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

ALS-MinkowskiNet	0.414 2	0.610 2	0.322 3	0.271 2	0.852 1	0.710 2	0.973 1	0.572 4	0.719 3	0.795 2	0.477 6	0.506 2	0.601 3	0.000 14	0.804 5	0.646 3	0.804 2	0.344 2	0.777 1	0.984 1	0.671 1	0.879 2	0.936 1	0.342 5	0.632 7	0.449 4	0.817 3	0.475 10	0.723 2	0.798 1	0.376 8	0.832 2	0.693 1	0.031 9	0.564 1	0.510 13	0.000 1	0.893 3	0.905 1	0.672 16	0.314 1	0.000 7	0.718 1	0.153 3	0.542 2	0.397 3	0.726 3	0.752 8	0.252 8	0.226 2	0.916 2	0.800 1	0.047 16	0.807 3	0.769 1	0.709 3	0.630 3	0.769 1	0.217 10	0.000 3	0.285 1	0.598 4	0.846 10	0.535 1	0.956 4	0.000 7	0.137 11	0.784 2	0.464 7	0.463 13	0.230 12	0.000 1	0.598 3	0.662 9	0.000 4	0.087 2	0.000 1	0.135 3	0.900 2	0.780 11	0.703 2	0.741 1	0.571 2	0.149 9	0.697 7	0.646 2	0.000 3	0.076 2	0.000 1	0.025 11	0.000 4	0.106 6	0.981 1	0.000 1	0.043 7	0.113 4	0.888 2	0.248 15	0.404 4	0.252 6	0.314 1	0.220 7	0.245 2	0.466 7	0.366 2	0.159 2	0.000 4	0.149 8	0.690 2	0.000 3	0.531 5	0.253 3	0.285 6	0.460 1	0.440 5	0.813 1	0.230 3	0.283 6	0.159 11	0.000 1	0.728 1	0.666 5	0.958 1	0.000 1	0.021 5	0.252 8	0.118 5	0.000 7	0.445 3	0.223 10	0.285 1	0.194 3	0.390 2	0.000 1	0.475 4	0.842 7	0.000 1	0.455 3	0.000 1	0.250 7	0.458 8	0.000 1	0.865 1	0.000 1	0.000 1	0.635 1	0.359 5	0.972 1	0.087 3	0.447 1	0.000 1	0.000 9	0.000 1	0.129 2	0.532 6	0.446 8	0.503 5	0.071 13	0.135 12	0.699 4	0.717 2	0.097 2	0.000 1	0.665 1	0.000 2	0.000 2	1.000 1	0.752 6	0.000 3	0.000 1	0.000 1	0.142 9	0.200 1	0.259 1	1.000 1	0.000 1
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. arxiv
DITR	0.449 1	0.629 1	0.392 1	0.289 1	0.851 2	0.727 1	0.969 4	0.600 1	0.741 2	0.805 1	0.519 1	0.480 3	0.636 1	0.014 10	0.867 1	0.680 1	0.849 1	0.318 3	0.753 2	0.982 2	0.508 12	0.871 6	0.934 2	0.482 1	0.596 11	0.551 2	0.804 4	0.508 6	0.729 1	0.718 2	0.417 4	0.886 1	0.664 3	0.000 17	0.500 2	0.698 1	0.000 1	0.913 1	0.901 3	0.766 7	0.113 12	0.000 7	0.617 5	0.168 2	0.650 1	0.477 1	0.826 1	0.962 1	0.348 3	0.300 1	0.947 1	0.776 2	0.160 3	0.889 1	0.651 5	0.720 2	0.700 1	0.728 3	0.317 1	0.000 3	0.238 5	0.664 1	0.869 4	0.514 2	0.998 1	0.313 3	0.138 10	0.815 1	0.828 1	0.622 2	0.421 5	0.000 1	0.823 1	0.817 1	0.000 4	0.000 9	0.000 1	0.157 2	0.866 3	0.991 1	0.805 1	0.660 4	0.571 2	0.043 12	0.709 6	0.642 3	0.000 3	0.000 7	0.000 1	0.028 10	0.018 3	0.134 3	0.967 2	0.000 1	0.150 2	0.130 2	0.949 1	0.855 1	0.580 1	0.262 5	0.314 1	0.230 5	0.222 4	0.498 5	0.367 1	0.153 3	0.869 1	0.334 2	0.397 8	0.000 3	0.904 1	0.486 2	1.000 1	0.423 4	0.484 1	0.632 6	0.716 1	0.733 2	0.862 1	0.000 1	0.433 14	0.710 1	0.851 2	0.000 1	0.034 4	0.315 3	0.385 1	0.000 7	0.001 9	0.268 9	0.066 11	0.000 8	0.278 4	0.000 1	0.978 1	0.839 8	0.000 1	0.448 4	0.000 1	0.579 1	0.403 12	0.000 1	0.647 3	0.000 1	0.000 1	0.411 3	0.315 6	0.904 7	0.420 1	0.392 2	0.000 1	0.091 6	0.000 1	0.128 3	0.564 3	0.591 3	0.568 2	0.079 9	0.139 9	1.000 1	0.714 3	0.178 1	0.000 1	0.606 3	0.000 2	0.000 2	0.148 6	0.983 1	0.000 3	0.000 1	0.000 1	0.374 2	0.000 7	0.000 3	0.662 4	0.000 1
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation.
PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 3	0.851 2	0.687 6	0.971 2	0.586 2	0.755 1	0.752 7	0.505 2	0.404 7	0.575 5	0.000 14	0.848 2	0.616 4	0.761 3	0.349 1	0.738 3	0.978 3	0.546 6	0.860 8	0.926 3	0.346 4	0.654 3	0.384 7	0.828 1	0.523 4	0.699 3	0.583 6	0.387 7	0.822 3	0.688 2	0.118 4	0.474 3	0.603 5	0.000 1	0.832 8	0.903 2	0.753 9	0.140 10	0.000 7	0.650 3	0.109 5	0.520 3	0.457 2	0.497 10	0.871 4	0.281 4	0.192 5	0.887 4	0.748 3	0.168 2	0.727 7	0.733 2	0.740 1	0.644 2	0.714 5	0.190 13	0.000 3	0.256 3	0.449 10	0.914 1	0.514 2	0.759 15	0.337 1	0.172 6	0.692 7	0.617 3	0.636 1	0.325 7	0.000 1	0.641 2	0.782 2	0.000 4	0.065 3	0.000 1	0.000 6	0.842 4	0.903 2	0.661 4	0.662 3	0.612 1	0.405 2	0.731 4	0.566 4	0.000 3	0.000 7	0.000 1	0.017 15	0.301 1	0.088 7	0.941 3	0.000 1	0.077 4	0.000 10	0.717 8	0.790 2	0.310 12	0.026 17	0.264 4	0.349 1	0.220 5	0.397 12	0.366 2	0.115 13	0.000 4	0.337 1	0.463 6	0.000 3	0.531 5	0.218 4	0.593 2	0.455 2	0.469 2	0.708 3	0.210 4	0.592 4	0.108 16	0.000 1	0.728 1	0.682 3	0.671 8	0.000 1	0.000 11	0.407 1	0.136 4	0.022 3	0.575 1	0.436 4	0.259 3	0.428 1	0.048 6	0.000 1	0.000 5	0.879 5	0.000 1	0.480 2	0.000 1	0.133 9	0.597 2	0.000 1	0.690 2	0.000 1	0.000 1	0.009 16	0.000 15	0.921 3	0.000 9	0.151 5	0.000 1	0.000 9	0.000 1	0.109 8	0.494 11	0.622 2	0.394 9	0.073 12	0.141 7	0.798 2	0.528 8	0.026 5	0.000 1	0.551 5	0.000 2	0.000 2	0.134 7	0.717 8	0.000 3	0.000 1	0.000 1	0.188 4	0.000 7	0.000 3	0.791 3	0.000 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
OA-CNN-L_ScanNet200	0.333 11	0.558 5	0.269 9	0.124 13	0.821 5	0.703 3	0.946 6	0.569 5	0.662 4	0.748 9	0.487 3	0.455 4	0.572 7	0.000 14	0.789 9	0.534 9	0.736 9	0.271 8	0.713 4	0.949 6	0.498 14	0.877 3	0.860 11	0.332 7	0.706 1	0.474 3	0.788 7	0.406 13	0.637 6	0.495 11	0.355 11	0.805 7	0.592 12	0.015 13	0.396 8	0.602 6	0.000 1	0.799 11	0.876 7	0.713 13	0.276 2	0.000 7	0.493 13	0.080 9	0.448 14	0.363 5	0.661 4	0.833 6	0.262 6	0.125 7	0.823 12	0.665 9	0.076 9	0.720 8	0.557 10	0.637 9	0.517 9	0.672 10	0.227 8	0.000 3	0.158 12	0.496 8	0.843 11	0.352 10	0.835 13	0.000 7	0.103 14	0.711 5	0.527 4	0.526 6	0.320 8	0.000 1	0.568 6	0.625 11	0.067 1	0.000 9	0.000 1	0.001 5	0.806 6	0.836 7	0.621 10	0.591 8	0.373 8	0.314 5	0.668 10	0.398 9	0.003 2	0.000 7	0.000 1	0.016 16	0.024 2	0.043 13	0.906 6	0.000 1	0.052 6	0.000 10	0.384 12	0.330 12	0.342 8	0.100 12	0.223 7	0.183 13	0.112 7	0.476 6	0.313 7	0.130 9	0.196 3	0.112 12	0.370 11	0.000 3	0.234 12	0.071 9	0.160 7	0.403 6	0.398 13	0.492 14	0.197 6	0.076 13	0.272 5	0.000 1	0.200 16	0.560 10	0.735 7	0.000 1	0.000 11	0.000 12	0.110 8	0.002 6	0.021 8	0.412 5	0.000 12	0.000 8	0.000 11	0.000 1	0.000 5	0.794 11	0.000 1	0.445 5	0.000 1	0.022 10	0.509 7	0.000 1	0.517 13	0.000 1	0.000 1	0.001 17	0.245 7	0.915 5	0.024 6	0.089 7	0.000 1	0.262 3	0.000 1	0.103 11	0.524 7	0.392 11	0.515 4	0.013 17	0.251 4	0.411 13	0.662 4	0.001 11	0.000 1	0.473 12	0.000 2	0.000 2	0.150 5	0.699 9	0.000 3	0.000 1	0.000 1	0.166 6	0.000 7	0.024 2	0.000 11	0.000 1

