675 71	0.591 61	0.784 70	0.444 61	0.565 88	0.610 35	0.492 70	0.949 46	0.456 55	0.254 52	0.587 50	0.706 58	0.599 74	0.665 81	0.612 43	0.868 66	0.791 69	0.579 66

3DSM_DMMF	0.631 67	0.626 85	0.745 54	0.801 36	0.607 56	0.751 83	0.506 27	0.729 59	0.565 58	0.491 71	0.866 100	0.434 63	0.197 76	0.595 48	0.630 70	0.709 32	0.705 66	0.560 63	0.875 56	0.740 85	0.491 89

APCF-Net	0.631 67	0.742 60	0.687 82	0.672 72	0.557 74	0.792 68	0.408 72	0.665 73	0.545 62	0.508 65	0.952 36	0.428 68	0.186 79	0.634 39	0.702 60	0.620 67	0.706 65	0.555 67	0.873 59	0.798 57	0.581 65
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointNet2-SFPN	0.631 67	0.771 47	0.692 77	0.672 72	0.524 79	0.837 26	0.440 64	0.706 66	0.538 63	0.446 80	0.944 62	0.421 73	0.219 64	0.552 64	0.751 50	0.591 78	0.737 53	0.543 74	0.901 36	0.768 77	0.557 76

FusionAwareConv	0.630 70	0.604 90	0.741 58	0.766 49	0.590 62	0.747 84	0.501 30	0.734 57	0.503 73	0.527 58	0.919 90	0.454 56	0.323 16	0.550 66	0.420 89	0.678 44	0.688 73	0.544 72	0.896 39	0.795 59	0.627 50
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 71	0.800 34	0.625 92	0.719 58	0.545 77	0.806 57	0.445 58	0.597 82	0.448 89	0.519 63	0.938 72	0.481 44	0.328 14	0.489 80	0.499 84	0.657 54	0.759 45	0.592 51	0.881 50	0.797 58	0.634 47

SegGroup_sem	0.627 72	0.818 30	0.747 53	0.701 63	0.602 58	0.764 79	0.385 83	0.629 79	0.490 77	0.508 65	0.931 83	0.409 76	0.201 73	0.564 59	0.725 54	0.618 68	0.692 71	0.539 76	0.873 59	0.794 60	0.548 79
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv	0.625 73	0.830 27	0.694 75	0.757 51	0.563 72	0.772 77	0.448 55	0.647 76	0.520 67	0.509 64	0.949 46	0.431 66	0.191 77	0.496 78	0.614 72	0.647 59	0.672 79	0.535 78	0.876 55	0.783 71	0.571 68

HPEIN	0.618 74	0.729 66	0.668 83	0.647 82	0.597 60	0.766 78	0.414 71	0.680 69	0.520 67	0.525 59	0.946 53	0.432 64	0.215 66	0.493 79	0.599 73	0.638 63	0.617 90	0.570 58	0.897 38	0.806 52	0.605 58
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCN	0.610 75	0.858 20	0.772 38	0.489 97	0.532 78	0.792 68	0.404 75	0.643 78	0.570 57	0.507 67	0.935 76	0.414 75	0.046 102	0.510 74	0.702 60	0.602 73	0.705 66	0.549 71	0.859 70	0.773 76	0.534 82
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 76	0.760 52	0.667 84	0.649 81	0.521 80	0.793 66	0.457 50	0.648 75	0.528 66	0.434 85	0.947 50	0.401 79	0.153 91	0.454 83	0.721 56	0.648 58	0.717 60	0.536 77	0.904 31	0.765 78	0.485 90
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer	0.600 77	0.634 84	0.743 56	0.697 66	0.601 59	0.781 71	0.437 66	0.585 85	0.493 75	0.446 80	0.933 81	0.394 80	0.011 104	0.654 33	0.661 69	0.603 72	0.733 55	0.526 79	0.832 76	0.761 80	0.480 91

dtc_net	0.596 78	0.683 76	0.725 63	0.715 60	0.549 76	0.803 60	0.444 61	0.647 76	0.493 75	0.495 69	0.941 67	0.409 76	0.000 106	0.424 88	0.544 76	0.598 75	0.703 68	0.522 80	0.912 28	0.792 65	0.520 85