PPT-SpUNet-F.T.	0.332 12	0.556 6	0.270 7	0.123 14	0.816 6	0.682 9	0.946 6	0.549 10	0.657 8	0.756 5	0.459 7	0.376 9	0.550 11	0.001 12	0.807 4	0.616 4	0.727 12	0.267 9	0.691 5	0.942 11	0.530 9	0.872 5	0.874 8	0.330 8	0.542 14	0.374 8	0.792 5	0.400 14	0.673 4	0.572 7	0.433 2	0.793 9	0.623 7	0.008 16	0.351 10	0.594 8	0.000 1	0.783 13	0.876 7	0.833 4	0.213 6	0.000 7	0.537 8	0.091 7	0.519 4	0.304 8	0.620 8	0.942 2	0.264 5	0.124 8	0.855 7	0.695 5	0.086 8	0.646 10	0.506 16	0.658 7	0.535 6	0.715 4	0.314 2	0.000 3	0.241 4	0.608 3	0.897 2	0.359 8	0.858 11	0.000 7	0.076 17	0.611 11	0.392 12	0.509 7	0.378 6	0.000 1	0.579 4	0.565 15	0.000 4	0.000 9	0.000 1	0.000 6	0.755 7	0.806 9	0.661 4	0.572 13	0.350 9	0.181 7	0.660 12	0.300 14	0.000 3	0.000 7	0.000 1	0.023 12	0.000 4	0.042 14	0.930 4	0.000 1	0.000 10	0.077 7	0.584 9	0.392 10	0.339 9	0.185 10	0.171 12	0.308 2	0.006 13	0.563 3	0.256 8	0.150 4	0.000 4	0.002 16	0.345 12	0.000 3	0.045 14	0.197 5	0.063 11	0.323 11	0.453 4	0.600 8	0.163 11	0.037 15	0.349 4	0.000 1	0.672 3	0.679 4	0.753 5	0.000 1	0.000 11	0.000 12	0.117 6	0.000 7	0.000 10	0.291 8	0.000 12	0.000 8	0.039 7	0.000 1	0.000 5	0.899 2	0.000 1	0.374 11	0.000 1	0.000 12	0.545 5	0.000 1	0.634 5	0.000 1	0.000 1	0.074 13	0.223 8	0.914 6	0.000 9	0.021 9	0.000 1	0.000 9	0.000 1	0.112 6	0.498 10	0.649 1	0.383 10	0.095 2	0.135 12	0.449 11	0.432 12	0.008 9	0.000 1	0.518 7	0.000 2	0.000 2	0.000 11	0.796 5	0.000 3	0.000 1	0.000 1	0.138 13	0.000 7	0.000 3	0.000 11	0.000 1
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
ODIN - Sem200	0.368 4	0.562 4	0.297 4	0.207 4	0.800 10	0.669 13	0.940 10	0.575 3	0.654 9	0.749 8	0.487 3	0.589 1	0.609 2	0.001 12	0.769 12	0.561 8	0.752 6	0.274 5	0.682 6	0.926 13	0.554 4	0.833 14	0.921 4	0.389 2	0.599 10	0.591 1	0.787 8	0.550 2	0.657 5	0.610 4	0.334 13	0.803 8	0.661 4	0.090 6	0.408 7	0.373 15	0.000 1	0.912 2	0.796 17	0.501 17	0.169 8	0.000 7	0.641 4	0.196 1	0.380 17	0.397 3	0.641 5	0.740 9	0.862 1	0.213 3	0.857 6	0.685 7	0.216 1	0.578 16	0.557 10	0.685 5	0.523 8	0.581 16	0.312 3	0.000 3	0.065 15	0.000 17	0.871 3	0.359 8	0.988 2	0.321 2	0.090 16	0.704 6	0.631 2	0.393 15	0.246 11	0.000 1	0.482 8	0.565 15	0.000 4	0.000 9	0.000 1	0.181 1	0.913 1	0.468 16	0.632 8	0.642 5	0.259 11	0.000 17	0.832 1	0.663 1	0.000 3	0.081 1	0.000 1	0.048 2	0.000 4	0.376 1	0.898 7	0.000 1	0.157 1	0.000 10	0.870 3	0.000 17	0.400 5	0.265 4	0.242 5	0.227 6	0.539 1	0.370 14	0.214 13	0.129 10	0.000 4	0.131 10	0.054 17	0.000 3	0.358 9	0.491 1	0.462 4	0.434 3	0.346 15	0.454 15	0.316 2	0.814 1	0.828 2	0.000 1	0.000 17	0.220 17	0.612 11	0.000 1	0.000 11	0.373 2	0.378 2	0.000 7	0.429 4	0.152 11	0.077 9	0.166 4	0.202 5	0.000 1	0.000 5	0.441 14	0.000 1	0.440 6	0.000 1	0.000 12	0.655 1	0.000 1	0.626 7	0.000 1	0.000 1	0.228 9	0.487 1	0.784 16	0.000 9	0.301 3	0.000 1	0.426 2	0.000 1	0.108 9	0.460 13	0.590 4	0.775 1	0.088 6	0.119 15	0.485 9	0.791 1	0.000 12	0.000 1	0.256 17	0.000 2	0.000 2	0.000 11	0.885 3	0.303 1	0.000 1	0.000 1	0.127 16	0.000 7	0.000 3	0.894 2	0.000 1
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024
PonderV2 ScanNet200	0.346 6	0.552 8	0.270 8	0.175 9	0.810 7	0.682 9	0.950 5	0.560 7	0.641 10	0.761 3	0.398 13	0.357 10	0.570 8	0.113 2	0.804 5	0.603 6	0.750 7	0.283 4	0.681 7	0.952 5	0.548 5	0.874 4	0.852 13	0.290 12	0.700 2	0.356 11	0.792 5	0.445 12	0.545 13	0.436 12	0.351 12	0.787 10	0.611 8	0.050 8	0.290 14	0.519 12	0.000 1	0.825 10	0.888 5	0.842 3	0.259 3	0.100 2	0.558 7	0.070 12	0.497 7	0.247 14	0.457 11	0.889 3	0.248 9	0.106 10	0.817 13	0.691 6	0.094 7	0.729 6	0.636 6	0.620 12	0.503 11	0.660 13	0.243 7	0.000 3	0.212 7	0.590 5	0.860 8	0.400 5	0.881 9	0.000 7	0.202 2	0.622 10	0.408 11	0.499 8	0.261 10	0.000 1	0.385 10	0.636 10	0.000 4	0.000 9	0.000 1	0.000 6	0.433 16	0.843 6	0.660 6	0.574 12	0.481 4	0.336 4	0.677 9	0.486 6	0.000 3	0.030 3	0.000 1	0.034 6	0.000 4	0.080 8	0.869 10	0.000 1	0.000 10	0.000 10	0.540 10	0.727 3	0.232 17	0.115 11	0.186 10	0.193 9	0.000 14	0.403 11	0.326 6	0.103 14	0.000 4	0.290 4	0.392 9	0.000 3	0.346 10	0.062 10	0.424 5	0.375 7	0.431 6	0.667 4	0.115 14	0.082 12	0.239 7	0.000 1	0.504 12	0.606 8	0.584 12	0.000 1	0.002 9	0.186 10	0.104 10	0.000 7	0.394 5	0.384 6	0.083 8	0.000 8	0.007 9	0.000 1	0.000 5	0.880 4	0.000 1	0.377 10	0.000 1	0.263 6	0.565 3	0.000 1	0.608 9	0.000 1	0.000 1	0.304 7	0.009 11	0.924 2	0.000 9	0.000 11	0.000 1	0.000 9	0.000 1	0.128 3	0.584 2	0.475 7	0.412 8	0.076 11	0.269 3	0.621 6	0.509 9	0.010 7	0.000 1	0.491 11	0.063 1	0.000 2	0.472 4	0.880 4	0.000 3	0.000 1	0.000 1	0.179 5	0.125 2	0.000 3	0.441 10	0.000 1
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
OctFormer ScanNet200	0.326 13	0.539 10	0.265 10	0.131 12	0.806 8	0.670 12	0.943 9	0.535 12	0.662 4	0.705 16	0.423 9	0.407 6	0.505 13	0.003 11	0.765 13	0.582 7	0.686 15	0.227 16	0.680 8	0.943 10	0.601 2	0.854 10	0.892 6	0.335 6	0.417 17	0.357 10	0.724 10	0.453 11	0.632 7	0.596 5	0.432 3	0.783 11	0.512 16	0.021 12	0.244 15	0.637 2	0.000 1	0.787 12	0.873 9	0.743 11	0.000 17	0.000 7	0.534 9	0.110 4	0.499 6	0.289 10	0.626 7	0.620 12	0.168 15	0.204 4	0.849 10	0.679 8	0.117 5	0.633 11	0.684 3	0.650 8	0.552 5	0.684 9	0.312 3	0.000 3	0.175 11	0.429 11	0.865 5	0.413 4	0.837 12	0.000 7	0.145 8	0.626 9	0.451 8	0.487 11	0.513 3	0.000 1	0.529 7	0.613 12	0.000 4	0.033 6	0.000 1	0.000 6	0.828 5	0.871 3	0.622 9	0.587 9	0.411 7	0.137 10	0.645 14	0.343 12	0.000 3	0.000 7	0.000 1	0.022 13	0.000 4	0.026 17	0.829 11	0.000 1	0.022 8	0.089 6	0.842 4	0.253 14	0.318 11	0.296 2	0.178 11	0.291 3	0.224 3	0.584 2	0.200 14	0.132 8	0.000 4	0.128 11	0.227 13	0.000 3	0.230 13	0.047 11	0.149 8	0.331 10	0.412 9	0.618 7	0.164 10	0.102 11	0.522 3	0.000 1	0.655 4	0.378 12	0.469 15	0.000 1	0.000 11	0.000 12	0.105 9	0.000 7	0.000 10	0.483 3	0.000 12	0.000 8	0.028 8	0.000 1	0.000 5	0.906 1	0.000 1	0.339 15	0.000 1	0.000 12	0.457 9	0.000 1	0.612 8	0.000 1	0.000 1	0.408 4	0.000 15	0.900 10	0.000 9	0.000 11	0.000 1	0.029 8	0.000 1	0.074 15	0.455 15	0.479 6	0.427 7	0.079 9	0.140 8	0.496 8	0.414 14	0.022 6	0.000 1	0.471 13	0.000 2	0.000 2	0.000 11	0.722 7	0.000 3	0.000 1	0.000 1	0.138 13	0.000 7	0.000 3	0.000 11	0.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
L3DETR-ScanNet_200	0.336 8	0.533 11	0.279 6	0.155 10	0.801 9	0.689 4	0.946 6	0.539 11	0.660 7	0.759 4	0.380 14	0.333 14	0.583 4	0.000 14	0.788 10	0.529 10	0.740 8	0.261 12	0.679 9	0.940 12	0.525 10	0.860 8	0.883 7	0.226 13	0.613 9	0.397 6	0.720 11	0.512 5	0.565 12	0.620 3	0.417 4	0.775 13	0.629 6	0.158 2	0.298 12	0.579 11	0.000 1	0.835 6	0.883 6	0.927 1	0.114 11	0.079 4	0.511 10	0.073 11	0.508 5	0.312 6	0.629 6	0.861 5	0.192 14	0.098 13	0.908 3	0.636 11	0.032 17	0.563 17	0.514 15	0.664 6	0.505 10	0.697 7	0.225 9	0.000 3	0.264 2	0.411 12	0.860 8	0.321 13	0.960 3	0.058 6	0.109 13	0.776 3	0.526 5	0.557 3	0.303 9	0.000 1	0.339 12	0.712 7	0.000 4	0.014 7	0.000 1	0.000 6	0.638 12	0.856 4	0.641 7	0.579 11	0.107 17	0.119 11	0.661 11	0.416 7	0.000 3	0.000 7	0.000 1	0.007 17	0.000 4	0.067 10	0.910 5	0.000 1	0.000 10	0.000 10	0.463 11	0.448 8	0.294 14	0.324 1	0.293 3	0.211 8	0.108 8	0.448 8	0.068 17	0.141 6	0.000 4	0.330 3	0.699 1	0.000 3	0.256 11	0.192 6	0.000 15	0.355 8	0.418 7	0.209 17	0.146 12	0.679 3	0.101 17	0.000 1	0.503 13	0.687 2	0.671 8	0.000 1	0.000 11	0.174 11	0.117 6	0.000 7	0.122 7	0.515 2	0.104 6	0.259 2	0.312 3	0.000 1	0.000 5	0.765 12	0.000 1	0.369 12	0.000 1	0.183 8	0.422 11	0.000 1	0.646 4	0.000 1	0.000 1	0.565 2	0.001 14	0.125 17	0.010 7	0.002 10	0.000 1	0.487 1	0.000 1	0.075 14	0.548 4	0.420 9	0.233 14	0.082 8	0.138 11	0.430 12	0.427 13	0.000 12	0.000 1	0.549 6	0.000 2	0.000 2	0.074 8	0.409 16	0.000 3	0.000 1	0.000 1	0.152 7	0.051 3	0.000 3	0.598 6	0.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
BFANet ScanNet200	0.360 5	0.553 7	0.293 5	0.193 5	0.827 4	0.689 4	0.970 3	0.528 13	0.661 6	0.753 6	0.436 8	0.378 8	0.469 15	0.042 7	0.810 3	0.654 2	0.760 4	0.266 10	0.659 10	0.973 4	0.574 3	0.849 11	0.897 5	0.382 3	0.546 13	0.372 9	0.698 14	0.491 9	0.617 10	0.526 10	0.436 1	0.764 14	0.476 17	0.101 5	0.409 6	0.585 10	0.000 1	0.835 6	0.901 3	0.810 5	0.102 14	0.000 7	0.688 2	0.096 6	0.483 10	0.264 12	0.612 9	0.591 16	0.358 2	0.161 6	0.863 5	0.707 4	0.128 4	0.814 2	0.669 4	0.629 10	0.563 4	0.651 14	0.258 5	0.000 3	0.194 10	0.494 9	0.806 12	0.394 6	0.953 5	0.000 7	0.233 1	0.757 4	0.508 6	0.556 4	0.476 4	0.000 1	0.573 5	0.741 6	0.000 4	0.000 9	0.000 1	0.000 6	0.000 17	0.852 5	0.678 3	0.616 6	0.460 5	0.338 3	0.710 5	0.534 5	0.000 3	0.025 4	0.000 1	0.043 3	0.000 4	0.056 12	0.493 17	0.000 1	0.000 10	0.109 5	0.785 7	0.590 6	0.298 13	0.282 3	0.143 13	0.262 4	0.053 11	0.526 4	0.337 5	0.215 1	0.000 4	0.135 9	0.510 4	0.000 3	0.596 4	0.043 14	0.511 3	0.321 12	0.459 3	0.772 2	0.124 13	0.060 14	0.266 6	0.000 1	0.574 9	0.568 9	0.653 10	0.000 1	0.093 1	0.298 4	0.239 3	0.000 7	0.516 2	0.129 14	0.284 2	0.000 8	0.431 1	0.000 1	0.000 5	0.848 6	0.000 1	0.492 1	0.000 1	0.376 3	0.522 6	0.000 1	0.469 17	0.000 1	0.000 1	0.330 6	0.151 10	0.875 14	0.000 9	0.254 4	0.000 1	0.000 9	0.000 1	0.088 13	0.661 1	0.481 5	0.255 12	0.105 1	0.139 9	0.666 5	0.641 5	0.000 12	0.000 1	0.614 2	0.000 2	0.000 2	0.000 11	0.921 2	0.000 3	0.000 1	0.000 1	0.497 1	0.000 7	0.000 3	0.000 11	0.000 1
Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis. CVPR 2025
CeCo	0.340 7	0.551 9	0.247 13	0.181 6	0.784 13	0.661 14	0.939 13	0.564 6	0.624 13	0.721 12	0.484 5	0.429 5	0.575 5	0.027 8	0.774 11	0.503 14	0.753 5	0.242 13	0.656 11	0.945 9	0.534 7	0.865 7	0.860 11	0.177 17	0.616 8	0.400 5	0.818 2	0.579 1	0.615 11	0.367 14	0.408 6	0.726 15	0.633 5	0.162 1	0.360 9	0.619 3	0.000 1	0.828 9	0.873 9	0.924 2	0.109 13	0.083 3	0.564 6	0.057 15	0.475 12	0.266 11	0.781 2	0.767 7	0.257 7	0.100 11	0.825 11	0.663 10	0.048 15	0.620 13	0.551 12	0.595 13	0.532 7	0.692 8	0.246 6	0.000 3	0.213 6	0.615 2	0.861 7	0.376 7	0.900 8	0.000 7	0.102 15	0.660 8	0.321 15	0.547 5	0.226 13	0.000 1	0.311 13	0.742 5	0.011 3	0.006 8	0.000 1	0.000 6	0.546 15	0.824 8	0.345 14	0.665 2	0.450 6	0.435 1	0.683 8	0.411 8	0.338 1	0.000 7	0.000 1	0.030 9	0.000 4	0.068 9	0.892 8	0.000 1	0.063 5	0.000 10	0.257 13	0.304 13	0.387 6	0.079 14	0.228 6	0.190 11	0.000 14	0.586 1	0.347 4	0.133 7	0.000 4	0.037 13	0.377 10	0.000 3	0.384 8	0.006 16	0.003 13	0.421 5	0.410 10	0.643 5	0.171 9	0.121 9	0.142 12	0.000 1	0.510 11	0.447 11	0.474 14	0.000 1	0.000 11	0.286 5	0.083 11	0.000 7	0.000 10	0.603 1	0.096 7	0.063 5	0.000 11	0.000 1	0.000 5	0.898 3	0.000 1	0.429 7	0.000 1	0.400 2	0.550 4	0.000 1	0.633 6	0.000 1	0.000 1	0.377 5	0.000 15	0.916 4	0.000 9	0.000 11	0.000 1	0.000 9	0.000 1	0.102 12	0.499 9	0.296 14	0.463 6	0.089 5	0.304 1	0.740 3	0.401 16	0.010 7	0.000 1	0.560 4	0.000 2	0.000 2	0.709 2	0.652 10	0.000 3	0.000 1	0.000 1	0.143 8	0.000 7	0.000 3	0.609 5	0.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
GSTran	0.334 10	0.533 12	0.250 12	0.179 8	0.799 11	0.684 7	0.940 10	0.554 9	0.633 11	0.741 10	0.405 11	0.337 12	0.560 9	0.060 5	0.794 8	0.517 13	0.732 11	0.274 5	0.647 12	0.948 7	0.459 16	0.849 11	0.864 9	0.306 9	0.648 5	0.282 14	0.717 12	0.496 7	0.624 9	0.533 8	0.363 9	0.821 4	0.573 14	0.009 15	0.411 4	0.593 9	0.000 1	0.841 5	0.873 9	0.704 14	0.242 5	0.000 7	0.495 11	0.041 16	0.487 8	0.304 8	0.439 13	0.613 13	0.133 17	0.055 16	0.853 8	0.634 12	0.075 12	0.791 5	0.601 9	0.574 16	0.483 13	0.669 11	0.217 10	0.000 3	0.198 8	0.518 6	0.782 14	0.345 11	0.914 6	0.273 5	0.193 3	0.598 14	0.440 9	0.499 8	0.570 1	0.000 1	0.381 11	0.775 4	0.000 4	0.063 5	0.000 1	0.000 6	0.712 8	0.752 13	0.507 12	0.512 16	0.158 16	0.036 13	0.773 2	0.361 11	0.000 3	0.000 7	0.000 1	0.032 7	0.000 4	0.032 15	0.651 15	0.000 1	0.000 10	0.000 10	0.831 5	0.595 4	0.273 16	0.229 7	0.200 9	0.191 10	0.000 14	0.425 9	0.233 12	0.125 11	0.000 4	0.279 5	0.213 15	0.003 1	0.608 3	0.044 12	0.138 9	0.321 12	0.408 11	0.593 10	0.198 5	0.205 8	0.139 13	0.000 1	0.614 7	0.609 7	0.838 4	0.000 1	0.014 6	0.260 6	0.080 12	0.010 5	0.000 10	0.136 13	0.136 4	0.047 6	0.000 11	0.000 1	0.787 3	0.797 10	0.000 1	0.354 14	0.000 1	0.372 4	0.357 14	0.000 1	0.507 16	0.000 1	0.000 1	0.121 11	0.423 3	0.903 8	0.028 4	0.089 7	0.000 1	0.252 4	0.000 1	0.072 17	0.465 12	0.340 12	0.189 16	0.020 16	0.011 16	0.320 16	0.606 7	0.060 3	0.000 1	0.496 9	0.000 2	0.000 2	0.070 9	0.618 13	0.000 3	0.000 1	0.000 1	0.139 11	0.047 4	0.000 3	0.558 8	0.000 1

IMFSegNet	0.334 9	0.532 13	0.251 11	0.179 7	0.799 11	0.683 8	0.940 10	0.555 8	0.631 12	0.740 11	0.406 10	0.336 13	0.560 9	0.062 4	0.795 7	0.518 12	0.733 10	0.274 5	0.646 13	0.947 8	0.458 17	0.848 13	0.862 10	0.305 10	0.649 4	0.284 13	0.713 13	0.495 8	0.626 8	0.527 9	0.363 9	0.820 5	0.574 13	0.010 14	0.411 4	0.597 7	0.000 1	0.842 4	0.873 9	0.704 14	0.246 4	0.000 7	0.495 11	0.041 16	0.486 9	0.305 7	0.444 12	0.604 15	0.134 16	0.055 16	0.852 9	0.633 13	0.076 9	0.792 4	0.612 8	0.573 17	0.484 12	0.668 12	0.216 12	0.000 3	0.197 9	0.518 6	0.784 13	0.344 12	0.908 7	0.283 4	0.190 4	0.599 13	0.439 10	0.496 10	0.569 2	0.000 1	0.392 9	0.776 3	0.000 4	0.064 4	0.000 1	0.000 6	0.710 9	0.756 12	0.508 11	0.512 16	0.159 15	0.034 14	0.773 2	0.363 10	0.000 3	0.000 7	0.000 1	0.032 7	0.000 4	0.029 16	0.648 16	0.000 1	0.000 10	0.000 10	0.830 6	0.595 4	0.274 15	0.228 8	0.206 8	0.188 12	0.000 14	0.425 9	0.237 11	0.123 12	0.000 4	0.277 6	0.214 14	0.003 1	0.610 2	0.044 12	0.124 10	0.320 14	0.408 11	0.594 9	0.196 7	0.213 7	0.139 13	0.000 1	0.615 6	0.618 6	0.839 3	0.000 1	0.014 6	0.260 6	0.080 12	0.025 2	0.000 10	0.139 12	0.135 5	0.035 7	0.000 11	0.000 1	0.793 2	0.799 9	0.000 1	0.357 13	0.000 1	0.369 5	0.359 13	0.000 1	0.512 15	0.000 1	0.000 1	0.120 12	0.424 2	0.903 8	0.027 5	0.091 6	0.000 1	0.245 5	0.000 1	0.073 16	0.457 14	0.340 12	0.191 15	0.021 15	0.009 17	0.322 15	0.608 6	0.060 3	0.000 1	0.494 10	0.000 2	0.000 2	0.068 10	0.624 11	0.000 3	0.000 1	0.000 1	0.139 11	0.047 4	0.000 3	0.561 7	0.000 1