LAP-D	0.594 79	0.720 69	0.692 77	0.637 86	0.456 89	0.773 76	0.391 81	0.730 58	0.587 48	0.445 82	0.940 70	0.381 83	0.288 29	0.434 86	0.453 87	0.591 78	0.649 83	0.581 56	0.777 84	0.749 84	0.610 54

DPC	0.592 80	0.720 69	0.700 71	0.602 90	0.480 85	0.762 81	0.380 84	0.713 64	0.585 51	0.437 83	0.940 70	0.369 85	0.288 29	0.434 86	0.509 83	0.590 80	0.639 88	0.567 61	0.772 85	0.755 82	0.592 64
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet	0.589 81	0.766 51	0.659 87	0.683 69	0.470 88	0.740 86	0.387 82	0.620 81	0.490 77	0.476 74	0.922 88	0.355 88	0.245 56	0.511 73	0.511 82	0.571 83	0.643 86	0.493 87	0.872 61	0.762 79	0.600 60

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

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

SQN_0.1%	0.569 84	0.676 78	0.696 74	0.657 78	0.497 82	0.779 74	0.424 68	0.548 89	0.515 69	0.376 90	0.902 97	0.422 72	0.357 4	0.379 91	0.456 86	0.596 77	0.659 82	0.544 72	0.685 94	0.665 98	0.556 77

TextureNet	0.566 85	0.672 80	0.664 85	0.671 74	0.494 83	0.719 87	0.445 58	0.678 71	0.411 95	0.396 88	0.935 76	0.356 87	0.225 61	0.412 89	0.535 78	0.565 84	0.636 89	0.464 91	0.794 83	0.680 95	0.568 71
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet	0.562 86	0.648 82	0.700 71	0.770 46	0.586 65	0.687 91	0.333 90	0.650 74	0.514 70	0.475 75	0.906 94	0.359 86	0.223 63	0.340 93	0.442 88	0.422 97	0.668 80	0.501 84	0.708 92	0.779 72	0.534 82

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

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

PanopticFusion-label	0.529 89	0.491 99	0.688 80	0.604 89	0.386 94	0.632 98	0.225 103	0.705 67	0.434 92	0.293 98	0.815 101	0.348 89	0.241 57	0.499 77	0.669 65	0.507 88	0.649 83	0.442 97	0.796 82	0.602 101	0.561 74
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak	0.516 90	0.676 78	0.591 99	0.609 87	0.442 90	0.774 75	0.335 89	0.597 82	0.422 94	0.357 93	0.932 82	0.341 90	0.094 98	0.298 95	0.528 81	0.473 93	0.676 77	0.495 86	0.602 100	0.721 90	0.349 101

Online SegFusion	0.515 91	0.607 89	0.644 90	0.579 92	0.434 91	0.630 99	0.353 87	0.628 80	0.440 90	0.410 86	0.762 104	0.307 93	0.167 86	0.520 71	0.403 91	0.516 87	0.565 93	0.447 95	0.678 95	0.701 92	0.514 87
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF	0.501 92	0.558 94	0.608 97	0.424 103	0.478 86	0.690 90	0.246 99	0.586 84	0.468 83	0.450 79	0.911 92	0.394 80	0.160 89	0.438 84	0.212 100	0.432 96	0.541 98	0.475 90	0.742 88	0.727 88	0.477 92