AWCS	0.305 14	0.508 14	0.225 14	0.142 11	0.782 14	0.634 17	0.937 14	0.489 15	0.578 14	0.721 12	0.364 15	0.355 11	0.515 12	0.023 9	0.764 14	0.523 11	0.707 14	0.264 11	0.633 14	0.922 14	0.507 13	0.886 1	0.804 15	0.179 15	0.436 16	0.300 12	0.656 16	0.529 3	0.501 15	0.394 13	0.296 16	0.820 5	0.603 9	0.131 3	0.179 17	0.619 3	0.000 1	0.707 16	0.865 13	0.773 6	0.171 7	0.010 6	0.484 14	0.063 13	0.463 13	0.254 13	0.332 16	0.649 11	0.220 11	0.100 11	0.729 15	0.613 15	0.071 13	0.582 14	0.628 7	0.702 4	0.424 15	0.749 2	0.137 15	0.000 3	0.142 13	0.360 13	0.863 6	0.305 14	0.877 10	0.000 7	0.173 5	0.606 12	0.337 14	0.478 12	0.154 15	0.000 1	0.253 14	0.664 8	0.000 4	0.000 9	0.000 1	0.000 6	0.626 13	0.782 10	0.302 16	0.602 7	0.185 13	0.282 6	0.651 13	0.317 13	0.000 3	0.000 7	0.000 1	0.022 13	0.000 4	0.154 2	0.876 9	0.000 1	0.014 9	0.063 9	0.029 17	0.553 7	0.467 3	0.084 13	0.124 14	0.157 16	0.049 12	0.373 13	0.252 9	0.097 15	0.000 4	0.219 7	0.542 3	0.000 3	0.392 7	0.172 8	0.000 15	0.339 9	0.417 8	0.533 13	0.093 15	0.115 10	0.195 9	0.000 1	0.516 10	0.288 15	0.741 6	0.000 1	0.001 10	0.233 9	0.056 14	0.000 7	0.159 6	0.334 7	0.077 9	0.000 8	0.000 11	0.000 1	0.000 5	0.749 13	0.000 1	0.411 8	0.000 1	0.008 11	0.452 10	0.000 1	0.595 10	0.000 1	0.000 1	0.220 10	0.006 12	0.894 12	0.006 8	0.000 11	0.000 1	0.000 9	0.000 1	0.112 6	0.504 8	0.404 10	0.551 3	0.093 4	0.129 14	0.484 10	0.381 17	0.000 12	0.000 1	0.396 14	0.000 2	0.000 2	0.620 3	0.402 17	0.000 3	0.000 1	0.000 1	0.142 9	0.000 7	0.000 3	0.512 9	0.000 1
: Long-Tailed 3D Semantic Segmentation with Adaptive Weight Constraint and Sampling. ICRA 2024
LGround	0.272 15	0.485 15	0.184 15	0.106 15	0.778 15	0.676 11	0.932 15	0.479 17	0.572 15	0.718 14	0.399 12	0.265 15	0.453 16	0.085 3	0.745 15	0.446 15	0.726 13	0.232 15	0.622 15	0.901 15	0.512 11	0.826 15	0.786 16	0.178 16	0.549 12	0.277 15	0.659 15	0.381 15	0.518 14	0.295 17	0.323 14	0.777 12	0.599 10	0.028 10	0.321 11	0.363 16	0.000 1	0.708 15	0.858 14	0.746 10	0.063 15	0.022 5	0.457 15	0.077 10	0.476 11	0.243 15	0.402 14	0.397 17	0.233 10	0.077 15	0.720 17	0.610 16	0.103 6	0.629 12	0.437 17	0.626 11	0.446 14	0.702 6	0.190 13	0.005 1	0.058 16	0.322 14	0.702 16	0.244 15	0.768 14	0.000 7	0.134 12	0.552 15	0.279 16	0.395 14	0.147 16	0.000 1	0.207 15	0.612 13	0.000 4	0.000 9	0.000 1	0.000 6	0.658 11	0.566 14	0.323 15	0.525 15	0.229 12	0.179 8	0.467 17	0.154 16	0.000 3	0.002 5	0.000 1	0.051 1	0.000 4	0.127 4	0.703 12	0.000 1	0.000 10	0.216 1	0.112 16	0.358 11	0.547 2	0.187 9	0.092 16	0.156 17	0.055 10	0.296 15	0.252 9	0.143 5	0.000 4	0.014 14	0.398 7	0.000 3	0.028 16	0.173 7	0.000 15	0.265 16	0.348 14	0.415 16	0.179 8	0.019 16	0.218 8	0.000 1	0.597 8	0.274 16	0.565 13	0.000 1	0.012 8	0.000 12	0.039 16	0.022 3	0.000 10	0.117 15	0.000 12	0.000 8	0.000 11	0.000 1	0.000 5	0.324 16	0.000 1	0.384 9	0.000 1	0.000 12	0.251 17	0.000 1	0.566 11	0.000 1	0.000 1	0.066 14	0.404 4	0.886 13	0.199 2	0.000 11	0.000 1	0.059 7	0.000 1	0.136 1	0.540 5	0.127 17	0.295 11	0.085 7	0.143 6	0.514 7	0.413 15	0.000 12	0.000 1	0.498 8	0.000 2	0.000 2	0.000 11	0.623 12	0.000 3	0.000 1	0.000 1	0.132 15	0.000 7	0.000 3	0.000 11	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34D	0.253 16	0.463 16	0.154 17	0.102 16	0.771 16	0.650 16	0.932 15	0.483 16	0.571 16	0.710 15	0.331 16	0.250 16	0.492 14	0.044 6	0.703 16	0.419 17	0.606 17	0.227 16	0.621 16	0.865 17	0.531 8	0.771 17	0.813 14	0.291 11	0.484 15	0.242 16	0.612 17	0.282 17	0.440 17	0.351 15	0.299 15	0.622 16	0.593 11	0.027 11	0.293 13	0.310 17	0.000 1	0.757 14	0.858 14	0.737 12	0.150 9	0.164 1	0.368 17	0.084 8	0.381 16	0.142 17	0.357 15	0.720 10	0.214 12	0.092 14	0.724 16	0.596 17	0.056 14	0.655 9	0.525 14	0.581 15	0.352 17	0.594 15	0.056 17	0.000 3	0.014 17	0.224 15	0.772 15	0.205 17	0.720 16	0.000 7	0.159 7	0.531 16	0.163 17	0.294 16	0.136 17	0.000 1	0.169 16	0.589 14	0.000 4	0.000 9	0.000 1	0.002 4	0.663 10	0.466 17	0.265 17	0.582 10	0.337 10	0.016 15	0.559 15	0.084 17	0.000 3	0.000 7	0.000 1	0.036 5	0.000 4	0.125 5	0.670 13	0.000 1	0.102 3	0.071 8	0.164 15	0.406 9	0.386 7	0.046 16	0.068 17	0.159 15	0.117 6	0.284 16	0.111 16	0.094 16	0.000 4	0.000 17	0.197 16	0.000 3	0.044 15	0.013 15	0.002 14	0.228 17	0.307 17	0.588 11	0.025 17	0.545 5	0.134 15	0.000 1	0.655 4	0.302 14	0.282 17	0.000 1	0.060 2	0.000 12	0.035 17	0.000 7	0.000 10	0.097 17	0.000 12	0.000 8	0.005 10	0.000 1	0.000 5	0.096 17	0.000 1	0.334 16	0.000 1	0.000 12	0.274 16	0.000 1	0.513 14	0.000 1	0.000 1	0.280 8	0.194 9	0.897 11	0.000 9	0.000 11	0.000 1	0.000 9	0.000 1	0.108 9	0.279 17	0.189 16	0.141 17	0.059 14	0.272 2	0.307 17	0.445 10	0.003 10	0.000 1	0.353 15	0.000 2	0.026 1	0.000 11	0.581 15	0.001 2	0.000 1	0.000 1	0.093 17	0.002 6	0.000 3	0.000 11	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain	0.249 17	0.455 17	0.171 16	0.079 17	0.766 17	0.659 15	0.930 17	0.494 14	0.542 17	0.700 17	0.314 17	0.215 17	0.430 17	0.121 1	0.697 17	0.441 16	0.683 16	0.235 14	0.609 17	0.895 16	0.476 15	0.816 16	0.770 17	0.186 14	0.634 6	0.216 17	0.734 9	0.340 16	0.471 16	0.307 16	0.293 17	0.591 17	0.542 15	0.076 7	0.205 16	0.464 14	0.000 1	0.484 17	0.832 16	0.766 7	0.052 16	0.000 7	0.413 16	0.059 14	0.418 15	0.222 16	0.318 17	0.609 14	0.206 13	0.112 9	0.743 14	0.625 14	0.076 9	0.579 15	0.548 13	0.590 14	0.371 16	0.552 17	0.081 16	0.003 2	0.142 13	0.201 16	0.638 17	0.233 16	0.686 17	0.000 7	0.142 9	0.444 17	0.375 13	0.247 17	0.198 14	0.000 1	0.128 17	0.454 17	0.019 2	0.097 1	0.000 1	0.000 6	0.553 14	0.557 15	0.373 13	0.545 14	0.164 14	0.014 16	0.547 16	0.174 15	0.000 3	0.002 5	0.000 1	0.037 4	0.000 4	0.063 11	0.664 14	0.000 1	0.000 10	0.130 2	0.170 14	0.152 16	0.335 10	0.079 14	0.110 15	0.175 14	0.098 9	0.175 17	0.166 15	0.045 17	0.207 2	0.014 14	0.465 5	0.000 3	0.001 17	0.001 17	0.046 12	0.299 15	0.327 16	0.537 12	0.033 16	0.012 17	0.186 10	0.000 1	0.205 15	0.377 13	0.463 16	0.000 1	0.058 3	0.000 12	0.055 15	0.041 1	0.000 10	0.105 16	0.000 12	0.000 8	0.000 11	0.000 1	0.000 5	0.398 15	0.000 1	0.308 17	0.000 1	0.000 12	0.319 15	0.000 1	0.543 12	0.000 1	0.000 1	0.062 15	0.004 13	0.862 15	0.000 9	0.000 11	0.000 1	0.000 9	0.000 1	0.123 5	0.316 16	0.225 15	0.250 13	0.094 3	0.180 5	0.332 14	0.441 11	0.000 12	0.000 1	0.310 16	0.000 2	0.000 2	0.000 11	0.592 14	0.000 3	0.000 1	0.000 1	0.203 3	0.000 7	0.000 3	0.000 11	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	head ap	common ap	tail ap	chair	table	door	couch	cabinet	shelf	desk	office chair	bed	pillow	sink	picture	window	toilet	bookshelf	monitor	curtain	book	coffee table	box	refrigerator	lamp	kitchen cabinet	towel	clothes	tv	nightstand	counter	dresser	stool	cushion	plant	ceiling	bathtub	end table	dining table	keyboard	bag	backpack	toilet paper	printer	tv stand	whiteboard	blanket	shower curtain	trash can	closet	stairs	microwave	stove	shoe	computer tower	bottle	bin	bench	board	washing machine	mirror	copier	basket	sofa chair	file cabinet	fan	laptop	shower	paper	person	paper towel dispenser	oven	blinds	rack	plate	blackboard	piano	suitcase	radiator	recycling bin	wardrobe	soap dispenser	telephone	bucket	clock	stand	light	laundry basket	pipe	clothes dryer	seat	speaker	column	bicycle	ladder	bathroom stall	shower wall	cup	jacket	storage bin	coffee maker	dishwasher	paper towel roll	machine	mat	windowsill	bar	toaster	bulletin board	ironing board	kitchen counter	doorframe	toilet paper dispenser	mini fridge	fire extinguisher	ball	hat	shower curtain rod	water cooler	paper cutter	tray	ledge	mouse	cart	storage container	scale	tissue box	light switch	power outlet	decoration	sign	projector	vacuum cleaner	candle	plunger	stuffed animal	headphones	dish rack	broom	range hood	water bottle	vent	shower floor	water pitcher	mailbox	bowl	paper bag	music stand	projector screen	laundry detergent	object	bathroom vanity	laundry hamper	bathroom stall door	ceiling light	trash bin	dumbbell	stair rail	tube	bathroom cabinet	closet rod	coffee kettle	structure	shower head	keyboard piano	case of water bottles	coat rack	storage organizer	folded chair	fire alarm	power strip	calendar	poster

Mask3D Scannet200	0.278 1	0.383 1	0.263 2	0.168 1	0.661 2	0.465 1	0.572 1	0.665 3	0.391 2	0.121 5	0.304 1	0.015 2	0.647 1	0.349 1	0.474 1	0.489 1	0.321 1	0.816 6	0.351 3	0.722 1	0.402 4	0.195 1	0.515 4	0.082 2	0.795 1	0.215 2	0.396 1	0.377 2	0.082 5	0.724 1	0.586 1	0.015 3	0.277 1	0.377 6	0.201 1	0.475 3	0.572 1	0.778 3	0.089 2	0.759 1	0.556 2	0.068 1	0.506 1	0.467 1	0.323 4	0.778 2	0.427 2	0.027 3	0.789 1	0.744 1	0.003 2	0.570 2	0.561 1	0.337 2	0.265 1	0.711 1	0.258 2	0.031 1	0.569 1	0.311 1	0.441 2	0.179 1	1.000 1	0.000 2	0.233 2	0.411 2	0.283 2	0.380 1	0.667 1	0.016 1	0.048 4	0.418 3	0.139 2	0.173 1	0.000 1	0.086 2	0.014 3	0.500 1	0.384 1	0.497 1	0.044 4	0.032 2	0.752 1	0.287 2	0.003 1	0.000 2	0.007 1	0.208 1	0.000 1	0.001 3	0.349 2	0.008 2	0.014 2	0.509 1	0.500 2	0.323 1	0.023 3	0.176 2	0.107 2	0.105 4	0.000 2	0.605 1	0.378 1	0.016 2	0.000 1	0.400 1	0.192 1	0.000 1	0.048 3	0.037 3	0.000 2	0.275 1	0.119 1	0.810 1	0.258 2	0.006 4	0.083 6	0.000 1	0.568 2	0.377 2	0.708 1	0.000 1	0.005 2	0.147 2	0.014 3	0.000 2	0.556 2	0.085 1	0.325 1	0.500 1	0.083 2	0.004 2	0.000 1	0.590 1	0.000 1	0.365 1	0.000 1	0.116 1	0.491 1	0.000 1	0.626 1	0.000 1	0.000 1	0.579 1	0.391 1	0.050 5	0.000 1	0.028 2	0.000 1	0.222 2	0.000 1	0.063 1	0.302 1	0.356 2	0.149 5	0.573 1	0.415 1	0.013 6	0.002 5	0.004 1	0.000 1	0.005 5	0.000 1	0.000 1	0.444 1	0.514 1	0.000 2	0.028 1	0.000 2	0.156 2	0.267 1	0.000 2	1.000 1	0.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
TD3D Scannet200	0.211 3	0.332 3	0.177 3	0.103 3	0.662 1	0.413 2	0.463 3	0.705 1	0.192 4	0.145 2	0.266 2	0.215 1	0.452 5	0.209 3	0.222 6	0.219 6	0.315 2	0.893 1	0.380 2	0.617 2	0.439 2	0.047 5	0.646 1	0.080 3	0.610 3	0.253 1	0.237 3	0.293 3	0.135 2	0.379 6	0.494 2	0.048 1	0.252 2	0.451 3	0.184 2	0.483 2	0.395 2	0.852 1	0.083 3	0.551 2	0.278 3	0.036 3	0.337 3	0.266 3	0.544 2	0.963 1	0.079 6	0.039 1	0.740 3	0.604 3	0.000 3	0.586 1	0.283 3	0.282 3	0.059 3	0.633 3	0.028 3	0.004 2	0.559 2	0.309 2	0.420 3	0.028 6	1.000 1	0.000 2	0.456 1	0.411 1	0.372 1	0.060 5	0.046 4	0.000 2	0.040 5	0.694 1	0.083 3	0.000 2	0.000 1	0.000 3	0.000 4	0.083 5	0.252 3	0.260 5	0.200 2	0.160 1	0.669 2	0.111 3	0.000 2	0.000 2	0.006 2	0.169 2	0.000 1	0.007 2	0.296 3	0.032 1	0.074 1	0.139 4	0.000 3	0.321 2	0.031 2	0.108 3	0.088 3	0.157 2	0.000 2	0.231 5	0.026 6	0.000 3	0.000 1	0.356 2	0.052 3	0.000 1	0.240 2	0.147 2	0.000 2	0.015 3	0.046 4	0.144 4	0.073 4	0.414 2	0.222 5	0.000 1	0.806 1	0.343 3	0.486 3	0.000 1	0.008 1	0.038 3	0.083 2	0.002 1	0.028 3	0.074 2	0.032 3	0.150 3	0.039 3	0.008 1	0.000 1	0.250 5	0.000 1	0.125 4	0.000 1	0.052 2	0.260 4	0.000 1	0.143 6	0.000 1	0.000 1	0.543 2	0.207 3	0.404 1	0.000 1	0.003 3	0.000 1	0.000 3	0.000 1	0.037 2	0.093 5	0.272 3	0.342 2	0.039 5	0.281 2	0.249 3	0.224 1	0.000 2	0.000 1	0.074 2	0.000 1	0.000 1	0.000 2	0.278 3	0.000 2	0.000 2	0.889 1	0.323 1	0.000 2	0.014 1	0.000 3	0.000 1
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
ODIN - Ins200	0.265 2	0.349 2	0.268 1	0.163 2	0.485 6	0.366 4	0.549 2	0.492 6	0.421 1	0.229 1	0.265 3	0.003 3	0.609 2	0.297 2	0.320 2	0.327 2	0.251 3	0.848 4	0.314 5	0.526 3	0.324 5	0.138 2	0.529 2	0.178 1	0.440 5	0.186 6	0.306 2	0.546 1	0.160 1	0.494 4	0.476 3	0.016 2	0.231 3	0.594 1	0.000 3	0.615 1	0.357 3	0.630 4	0.141 1	0.167 3	0.665 1	0.054 2	0.360 2	0.451 2	0.610 1	0.769 4	0.640 1	0.032 2	0.746 2	0.698 2	0.040 1	0.389 4	0.550 2	0.371 1	0.257 2	0.617 4	0.310 1	0.000 3	0.481 3	0.022 5	0.463 1	0.160 2	1.000 1	0.125 1	0.193 3	0.267 3	0.253 3	0.156 3	0.000 5	0.000 2	0.332 1	0.606 2	0.444 1	0.000 2	0.000 1	0.281 1	1.000 1	0.417 3	0.344 2	0.238 6	0.218 1	0.000 3	0.655 3	0.506 1	0.000 2	0.052 1	0.000 3	0.091 3	0.000 1	0.035 1	0.370 1	0.000 3	0.000 3	0.250 2	0.903 1	0.037 6	0.031 1	0.221 1	0.197 1	0.285 1	0.037 1	0.191 6	0.200 3	0.083 1	0.000 1	0.200 3	0.115 2	0.000 1	0.250 1	0.552 1	0.278 1	0.077 2	0.107 2	0.389 2	0.674 1	0.565 1	0.278 1	0.000 1	0.361 6	0.333 4	0.361 4	0.000 1	0.000 3	0.438 1	0.451 1	0.000 2	1.000 1	0.074 2	0.204 2	0.250 2	0.250 1	0.000 3	0.000 1	0.493 2	0.000 1	0.083 5	0.000 1	0.000 3	0.317 2	0.000 1	0.481 2	0.000 1	0.000 1	0.188 3	0.333 2	0.345 2	0.000 1	0.333 1	0.000 1	0.333 1	0.000 1	0.035 3	0.266 2	0.478 1	0.506 1	0.054 3	0.205 3	0.119 5	0.067 2	0.000 2	0.000 1	0.210 1	0.000 1	0.000 1	0.000 2	0.389 2	0.097 1	0.000 2	0.000 2	0.111 3	0.000 2	0.000 2	0.889 2	0.000 1

LGround Inst.	0.154 4	0.275 4	0.108 4	0.060 4	0.573 3	0.381 3	0.434 4	0.654 4	0.190 5	0.141 3	0.097 4	0.000 4	0.503 4	0.180 4	0.252 4	0.242 5	0.242 4	0.881 3	0.448 1	0.494 4	0.429 3	0.078 3	0.364 6	0.024 4	0.654 2	0.213 4	0.222 4	0.239 4	0.099 4	0.616 2	0.363 4	0.000 4	0.092 4	0.444 4	0.000 3	0.383 5	0.209 6	0.815 2	0.030 4	0.000 4	0.166 4	0.002 5	0.295 6	0.099 5	0.364 3	0.778 2	0.177 4	0.001 5	0.427 6	0.585 5	0.000 3	0.470 3	0.268 6	0.205 4	0.045 4	0.642 2	0.007 4	0.000 3	0.333 6	0.148 3	0.407 4	0.130 3	1.000 1	0.000 2	0.156 5	0.189 4	0.097 5	0.169 2	0.000 5	0.000 2	0.056 3	0.400 4	0.000 4	0.000 2	0.000 1	0.000 3	0.556 2	0.278 4	0.203 4	0.323 4	0.019 5	0.000 3	0.402 5	0.026 4	0.000 2	0.000 2	0.000 3	0.044 4	0.000 1	0.000 4	0.037 5	0.000 3	0.000 3	0.181 3	0.000 3	0.127 3	0.006 5	0.028 5	0.023 4	0.115 3	0.000 2	0.327 2	0.267 2	0.000 3	0.000 1	0.000 5	0.028 4	0.000 1	0.000 4	0.000 4	0.000 2	0.003 4	0.048 3	0.135 5	0.222 3	0.089 3	0.278 1	0.000 1	0.514 3	0.333 4	0.611 2	0.000 1	0.000 3	0.000 4	0.000 4	0.000 2	0.000 4	0.037 4	0.000 4	0.000 4	0.000 4	0.000 3	0.000 1	0.322 3	0.000 1	0.209 2	0.000 1	0.000 3	0.278 3	0.000 1	0.302 4	0.000 1	0.000 1	0.143 4	0.148 4	0.000 6	0.000 1	0.000 4	0.000 1	0.000 3	0.000 1	0.015 4	0.064 6	0.000 4	0.272 3	0.031 6	0.000 5	0.257 2	0.028 3	0.000 2	0.000 1	0.041 3	0.000 1	0.000 1	0.000 2	0.222 6	0.000 2	0.000 2	0.000 2	0.000 6	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.
Minkowski 34D Inst.	0.130 5	0.246 5	0.083 5	0.043 6	0.547 5	0.236 5	0.415 5	0.672 2	0.141 6	0.133 4	0.067 5	0.000 4	0.521 3	0.114 6	0.238 5	0.289 3	0.232 5	0.883 2	0.182 6	0.373 6	0.486 1	0.076 4	0.488 5	0.022 5	0.529 4	0.199 5	0.110 5	0.217 5	0.100 3	0.460 5	0.319 5	0.000 4	0.025 6	0.472 2	0.000 3	0.394 4	0.210 5	0.537 5	0.004 5	0.000 4	0.083 6	0.000 6	0.299 5	0.061 6	0.201 6	0.761 5	0.084 5	0.008 4	0.720 4	0.557 6	0.000 3	0.317 6	0.280 4	0.094 6	0.020 6	0.564 6	0.000 5	0.000 3	0.400 4	0.048 4	0.259 5	0.101 4	1.000 1	0.000 2	0.190 4	0.142 6	0.094 6	0.137 4	0.089 3	0.000 2	0.101 2	0.355 6	0.000 4	0.000 2	0.000 1	0.000 3	0.000 4	0.444 2	0.082 6	0.384 2	0.000 6	0.000 3	0.334 6	0.004 6	0.000 2	0.000 2	0.000 3	0.041 5	0.000 1	0.000 4	0.026 6	0.000 3	0.000 3	0.000 5	0.000 3	0.082 5	0.022 4	0.000 6	0.021 5	0.088 5	0.000 2	0.241 4	0.033 5	0.000 3	0.000 1	0.067 4	0.000 6	0.000 1	0.000 4	0.000 4	0.000 2	0.000 5	0.026 5	0.262 3	0.016 5	0.000 5	0.278 1	0.000 1	0.500 4	0.394 1	0.028 6	0.000 1	0.000 3	0.000 4	0.000 4	0.000 2	0.000 4	0.019 5	0.000 4	0.000 4	0.000 4	0.000 3	0.000 1	0.156 6	0.000 1	0.032 6	0.000 1	0.000 3	0.194 6	0.000 1	0.248 5	0.000 1	0.000 1	0.099 5	0.019 5	0.308 3	0.000 1	0.000 4	0.000 1	0.000 3	0.000 1	0.007 5	0.122 3	0.000 4	0.175 4	0.063 2	0.000 5	0.271 1	0.000 6	0.000 2	0.000 1	0.000 6	0.000 1	0.000 1	0.000 2	0.278 3	0.000 2	0.000 2	0.000 2	0.111 3	0.000 2	0.000 2	0.000 3	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.	0.123 6	0.223 6	0.082 6	0.046 5	0.564 4	0.152 6	0.394 6	0.578 5	0.235 3	0.116 6	0.034 6	0.000 4	0.348 6	0.119 5	0.297 3	0.285 4	0.202 6	0.838 5	0.323 4	0.407 5	0.184 6	0.037 6	0.516 3	0.013 6	0.424 6	0.214 3	0.093 6	0.105 6	0.078 6	0.542 3	0.250 6	0.000 4	0.064 5	0.444 4	0.000 3	0.224 6	0.231 4	0.537 5	0.001 6	0.000 4	0.126 5	0.004 4	0.308 4	0.193 4	0.244 5	0.343 6	0.228 3	0.000 6	0.441 5	0.588 4	0.000 3	0.338 5	0.275 5	0.189 5	0.030 5	0.600 5	0.000 5	0.000 3	0.378 5	0.000 6	0.108 6	0.098 5	1.000 1	0.000 2	0.096 6	0.172 5	0.144 4	0.011 6	0.125 2	0.000 2	0.000 6	0.376 5	0.000 4	0.000 2	0.000 1	0.000 3	0.000 4	0.042 6	0.141 5	0.377 3	0.051 3	0.000 3	0.483 4	0.017 5	0.000 2	0.000 2	0.000 3	0.022 6	0.000 1	0.000 4	0.065 4	0.000 3	0.000 3	0.000 5	0.000 3	0.094 4	0.000 6	0.042 4	0.000 6	0.064 6	0.000 2	0.259 3	0.089 4	0.000 3	0.000 1	0.000 5	0.022 5	0.000 1	0.000 4	0.000 4	0.000 2	0.000 5	0.018 6	0.111 6	0.000 6	0.000 5	0.278 1	0.000 1	0.444 5	0.333 4	0.333 5	0.000 1	0.000 3	0.000 4	0.000 4	0.000 2	0.000 4	0.000 6	0.000 4	0.000 4	0.000 4	0.000 3	0.000 1	0.267 4	0.000 1	0.184 3	0.000 1	0.000 3	0.211 5	0.000 1	0.378 3	0.000 1	0.000 1	0.063 6	0.000 6	0.275 4	0.000 1	0.000 4	0.000 1	0.000 3	0.000 1	0.007 6	0.105 4	0.000 4	0.032 6	0.045 4	0.198 4	0.171 4	0.028 3	0.000 2	0.000 1	0.006 4	0.000 1	0.000 1	0.000 2	0.278 3	0.000 2	0.000 2	0.000 2	0.044 5	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

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

TTT-KD	0.773 7	0.646 97	0.818 16	0.809 41	0.774 10	0.878 3	0.581 3	0.943 1	0.687 15	0.704 7	0.978 6	0.607 6	0.336 19	0.775 11	0.912 8	0.838 4	0.823 4	0.694 15	0.967 4	0.899 4	0.794 6
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
DITR ScanNet	0.797 2	0.727 76	0.869 1	0.882 1	0.785 6	0.868 7	0.578 5	0.943 1	0.744 1	0.727 3	0.979 1	0.627 2	0.364 9	0.824 1	0.949 2	0.779 15	0.844 1	0.757 1	0.982 1	0.905 2	0.802 3
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation.
PTv3-PPT-ALC	0.798 1	0.911 11	0.812 22	0.854 8	0.770 12	0.856 15	0.555 17	0.943 1	0.660 26	0.735 2	0.979 1	0.606 7	0.492 1	0.792 4	0.934 4	0.841 2	0.819 6	0.716 9	0.947 10	0.906 1	0.822 1
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. arxiv
DiffSegNet	0.758 14	0.725 78	0.789 41	0.843 20	0.762 17	0.856 15	0.562 14	0.920 4	0.657 29	0.658 21	0.958 23	0.589 14	0.337 18	0.782 6	0.879 24	0.787 11	0.779 41	0.678 22	0.926 29	0.880 13	0.799 5

ODIN	0.744 29	0.658 93	0.752 64	0.870 3	0.714 40	0.843 33	0.569 11	0.919 5	0.703 8	0.622 40	0.949 59	0.591 12	0.343 15	0.736 34	0.784 56	0.816 7	0.838 2	0.672 31	0.918 37	0.854 39	0.725 28
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024
PTv3 ScanNet	0.794 3	0.941 3	0.813 21	0.851 11	0.782 7	0.890 2	0.597 1	0.916 6	0.696 11	0.713 5	0.979 1	0.635 1	0.384 3	0.793 3	0.907 10	0.821 5	0.790 36	0.696 14	0.967 4	0.903 3	0.805 2
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
Retro-FPN	0.744 29	0.842 30	0.800 30	0.767 61	0.740 32	0.836 41	0.541 23	0.914 7	0.672 22	0.626 37	0.958 23	0.552 33	0.272 53	0.777 9	0.886 22	0.696 52	0.801 24	0.674 29	0.941 14	0.858 33	0.717 33
Peng Xiang, Xin Wen, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
MSP	0.748 24	0.623 100	0.804 28	0.859 5	0.745 31	0.824 54	0.501 42	0.912 8	0.690 13	0.685 10	0.956 30	0.567 25	0.320 27	0.768 17	0.918 7	0.720 39	0.802 20	0.676 26	0.921 33	0.881 12	0.779 9

PonderV2	0.785 4	0.978 1	0.800 30	0.833 29	0.788 4	0.853 20	0.545 21	0.910 9	0.713 3	0.705 6	0.979 1	0.596 9	0.390 2	0.769 15	0.832 45	0.821 5	0.792 35	0.730 2	0.975 2	0.897 6	0.785 7
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Swin3D	0.779 6	0.861 23	0.818 16	0.836 26	0.790 3	0.875 4	0.576 7	0.905 10	0.704 7	0.739 1	0.969 12	0.611 3	0.349 12	0.756 25	0.958 1	0.702 51	0.805 19	0.708 10	0.916 39	0.898 5	0.801 4

PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 36	0.829 31	0.751 26	0.854 18	0.540 25	0.903 11	0.630 39	0.672 17	0.963 16	0.565 26	0.357 10	0.788 5	0.900 14	0.737 31	0.802 20	0.685 20	0.950 8	0.887 8	0.780 8
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. CVPR 2024
ResLFE_HDS	0.772 8	0.939 4	0.824 7	0.854 8	0.771 11	0.840 35	0.564 13	0.900 12	0.686 16	0.677 14	0.961 18	0.537 36	0.348 13	0.769 15	0.903 12	0.785 13	0.815 9	0.676 26	0.939 16	0.880 13	0.772 11