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

3DMV	0.484 94	0.484 100	0.538 101	0.643 84	0.424 92	0.606 102	0.310 91	0.574 86	0.433 93	0.378 89	0.796 102	0.301 94	0.214 67	0.537 69	0.208 101	0.472 94	0.507 102	0.413 100	0.693 93	0.602 101	0.539 80
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 95	0.577 92	0.611 95	0.356 105	0.321 102	0.715 88	0.299 95	0.376 99	0.328 102	0.319 96	0.944 62	0.285 96	0.164 87	0.216 103	0.229 98	0.484 91	0.545 97	0.456 93	0.755 86	0.709 91	0.475 93
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 96	0.679 77	0.604 98	0.578 93	0.380 95	0.682 92	0.291 96	0.106 105	0.483 80	0.258 103	0.920 89	0.258 100	0.025 103	0.231 102	0.325 94	0.480 92	0.560 95	0.463 92	0.725 90	0.666 97	0.231 105
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 97	0.474 101	0.623 93	0.463 99	0.366 97	0.651 95	0.310 91	0.389 98	0.349 100	0.330 95	0.937 73	0.271 98	0.126 95	0.285 96	0.224 99	0.350 102	0.577 92	0.445 96	0.625 98	0.723 89	0.394 97
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET2	0.442 98	0.548 95	0.548 100	0.597 91	0.363 98	0.628 100	0.300 93	0.292 100	0.374 97	0.307 97	0.881 98	0.268 99	0.186 79	0.238 100	0.204 102	0.407 98	0.506 103	0.449 94	0.667 96	0.620 100	0.462 95

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

SPLAT Net	0.393 102	0.472 102	0.511 102	0.606 88	0.311 103	0.656 93	0.245 100	0.405 96	0.328 102	0.197 104	0.927 85	0.227 103	0.000 106	0.001 107	0.249 97	0.271 105	0.510 100	0.383 103	0.593 101	0.699 93	0.267 103
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF	0.383 103	0.297 105	0.491 103	0.432 102	0.358 99	0.612 101	0.274 97	0.116 104	0.411 95	0.265 101	0.904 95	0.229 102	0.079 100	0.250 98	0.185 103	0.320 103	0.510 100	0.385 102	0.548 102	0.597 104	0.394 97

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

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

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

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

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

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

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

ISBNet	0.763 6	1.000 1	0.873 6	0.717 25	0.666 10	0.858 12	0.508 3	0.667 24	0.764 4	0.643 6	0.676 6	0.688 7	0.825 1	1.000 1	0.773 5	0.741 28	0.777 6	1.000 1	0.556 18
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
SoftGroup	0.761 7	1.000 1	0.808 18	0.845 8	0.716 2	0.862 10	0.243 18	0.824 4	0.655 12	0.620 7	0.734 2	0.699 6	0.791 6	0.981 26	0.716 8	0.844 6	0.769 8	1.000 1	0.594 10
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
TD3D	0.751 8	1.000 1	0.774 29	0.867 7	0.621 14	0.934 1	0.404 8	0.706 19	0.812 1	0.605 9	0.633 12	0.626 13	0.690 11	1.000 1	0.640 19	0.820 12	0.777 5	1.000 1	0.612 5

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

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

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

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

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

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

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

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

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

PointGroup	0.636 28	1.000 1	0.765 32	0.624 33	0.505 35	0.797 25	0.116 32	0.696 20	0.384 32	0.441 28	0.559 23	0.476 23	0.596 29	1.000 1	0.666 12	0.756 24	0.556 39	0.997 31	0.513 22
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group	0.635 29	0.667 42	0.797 25	0.714 27	0.562 23	0.774 30	0.146 25	0.810 8	0.429 27	0.476 23	0.546 26	0.399 33	0.633 24	1.000 1	0.632 20	0.722 32	0.609 26	1.000 1	0.514 21
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
DENet	0.629 30	1.000 1	0.797 24	0.608 34	0.589 19	0.627 43	0.219 20	0.882 1	0.310 37	0.402 40	0.383 43	0.396 34	0.650 14	1.000 1	0.663 14	0.543 51	0.691 17	1.000 1	0.568 15