Virtual MVFusion	0.746 26	0.771 55	0.819 14	0.848 15	0.702 43	0.865 10	0.397 90	0.899 13	0.699 9	0.664 20	0.948 62	0.588 15	0.330 23	0.746 32	0.851 39	0.764 21	0.796 29	0.704 12	0.935 21	0.866 28	0.728 24
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
EQ-Net	0.743 31	0.620 101	0.799 33	0.849 13	0.730 35	0.822 56	0.493 50	0.897 14	0.664 23	0.681 12	0.955 34	0.562 29	0.378 4	0.760 21	0.903 12	0.738 30	0.801 24	0.673 30	0.907 43	0.877 16	0.745 17
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
OA-CNN-L_ScanNet20	0.756 16	0.783 47	0.826 6	0.858 6	0.776 9	0.837 39	0.548 20	0.896 15	0.649 31	0.675 15	0.962 17	0.586 17	0.335 21	0.771 14	0.802 54	0.770 19	0.787 38	0.691 17	0.936 20	0.880 13	0.761 13

DiffSeg3D2	0.745 28	0.725 78	0.814 20	0.837 25	0.751 26	0.831 46	0.514 36	0.896 15	0.674 20	0.684 11	0.960 19	0.564 27	0.303 34	0.773 12	0.820 48	0.713 45	0.798 27	0.690 19	0.923 31	0.875 20	0.757 14

PointTransformerV2	0.752 20	0.742 68	0.809 25	0.872 2	0.758 19	0.860 12	0.552 18	0.891 17	0.610 46	0.687 8	0.960 19	0.559 30	0.304 33	0.766 18	0.926 6	0.767 20	0.797 28	0.644 38	0.942 13	0.876 19	0.722 31
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer	0.749 22	0.793 43	0.790 39	0.807 43	0.750 28	0.856 15	0.524 31	0.881 18	0.588 58	0.642 30	0.977 10	0.591 12	0.274 51	0.781 7	0.929 5	0.804 8	0.796 29	0.642 39	0.947 10	0.885 10	0.715 36
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
OctFormer	0.766 9	0.925 7	0.808 26	0.849 13	0.786 5	0.846 30	0.566 12	0.876 19	0.690 13	0.674 16	0.960 19	0.576 22	0.226 72	0.753 27	0.904 11	0.777 16	0.815 9	0.722 7	0.923 31	0.877 16	0.776 10
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
VMNet	0.746 26	0.870 21	0.838 3	0.858 6	0.729 36	0.850 24	0.501 42	0.874 20	0.587 59	0.658 21	0.956 30	0.564 27	0.299 35	0.765 19	0.900 14	0.716 42	0.812 15	0.631 44	0.939 16	0.858 33	0.709 37
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)
online3d	0.727 38	0.715 83	0.777 48	0.854 8	0.748 29	0.858 13	0.497 47	0.872 21	0.572 65	0.639 32	0.957 28	0.523 43	0.297 37	0.750 30	0.803 53	0.744 28	0.810 16	0.587 66	0.938 18	0.871 25	0.719 32

DTC	0.757 15	0.843 29	0.820 12	0.847 16	0.791 2	0.862 11	0.511 38	0.870 22	0.707 6	0.652 23	0.954 40	0.604 8	0.279 48	0.760 21	0.942 3	0.734 32	0.766 50	0.701 13	0.884 61	0.874 22	0.736 20

DMF-Net	0.752 20	0.906 14	0.793 38	0.802 47	0.689 45	0.825 52	0.556 16	0.867 23	0.681 18	0.602 50	0.960 19	0.555 32	0.365 8	0.779 8	0.859 30	0.747 27	0.795 32	0.717 8	0.917 38	0.856 35	0.764 12
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
RPN	0.736 35	0.776 51	0.790 39	0.851 11	0.754 23	0.854 18	0.491 52	0.866 24	0.596 56	0.686 9	0.955 34	0.536 37	0.342 16	0.624 55	0.869 26	0.787 11	0.802 20	0.628 45	0.927 27	0.875 20	0.704 39

PointMetaBase	0.714 43	0.835 31	0.785 43	0.821 32	0.684 47	0.846 30	0.531 29	0.865 25	0.614 43	0.596 54	0.953 44	0.500 50	0.246 67	0.674 40	0.888 20	0.692 53	0.764 52	0.624 47	0.849 87	0.844 48	0.675 47

ConDaFormer	0.755 17	0.927 6	0.822 10	0.836 26	0.801 1	0.849 25	0.516 35	0.864 26	0.651 30	0.680 13	0.958 23	0.584 19	0.282 45	0.759 23	0.855 35	0.728 34	0.802 20	0.678 22	0.880 66	0.873 23	0.756 16
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
MatchingNet	0.724 41	0.812 40	0.812 22	0.810 40	0.735 34	0.834 43	0.495 49	0.860 27	0.572 65	0.602 50	0.954 40	0.512 47	0.280 47	0.757 24	0.845 41	0.725 36	0.780 40	0.606 55	0.937 19	0.851 42	0.700 41

MinkowskiNet	0.736 35	0.859 25	0.818 16	0.832 30	0.709 41	0.840 35	0.521 33	0.853 28	0.660 26	0.643 27	0.951 51	0.544 34	0.286 43	0.731 35	0.893 18	0.675 60	0.772 45	0.683 21	0.874 72	0.852 41	0.727 26
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
OccuSeg+Semantic	0.764 11	0.758 61	0.796 34	0.839 24	0.746 30	0.907 1	0.562 14	0.850 29	0.680 19	0.672 17	0.978 6	0.610 4	0.335 21	0.777 9	0.819 49	0.847 1	0.830 3	0.691 17	0.972 3	0.885 10	0.727 26

CU-Hybrid Net	0.764 11	0.924 8	0.819 14	0.840 23	0.757 21	0.853 20	0.580 4	0.848 30	0.709 5	0.643 27	0.958 23	0.587 16	0.295 38	0.753 27	0.884 23	0.758 23	0.815 9	0.725 5	0.927 27	0.867 27	0.743 19

PicassoNet-II	0.692 50	0.732 72	0.772 50	0.786 53	0.677 49	0.866 9	0.517 34	0.848 30	0.509 85	0.626 37	0.952 49	0.536 37	0.225 74	0.545 80	0.704 71	0.689 57	0.810 16	0.564 75	0.903 47	0.854 39	0.729 23
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
O-CNN	0.762 13	0.924 8	0.823 8	0.844 19	0.770 12	0.852 22	0.577 6	0.847 32	0.711 4	0.640 31	0.958 23	0.592 11	0.217 78	0.762 20	0.888 20	0.758 23	0.813 13	0.726 4	0.932 25	0.868 26	0.744 18
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
LSK3DNet	0.755 17	0.899 16	0.823 8	0.843 20	0.764 16	0.838 38	0.584 2	0.845 33	0.717 2	0.638 33	0.956 30	0.580 21	0.229 71	0.640 48	0.900 14	0.750 26	0.813 13	0.729 3	0.920 35	0.872 24	0.757 14
Tuo Feng, Wenguan Wang, Fan Ma, Yi Yang: LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels. CVPR 2024
BPNet	0.749 22	0.909 12	0.818 16	0.811 39	0.752 24	0.839 37	0.485 53	0.842 34	0.673 21	0.644 26	0.957 28	0.528 42	0.305 32	0.773 12	0.859 30	0.788 10	0.818 8	0.693 16	0.916 39	0.856 35	0.723 30
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
INS-Conv-semantic	0.717 42	0.751 64	0.759 58	0.812 38	0.704 42	0.868 7	0.537 26	0.842 34	0.609 48	0.608 46	0.953 44	0.534 39	0.293 39	0.616 58	0.864 28	0.719 41	0.793 33	0.640 40	0.933 23	0.845 47	0.663 50

LRPNet	0.742 32	0.816 38	0.806 27	0.807 43	0.752 24	0.828 50	0.575 8	0.839 36	0.699 9	0.637 34	0.954 40	0.520 45	0.320 27	0.755 26	0.834 43	0.760 22	0.772 45	0.676 26	0.915 41	0.862 30	0.717 33

PNE	0.755 17	0.786 45	0.835 5	0.834 28	0.758 19	0.849 25	0.570 10	0.836 37	0.648 32	0.668 19	0.978 6	0.581 20	0.367 7	0.683 39	0.856 33	0.804 8	0.801 24	0.678 22	0.961 6	0.889 7	0.716 35
P. Hermosilla: Point Neighborhood Embeddings.
Mix3D	0.781 5	0.964 2	0.855 2	0.843 20	0.781 8	0.858 13	0.575 8	0.831 38	0.685 17	0.714 4	0.979 1	0.594 10	0.310 30	0.801 2	0.892 19	0.841 2	0.819 6	0.723 6	0.940 15	0.887 8	0.725 28
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
SAT	0.742 32	0.860 24	0.765 55	0.819 34	0.769 14	0.848 27	0.533 27	0.829 39	0.663 24	0.631 36	0.955 34	0.586 17	0.274 51	0.753 27	0.896 17	0.729 33	0.760 56	0.666 33	0.921 33	0.855 37	0.733 22

LargeKernel3D	0.739 34	0.909 12	0.820 12	0.806 45	0.740 32	0.852 22	0.545 21	0.826 40	0.594 57	0.643 27	0.955 34	0.541 35	0.263 61	0.723 37	0.858 32	0.775 18	0.767 49	0.678 22	0.933 23	0.848 43	0.694 42
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
StratifiedFormer	0.747 25	0.901 15	0.803 29	0.845 18	0.757 21	0.846 30	0.512 37	0.825 41	0.696 11	0.645 25	0.956 30	0.576 22	0.262 62	0.744 33	0.861 29	0.742 29	0.770 48	0.705 11	0.899 51	0.860 32	0.734 21
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
IPCA	0.731 37	0.890 17	0.837 4	0.864 4	0.726 37	0.873 5	0.530 30	0.824 42	0.489 92	0.647 24	0.978 6	0.609 5	0.336 19	0.624 55	0.733 64	0.758 23	0.776 43	0.570 70	0.949 9	0.877 16	0.728 24

DGNet	0.684 54	0.712 84	0.784 44	0.782 57	0.658 52	0.835 42	0.499 46	0.823 43	0.641 34	0.597 53	0.950 55	0.487 55	0.281 46	0.575 68	0.619 85	0.647 73	0.764 52	0.620 50	0.871 78	0.846 46	0.688 44

VACNN++	0.684 54	0.728 75	0.757 61	0.776 58	0.690 44	0.804 74	0.464 62	0.816 44	0.577 64	0.587 57	0.945 70	0.508 49	0.276 50	0.671 41	0.710 69	0.663 65	0.750 64	0.589 64	0.881 64	0.832 51	0.653 53

One Thing One Click	0.701 47	0.825 35	0.796 34	0.723 68	0.716 39	0.832 45	0.433 80	0.816 44	0.634 37	0.609 45	0.969 12	0.418 88	0.344 14	0.559 74	0.833 44	0.715 43	0.808 18	0.560 76	0.902 48	0.847 44	0.680 46

RFCR	0.702 46	0.889 18	0.745 69	0.813 37	0.672 50	0.818 63	0.493 50	0.815 46	0.623 40	0.610 44	0.947 64	0.470 62	0.249 66	0.594 62	0.848 40	0.705 48	0.779 41	0.646 37	0.892 56	0.823 55	0.611 65
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
PointTransformer++	0.725 39	0.727 76	0.811 24	0.819 34	0.765 15	0.841 34	0.502 41	0.814 47	0.621 42	0.623 39	0.955 34	0.556 31	0.284 44	0.620 57	0.866 27	0.781 14	0.757 60	0.648 36	0.932 25	0.862 30	0.709 37

contrastBoundary	0.705 44	0.769 58	0.775 49	0.809 41	0.687 46	0.820 59	0.439 78	0.812 48	0.661 25	0.591 56	0.945 70	0.515 46	0.171 97	0.633 52	0.856 33	0.720 39	0.796 29	0.668 32	0.889 58	0.847 44	0.689 43
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
joint point-based	0.634 78	0.614 102	0.778 47	0.667 88	0.633 65	0.825 52	0.420 83	0.804 49	0.467 97	0.561 60	0.951 51	0.494 51	0.291 40	0.566 71	0.458 99	0.579 96	0.764 52	0.559 78	0.838 89	0.814 61	0.598 74
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
Superpoint Network	0.683 57	0.851 27	0.728 77	0.800 49	0.653 55	0.806 72	0.468 59	0.804 49	0.572 65	0.602 50	0.946 67	0.453 72	0.239 70	0.519 85	0.822 46	0.689 57	0.762 55	0.595 61	0.895 54	0.827 53	0.630 62

Feature-Geometry Net	0.685 53	0.866 22	0.748 66	0.819 34	0.645 60	0.794 79	0.450 68	0.802 51	0.587 59	0.604 48	0.945 70	0.464 64	0.201 87	0.554 76	0.840 42	0.723 38	0.732 71	0.602 57	0.907 43	0.822 57	0.603 72

ClickSeg_Semantic	0.703 45	0.774 53	0.800 30	0.793 52	0.760 18	0.847 29	0.471 57	0.802 51	0.463 99	0.634 35	0.968 14	0.491 53	0.271 55	0.726 36	0.910 9	0.706 47	0.815 9	0.551 82	0.878 67	0.833 49	0.570 82