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Virtual MVFusion (R)	0.745 1	0.861 1	0.839 1	0.881 1	0.672 1	0.512 1	0.422 15	0.898 1	0.723 1	0.714 1	0.954 2	0.454 1	0.509 1	0.773 1	0.895 1	0.756 1	0.820 1	0.653 1	0.935 1	0.891 1	0.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2D	0.670 2	0.822 3	0.795 3	0.836 2	0.659 2	0.481 2	0.451 11	0.769 3	0.656 3	0.567 3	0.931 3	0.395 4	0.390 4	0.700 3	0.534 3	0.689 9	0.770 2	0.574 3	0.865 6	0.831 3	0.675 4
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
CU-Hybrid-2D Net	0.636 3	0.825 2	0.820 2	0.179 20	0.648 3	0.463 3	0.549 2	0.742 6	0.676 2	0.628 2	0.961 1	0.420 2	0.379 5	0.684 6	0.381 15	0.732 2	0.723 3	0.599 2	0.827 13	0.851 2	0.634 6

CMX	0.613 4	0.681 7	0.725 9	0.502 12	0.634 5	0.297 15	0.478 9	0.830 2	0.651 4	0.537 6	0.924 4	0.375 5	0.315 12	0.686 5	0.451 12	0.714 4	0.543 18	0.504 5	0.894 4	0.823 4	0.688 3

DMMF_3d	0.605 5	0.651 8	0.744 7	0.782 3	0.637 4	0.387 4	0.536 3	0.732 7	0.590 6	0.540 5	0.856 18	0.359 9	0.306 13	0.596 11	0.539 2	0.627 18	0.706 4	0.497 7	0.785 18	0.757 16	0.476 19

MCA-Net	0.595 6	0.533 17	0.756 6	0.746 4	0.590 8	0.334 7	0.506 6	0.670 12	0.587 7	0.500 10	0.905 8	0.366 8	0.352 8	0.601 10	0.506 6	0.669 15	0.648 7	0.501 6	0.839 12	0.769 12	0.516 18

RFBNet	0.592 7	0.616 9	0.758 5	0.659 5	0.581 9	0.330 8	0.469 10	0.655 15	0.543 12	0.524 7	0.924 4	0.355 10	0.336 10	0.572 14	0.479 8	0.671 13	0.648 7	0.480 9	0.814 16	0.814 5	0.614 9

FAN_NV_RVC	0.586 8	0.510 18	0.764 4	0.079 23	0.620 7	0.330 8	0.494 7	0.753 4	0.573 8	0.556 4	0.884 13	0.405 3	0.303 14	0.718 2	0.452 11	0.672 12	0.658 5	0.509 4	0.898 3	0.813 6	0.727 2

DCRedNet	0.583 9	0.682 6	0.723 10	0.542 11	0.510 17	0.310 12	0.451 11	0.668 13	0.549 11	0.520 8	0.920 6	0.375 5	0.446 2	0.528 17	0.417 13	0.670 14	0.577 15	0.478 10	0.862 7	0.806 7	0.628 8

MIX6D_RVC	0.582 10	0.695 4	0.687 14	0.225 18	0.632 6	0.328 10	0.550 1	0.748 5	0.623 5	0.494 13	0.890 11	0.350 12	0.254 20	0.688 4	0.454 10	0.716 3	0.597 14	0.489 8	0.881 5	0.768 13	0.575 12

SSMA	0.577 11	0.695 4	0.716 12	0.439 14	0.563 11	0.314 11	0.444 13	0.719 8	0.551 10	0.503 9	0.887 12	0.346 13	0.348 9	0.603 9	0.353 17	0.709 5	0.600 12	0.457 12	0.901 2	0.786 8	0.599 11
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
UNIV_CNP_RVC_UE	0.566 12	0.569 16	0.686 16	0.435 15	0.524 14	0.294 16	0.421 16	0.712 9	0.543 12	0.463 15	0.872 14	0.320 14	0.363 7	0.611 8	0.477 9	0.686 10	0.627 9	0.443 15	0.862 7	0.775 11	0.639 5