Feature_GeometricNet	0.690 51	0.884 19	0.754 62	0.795 50	0.647 58	0.818 63	0.422 82	0.802 51	0.612 45	0.604 48	0.945 70	0.462 65	0.189 92	0.563 73	0.853 37	0.726 35	0.765 51	0.632 43	0.904 45	0.821 58	0.606 69
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
JSENet	0.699 48	0.881 20	0.762 56	0.821 32	0.667 51	0.800 76	0.522 32	0.792 54	0.613 44	0.607 47	0.935 90	0.492 52	0.205 84	0.576 67	0.853 37	0.691 54	0.758 58	0.652 35	0.872 75	0.828 52	0.649 54
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
PointConv	0.666 63	0.781 48	0.759 58	0.699 76	0.644 61	0.822 56	0.475 55	0.779 55	0.564 71	0.504 82	0.953 44	0.428 82	0.203 86	0.586 65	0.754 60	0.661 66	0.753 61	0.588 65	0.902 48	0.813 63	0.642 57
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointContrast_LA_SEM	0.683 57	0.757 62	0.784 44	0.786 53	0.639 62	0.824 54	0.408 85	0.775 56	0.604 51	0.541 65	0.934 94	0.532 40	0.269 57	0.552 77	0.777 57	0.645 76	0.793 33	0.640 40	0.913 42	0.824 54	0.671 48

KP-FCNN	0.684 54	0.847 28	0.758 60	0.784 55	0.647 58	0.814 66	0.473 56	0.772 57	0.605 50	0.594 55	0.935 90	0.450 73	0.181 95	0.587 63	0.805 52	0.690 55	0.785 39	0.614 51	0.882 63	0.819 59	0.632 61
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
PointASNL	0.666 63	0.703 86	0.781 46	0.751 67	0.655 54	0.830 47	0.471 57	0.769 58	0.474 95	0.537 67	0.951 51	0.475 60	0.279 48	0.635 50	0.698 74	0.675 60	0.751 62	0.553 81	0.816 94	0.806 65	0.703 40
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
Supervoxel-CNN	0.635 77	0.656 94	0.711 79	0.719 71	0.613 68	0.757 96	0.444 75	0.765 59	0.534 77	0.566 59	0.928 98	0.478 59	0.272 53	0.636 49	0.531 93	0.664 64	0.645 99	0.508 97	0.864 82	0.792 79	0.611 65

O3DSeg	0.668 62	0.822 36	0.771 52	0.496 111	0.651 57	0.833 44	0.541 23	0.761 60	0.555 74	0.611 43	0.966 15	0.489 54	0.370 6	0.388 104	0.580 88	0.776 17	0.751 62	0.570 70	0.956 7	0.817 60	0.646 56

VI-PointConv	0.676 59	0.770 57	0.754 62	0.783 56	0.621 66	0.814 66	0.552 18	0.758 61	0.571 68	0.557 61	0.954 40	0.529 41	0.268 59	0.530 83	0.682 75	0.675 60	0.719 74	0.603 56	0.888 59	0.833 49	0.665 49
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
HPGCNN	0.656 67	0.698 88	0.743 71	0.650 92	0.564 84	0.820 59	0.505 40	0.758 61	0.631 38	0.479 86	0.945 70	0.480 58	0.226 72	0.572 69	0.774 58	0.690 55	0.735 69	0.614 51	0.853 86	0.776 89	0.597 75
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 68	0.752 63	0.734 75	0.664 89	0.583 79	0.815 65	0.399 89	0.754 63	0.639 35	0.535 69	0.942 80	0.470 62	0.309 31	0.665 42	0.539 91	0.650 69	0.708 79	0.635 42	0.857 85	0.793 76	0.642 57
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
SparseConvNet	0.725 39	0.647 96	0.821 11	0.846 17	0.721 38	0.869 6	0.533 27	0.754 63	0.603 52	0.614 42	0.955 34	0.572 24	0.325 25	0.710 38	0.870 25	0.724 37	0.823 4	0.628 45	0.934 22	0.865 29	0.683 45

SALANet	0.670 61	0.816 38	0.770 53	0.768 60	0.652 56	0.807 71	0.451 65	0.747 65	0.659 28	0.545 64	0.924 100	0.473 61	0.149 107	0.571 70	0.811 51	0.635 80	0.746 65	0.623 48	0.892 56	0.794 74	0.570 82

ROSMRF3D	0.673 60	0.789 44	0.748 66	0.763 63	0.635 64	0.814 66	0.407 87	0.747 65	0.581 63	0.573 58	0.950 55	0.484 56	0.271 55	0.607 59	0.754 60	0.649 70	0.774 44	0.596 59	0.883 62	0.823 55	0.606 69

PointSPNet	0.637 75	0.734 71	0.692 91	0.714 73	0.576 81	0.797 78	0.446 70	0.743 67	0.598 55	0.437 97	0.942 80	0.403 91	0.150 106	0.626 54	0.800 55	0.649 70	0.697 83	0.557 79	0.846 88	0.777 88	0.563 86

FusionNet	0.688 52	0.704 85	0.741 73	0.754 65	0.656 53	0.829 48	0.501 42	0.741 68	0.609 48	0.548 63	0.950 55	0.522 44	0.371 5	0.633 52	0.756 59	0.715 43	0.771 47	0.623 48	0.861 83	0.814 61	0.658 51
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
RandLA-Net	0.645 69	0.778 49	0.731 76	0.699 76	0.577 80	0.829 48	0.446 70	0.736 69	0.477 94	0.523 75	0.945 70	0.454 69	0.269 57	0.484 94	0.749 63	0.618 83	0.738 67	0.599 58	0.827 91	0.792 79	0.621 64

FusionAwareConv	0.630 83	0.604 104	0.741 73	0.766 62	0.590 75	0.747 98	0.501 42	0.734 70	0.503 87	0.527 71	0.919 104	0.454 69	0.323 26	0.550 79	0.420 103	0.678 59	0.688 87	0.544 86	0.896 53	0.795 73	0.627 63
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
LAP-D	0.594 93	0.720 80	0.692 91	0.637 98	0.456 103	0.773 90	0.391 95	0.730 71	0.587 59	0.445 96	0.940 84	0.381 96	0.288 41	0.434 100	0.453 101	0.591 92	0.649 97	0.581 68	0.777 98	0.749 98	0.610 67

3DSM_DMMF	0.631 80	0.626 99	0.745 69	0.801 48	0.607 69	0.751 97	0.506 39	0.729 72	0.565 70	0.491 84	0.866 114	0.434 77	0.197 90	0.595 61	0.630 84	0.709 46	0.705 81	0.560 76	0.875 70	0.740 99	0.491 103

One-Thing-One-Click	0.693 49	0.743 67	0.794 36	0.655 91	0.684 47	0.822 56	0.497 47	0.719 73	0.622 41	0.617 41	0.977 10	0.447 75	0.339 17	0.750 30	0.664 81	0.703 50	0.790 36	0.596 59	0.946 12	0.855 37	0.647 55
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SConv	0.636 76	0.830 33	0.697 87	0.752 66	0.572 83	0.780 87	0.445 72	0.716 74	0.529 78	0.530 70	0.951 51	0.446 76	0.170 98	0.507 89	0.666 80	0.636 79	0.682 89	0.541 89	0.886 60	0.799 69	0.594 76

PointMRNet	0.640 72	0.717 82	0.701 84	0.692 79	0.576 81	0.801 75	0.467 61	0.716 74	0.563 72	0.459 92	0.953 44	0.429 81	0.169 99	0.581 66	0.854 36	0.605 86	0.710 76	0.550 83	0.894 55	0.793 76	0.575 80

SD-DETR	0.576 97	0.746 65	0.609 110	0.445 116	0.517 95	0.643 111	0.366 99	0.714 76	0.456 100	0.468 90	0.870 113	0.432 78	0.264 60	0.558 75	0.674 76	0.586 95	0.688 87	0.482 103	0.739 103	0.733 101	0.537 94

DPC	0.592 94	0.720 80	0.700 85	0.602 103	0.480 99	0.762 95	0.380 98	0.713 77	0.585 62	0.437 97	0.940 84	0.369 98	0.288 41	0.434 100	0.509 97	0.590 94	0.639 102	0.567 74	0.772 99	0.755 96	0.592 77
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
3DWSSS	0.425 115	0.525 110	0.647 102	0.522 108	0.324 115	0.488 120	0.077 121	0.712 78	0.353 113	0.401 101	0.636 121	0.281 110	0.176 96	0.340 107	0.565 90	0.175 120	0.551 110	0.398 115	0.370 121	0.602 116	0.361 114

PointNet2-SFPN	0.631 80	0.771 55	0.692 91	0.672 84	0.524 93	0.837 39	0.440 77	0.706 79	0.538 76	0.446 94	0.944 76	0.421 87	0.219 77	0.552 77	0.751 62	0.591 92	0.737 68	0.543 88	0.901 50	0.768 91	0.557 89

PanopticFusion-label	0.529 103	0.491 113	0.688 94	0.604 102	0.386 108	0.632 112	0.225 118	0.705 80	0.434 105	0.293 112	0.815 116	0.348 102	0.241 69	0.499 90	0.669 78	0.507 102	0.649 97	0.442 111	0.796 96	0.602 116	0.561 87
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
DCM-Net	0.658 66	0.778 49	0.702 83	0.806 45	0.619 67	0.813 69	0.468 59	0.693 81	0.494 88	0.524 73	0.941 82	0.449 74	0.298 36	0.510 87	0.821 47	0.675 60	0.727 73	0.568 73	0.826 92	0.803 67	0.637 59
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPEIN	0.618 89	0.729 74	0.668 97	0.647 94	0.597 73	0.766 92	0.414 84	0.680 82	0.520 81	0.525 72	0.946 67	0.432 78	0.215 79	0.493 92	0.599 87	0.638 78	0.617 104	0.570 70	0.897 52	0.806 65	0.605 71
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
MVPNet	0.641 70	0.831 32	0.715 78	0.671 86	0.590 75	0.781 85	0.394 91	0.679 83	0.642 33	0.553 62	0.937 87	0.462 65	0.256 63	0.649 45	0.406 104	0.626 81	0.691 86	0.666 33	0.877 68	0.792 79	0.608 68
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
TextureNet	0.566 99	0.672 92	0.664 99	0.671 86	0.494 97	0.719 101	0.445 72	0.678 84	0.411 108	0.396 102	0.935 90	0.356 100	0.225 74	0.412 102	0.535 92	0.565 98	0.636 103	0.464 105	0.794 97	0.680 109	0.568 84
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
dtc_net	0.625 86	0.703 86	0.751 65	0.794 51	0.535 91	0.848 27	0.480 54	0.676 85	0.528 79	0.469 89	0.944 76	0.454 69	0.004 119	0.464 96	0.636 83	0.704 49	0.758 58	0.548 85	0.924 30	0.787 83	0.492 102

PointConv-SFPN	0.641 70	0.776 51	0.703 82	0.721 70	0.557 87	0.826 51	0.451 65	0.672 86	0.563 72	0.483 85	0.943 79	0.425 85	0.162 102	0.644 47	0.726 65	0.659 67	0.709 78	0.572 69	0.875 70	0.786 84	0.559 88

Weakly-Openseg v3	0.625 86	0.924 8	0.787 42	0.620 99	0.555 89	0.811 70	0.393 92	0.666 87	0.382 110	0.520 76	0.953 44	0.250 114	0.208 81	0.604 60	0.670 77	0.644 77	0.742 66	0.538 91	0.919 36	0.803 67	0.513 100

APCF-Net	0.631 80	0.742 68	0.687 96	0.672 84	0.557 87	0.792 82	0.408 85	0.665 88	0.545 75	0.508 79	0.952 49	0.428 82	0.186 93	0.634 51	0.702 72	0.620 82	0.706 80	0.555 80	0.873 73	0.798 71	0.581 78
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
DVVNet	0.562 100	0.648 95	0.700 85	0.770 59	0.586 78	0.687 105	0.333 104	0.650 89	0.514 84	0.475 88	0.906 108	0.359 99	0.223 76	0.340 107	0.442 102	0.422 111	0.668 94	0.501 98	0.708 106	0.779 86	0.534 95

AttAN	0.609 91	0.760 60	0.667 98	0.649 93	0.521 94	0.793 80	0.457 64	0.648 90	0.528 79	0.434 99	0.947 64	0.401 92	0.153 105	0.454 97	0.721 68	0.648 72	0.717 75	0.536 92	0.904 45	0.765 92	0.485 104
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
SIConv	0.625 86	0.830 33	0.694 89	0.757 64	0.563 85	0.772 91	0.448 69	0.647 91	0.520 81	0.509 78	0.949 59	0.431 80	0.191 91	0.496 91	0.614 86	0.647 73	0.672 93	0.535 93	0.876 69	0.783 85	0.571 81