EMSAFormer	0.564 13	0.581 13	0.736 8	0.564 10	0.546 13	0.219 20	0.517 4	0.675 11	0.486 17	0.427 19	0.904 9	0.352 11	0.320 11	0.589 12	0.528 4	0.708 6	0.464 21	0.413 19	0.847 11	0.786 8	0.611 10

SN_RN152pyrx8_RVC	0.546 14	0.572 14	0.663 18	0.638 7	0.518 15	0.298 14	0.366 21	0.633 18	0.510 15	0.446 17	0.864 16	0.296 17	0.267 17	0.542 16	0.346 18	0.704 7	0.575 16	0.431 16	0.853 10	0.766 14	0.630 7

UDSSEG_RVC	0.545 15	0.610 11	0.661 19	0.588 8	0.556 12	0.268 18	0.482 8	0.642 17	0.572 9	0.475 14	0.836 20	0.312 15	0.367 6	0.630 7	0.189 20	0.639 17	0.495 20	0.452 13	0.826 14	0.756 17	0.541 14

segfomer with 6d	0.542 16	0.594 12	0.687 14	0.146 21	0.579 10	0.308 13	0.515 5	0.703 10	0.472 18	0.498 11	0.868 15	0.369 7	0.282 15	0.589 12	0.390 14	0.701 8	0.556 17	0.416 18	0.860 9	0.759 15	0.539 16

FuseNet	0.535 17	0.570 15	0.681 17	0.182 19	0.512 16	0.290 17	0.431 14	0.659 14	0.504 16	0.495 12	0.903 10	0.308 16	0.428 3	0.523 18	0.365 16	0.676 11	0.621 11	0.470 11	0.762 19	0.779 10	0.541 14
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++	0.503 18	0.613 10	0.722 11	0.418 16	0.358 23	0.337 6	0.370 20	0.479 21	0.443 19	0.368 21	0.907 7	0.207 20	0.213 22	0.464 21	0.525 5	0.618 19	0.657 6	0.450 14	0.788 17	0.721 20	0.408 22
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)	0.498 19	0.481 21	0.612 20	0.579 9	0.456 19	0.343 5	0.384 18	0.623 19	0.525 14	0.381 20	0.845 19	0.254 19	0.264 19	0.557 15	0.182 21	0.581 21	0.598 13	0.429 17	0.760 20	0.661 22	0.446 21
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVC	0.485 20	0.505 19	0.709 13	0.092 22	0.427 20	0.241 19	0.411 17	0.654 16	0.385 23	0.457 16	0.861 17	0.053 23	0.279 16	0.503 19	0.481 7	0.645 16	0.626 10	0.365 21	0.748 21	0.725 19	0.529 17
John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet	0.475 21	0.490 20	0.581 21	0.289 17	0.507 18	0.067 23	0.379 19	0.610 20	0.417 21	0.435 18	0.822 22	0.278 18	0.267 17	0.503 19	0.228 19	0.616 20	0.533 19	0.375 20	0.820 15	0.729 18	0.560 13

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

Presenting the ScanNet200 Benchmark

ScanNet200 Benchmark

ScanNet200 3D Semantic Label Benchmark

ScanNet200 3D Semantic Instance Benchmark

ScanNet Benchmark

3D Semantic Label Benchmark

3D Semantic Instance Benchmark

2D Semantic Label Benchmark

2D Semantic Instance Benchmark

Scene Type Classification Benchmark

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

UniDet_RVC	0.205 1	0.381 1	0.323 1	0.037 1	0.226 1	0.177 1	0.063 1	0.277 1	0.120 1	0.067 1	0.131 1	0.074 2	0.317 1	0.080 1	0.235 1	0.289 1	0.141 1	0.678 1	0.080 1

MaskRCNN_ScanNet	0.119 2	0.129 2	0.212 2	0.002 2	0.112 2	0.148 2	0.014 2	0.205 2	0.044 2	0.066 2	0.078 2	0.095 1	0.142 2	0.030 2	0.128 2	0.139 2	0.080 2	0.459 2	0.057 2
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17

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

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

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