SPH3D-GCN	0.610 90	0.858 26	0.772 50	0.489 112	0.532 92	0.792 82	0.404 88	0.643 92	0.570 69	0.507 81	0.935 90	0.414 89	0.046 116	0.510 87	0.702 72	0.602 88	0.705 81	0.549 84	0.859 84	0.773 90	0.534 95
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
SegGroup_sem	0.627 85	0.818 37	0.747 68	0.701 75	0.602 71	0.764 93	0.385 97	0.629 93	0.490 90	0.508 79	0.931 97	0.409 90	0.201 87	0.564 72	0.725 66	0.618 83	0.692 85	0.539 90	0.873 73	0.794 74	0.548 92
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
Online SegFusion	0.515 105	0.607 103	0.644 104	0.579 105	0.434 105	0.630 113	0.353 101	0.628 94	0.440 103	0.410 100	0.762 119	0.307 106	0.167 100	0.520 84	0.403 105	0.516 101	0.565 107	0.447 109	0.678 109	0.701 106	0.514 99
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
CCRFNet	0.589 95	0.766 59	0.659 101	0.683 81	0.470 102	0.740 100	0.387 96	0.620 95	0.490 90	0.476 87	0.922 102	0.355 101	0.245 68	0.511 86	0.511 96	0.571 97	0.643 100	0.493 101	0.872 75	0.762 93	0.600 73

subcloud_weak	0.516 104	0.676 90	0.591 113	0.609 100	0.442 104	0.774 89	0.335 103	0.597 96	0.422 107	0.357 107	0.932 96	0.341 103	0.094 112	0.298 109	0.528 95	0.473 107	0.676 91	0.495 100	0.602 114	0.721 104	0.349 116

DenSeR	0.628 84	0.800 41	0.625 106	0.719 71	0.545 90	0.806 72	0.445 72	0.597 96	0.448 102	0.519 77	0.938 86	0.481 57	0.328 24	0.489 93	0.499 98	0.657 68	0.759 57	0.592 62	0.881 64	0.797 72	0.634 60

3DMV, FTSDF	0.501 106	0.558 108	0.608 111	0.424 118	0.478 100	0.690 104	0.246 114	0.586 98	0.468 96	0.450 93	0.911 106	0.394 93	0.160 103	0.438 98	0.212 114	0.432 110	0.541 112	0.475 104	0.742 102	0.727 102	0.477 106

wsss-transformer	0.600 92	0.634 98	0.743 71	0.697 78	0.601 72	0.781 85	0.437 79	0.585 99	0.493 89	0.446 94	0.933 95	0.394 93	0.011 118	0.654 44	0.661 82	0.603 87	0.733 70	0.526 94	0.832 90	0.761 94	0.480 105

3DMV	0.484 108	0.484 114	0.538 116	0.643 96	0.424 106	0.606 116	0.310 105	0.574 100	0.433 106	0.378 103	0.796 117	0.301 107	0.214 80	0.537 82	0.208 115	0.472 108	0.507 116	0.413 114	0.693 107	0.602 116	0.539 93
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PPCNN++	0.663 65	0.746 65	0.708 80	0.722 69	0.638 63	0.820 59	0.451 65	0.566 101	0.599 54	0.541 65	0.950 55	0.510 48	0.313 29	0.648 46	0.819 49	0.616 85	0.682 89	0.590 63	0.869 79	0.810 64	0.656 52
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
PointMTL	0.632 79	0.731 73	0.688 94	0.675 83	0.591 74	0.784 84	0.444 75	0.565 102	0.610 46	0.492 83	0.949 59	0.456 68	0.254 64	0.587 63	0.706 70	0.599 89	0.665 95	0.612 54	0.868 80	0.791 82	0.579 79

SQN_0.1%	0.569 98	0.676 90	0.696 88	0.657 90	0.497 96	0.779 88	0.424 81	0.548 103	0.515 83	0.376 104	0.902 111	0.422 86	0.357 10	0.379 105	0.456 100	0.596 91	0.659 96	0.544 86	0.685 108	0.665 112	0.556 90

Pointnet++ & Feature	0.557 101	0.735 70	0.661 100	0.686 80	0.491 98	0.744 99	0.392 93	0.539 104	0.451 101	0.375 105	0.946 67	0.376 97	0.205 84	0.403 103	0.356 107	0.553 99	0.643 100	0.497 99	0.824 93	0.756 95	0.515 98

PD-Net	0.638 74	0.797 42	0.769 54	0.641 97	0.590 75	0.820 59	0.461 63	0.537 105	0.637 36	0.536 68	0.947 64	0.388 95	0.206 83	0.656 43	0.668 79	0.647 73	0.732 71	0.585 67	0.868 80	0.793 76	0.473 108

FPConv	0.639 73	0.785 46	0.760 57	0.713 74	0.603 70	0.798 77	0.392 93	0.534 106	0.603 52	0.524 73	0.948 62	0.457 67	0.250 65	0.538 81	0.723 67	0.598 90	0.696 84	0.614 51	0.872 75	0.799 69	0.567 85
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
ROSMRF	0.580 96	0.772 54	0.707 81	0.681 82	0.563 85	0.764 93	0.362 100	0.515 107	0.465 98	0.465 91	0.936 89	0.427 84	0.207 82	0.438 98	0.577 89	0.536 100	0.675 92	0.486 102	0.723 105	0.779 86	0.524 97

GMLPs	0.538 102	0.495 112	0.693 90	0.647 94	0.471 101	0.793 80	0.300 107	0.477 108	0.505 86	0.358 106	0.903 110	0.327 104	0.081 113	0.472 95	0.529 94	0.448 109	0.710 76	0.509 95	0.746 101	0.737 100	0.554 91

PCNN	0.498 107	0.559 107	0.644 104	0.560 107	0.420 107	0.711 103	0.229 116	0.414 109	0.436 104	0.352 108	0.941 82	0.324 105	0.155 104	0.238 114	0.387 106	0.493 103	0.529 113	0.509 95	0.813 95	0.751 97	0.504 101

SPLAT Net	0.393 116	0.472 116	0.511 117	0.606 101	0.311 117	0.656 107	0.245 115	0.405 110	0.328 116	0.197 119	0.927 99	0.227 117	0.000 121	0.001 122	0.249 111	0.271 119	0.510 114	0.383 117	0.593 115	0.699 107	0.267 118
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
SurfaceConvPF	0.442 112	0.505 111	0.622 108	0.380 119	0.342 114	0.654 108	0.227 117	0.397 111	0.367 112	0.276 114	0.924 100	0.240 115	0.198 89	0.359 106	0.262 110	0.366 113	0.581 105	0.435 112	0.640 111	0.668 110	0.398 111
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
DGCNN_reproduce	0.446 111	0.474 115	0.623 107	0.463 114	0.366 111	0.651 109	0.310 105	0.389 112	0.349 114	0.330 109	0.937 87	0.271 111	0.126 109	0.285 110	0.224 113	0.350 116	0.577 106	0.445 110	0.625 112	0.723 103	0.394 112
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
PointCNN with RGB	0.458 109	0.577 106	0.611 109	0.356 120	0.321 116	0.715 102	0.299 109	0.376 113	0.328 116	0.319 110	0.944 76	0.285 109	0.164 101	0.216 117	0.229 112	0.484 105	0.545 111	0.456 107	0.755 100	0.709 105	0.475 107
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
GrowSP++	0.323 119	0.114 121	0.589 114	0.499 110	0.147 121	0.555 117	0.290 111	0.336 114	0.290 118	0.262 116	0.865 115	0.102 121	0.000 121	0.037 120	0.000 122	0.000 122	0.462 118	0.381 118	0.389 120	0.664 113	0.473 108

PNET2	0.442 112	0.548 109	0.548 115	0.597 104	0.363 112	0.628 114	0.300 107	0.292 115	0.374 111	0.307 111	0.881 112	0.268 112	0.186 93	0.238 114	0.204 116	0.407 112	0.506 117	0.449 108	0.667 110	0.620 115	0.462 110

SSC-UNet	0.308 120	0.353 118	0.290 121	0.278 121	0.166 120	0.553 118	0.169 120	0.286 116	0.147 121	0.148 121	0.908 107	0.182 119	0.064 115	0.023 121	0.018 121	0.354 115	0.363 119	0.345 119	0.546 118	0.685 108	0.278 117

Tangent Convolutions	0.438 114	0.437 117	0.646 103	0.474 113	0.369 110	0.645 110	0.353 101	0.258 117	0.282 119	0.279 113	0.918 105	0.298 108	0.147 108	0.283 111	0.294 109	0.487 104	0.562 108	0.427 113	0.619 113	0.633 114	0.352 115
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
PointNet++	0.339 118	0.584 105	0.478 119	0.458 115	0.256 119	0.360 121	0.250 113	0.247 118	0.278 120	0.261 117	0.677 120	0.183 118	0.117 110	0.212 118	0.145 119	0.364 114	0.346 121	0.232 121	0.548 116	0.523 120	0.252 119
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
ScanNet+FTSDF	0.383 117	0.297 119	0.491 118	0.432 117	0.358 113	0.612 115	0.274 112	0.116 119	0.411 108	0.265 115	0.904 109	0.229 116	0.079 114	0.250 112	0.185 117	0.320 117	0.510 114	0.385 116	0.548 116	0.597 119	0.394 112

FCPN	0.447 110	0.679 89	0.604 112	0.578 106	0.380 109	0.682 106	0.291 110	0.106 120	0.483 93	0.258 118	0.920 103	0.258 113	0.025 117	0.231 116	0.325 108	0.480 106	0.560 109	0.463 106	0.725 104	0.666 111	0.231 120
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
ERROR	0.054 122	0.000 122	0.041 122	0.172 122	0.030 122	0.062 123	0.001 122	0.035 121	0.004 122	0.051 122	0.143 122	0.019 122	0.003 120	0.041 119	0.050 120	0.003 121	0.054 122	0.018 122	0.005 123	0.264 122	0.082 122

ScanNet	0.306 121	0.203 120	0.366 120	0.501 109	0.311 117	0.524 119	0.211 119	0.002 122	0.342 115	0.189 120	0.786 118	0.145 120	0.102 111	0.245 113	0.152 118	0.318 118	0.348 120	0.300 120	0.460 119	0.437 121	0.182 121
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
MVF-GNN	0.014 123	0.000 122	0.000 123	0.000 123	0.007 123	0.086 122	0.000 123	0.000 123	0.001 123	0.000 123	0.029 123	0.001 123	0.000 121	0.000 123	0.000 122	0.000 122	0.000 123	0.018 122	0.015 122	0.115 123	0.000 123

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

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

DENet	0.413 40	0.741 30	0.520 36	0.237 56	0.284 40	0.523 55	0.097 31	0.691 1	0.138 45	0.209 55	0.229 57	0.238 43	0.390 46	0.707 33	0.310 32	0.448 60	0.470 29	0.892 28	0.310 33

SIM3D	0.617 3	0.952 4	0.629 17	0.539 11	0.426 16	0.768 11	0.302 6	0.681 2	0.425 9	0.473 16	0.511 16	0.701 2	0.717 1	0.821 6	0.467 15	0.774 1	0.559 15	0.914 18	0.448 3

SoftGroup	0.504 26	0.667 44	0.579 24	0.372 42	0.381 23	0.694 26	0.072 33	0.677 3	0.303 26	0.387 24	0.531 13	0.319 32	0.582 15	0.754 20	0.318 31	0.643 26	0.492 27	0.907 22	0.388 14
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
RPGN	0.428 39	0.630 52	0.508 43	0.367 43	0.249 48	0.658 35	0.016 56	0.673 4	0.131 48	0.234 45	0.383 40	0.270 38	0.434 35	0.748 22	0.274 39	0.609 30	0.406 38	0.842 47	0.267 46
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
SoftGroup++	0.513 24	0.704 37	0.578 26	0.398 36	0.363 30	0.704 24	0.061 37	0.647 5	0.297 30	0.378 26	0.537 10	0.343 28	0.614 13	0.828 5	0.295 35	0.710 14	0.505 26	0.875 34	0.394 11

DD-UNet+Group	0.436 35	0.630 52	0.508 44	0.480 17	0.310 38	0.624 45	0.065 35	0.638 6	0.174 41	0.256 42	0.384 39	0.194 49	0.428 37	0.759 19	0.289 36	0.574 40	0.400 39	0.849 42	0.291 38
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
SSEC	0.465 32	0.667 44	0.578 25	0.502 13	0.362 31	0.641 40	0.035 46	0.605 7	0.291 31	0.323 32	0.451 29	0.296 34	0.417 43	0.677 37	0.245 44	0.501 53	0.506 25	0.900 25	0.366 18

EV3D	0.615 5	0.946 5	0.652 13	0.555 6	0.433 14	0.773 6	0.271 13	0.604 8	0.447 5	0.506 7	0.544 7	0.698 3	0.716 2	0.775 16	0.480 9	0.747 5	0.572 13	0.925 14	0.435 6

Spherical Mask(CtoF)	0.616 4	0.946 5	0.654 12	0.555 6	0.434 13	0.769 10	0.271 12	0.604 8	0.447 5	0.505 8	0.549 3	0.698 3	0.716 2	0.775 16	0.480 9	0.747 5	0.575 11	0.925 14	0.436 5

CSC-Pretrained	0.405 42	0.738 32	0.465 50	0.331 49	0.205 54	0.655 36	0.051 41	0.601 10	0.092 55	0.211 54	0.329 45	0.198 48	0.459 31	0.775 14	0.195 51	0.524 51	0.400 40	0.878 31	0.184 56

Mask3D	0.566 17	0.926 8	0.597 20	0.408 34	0.420 18	0.737 18	0.239 17	0.598 11	0.386 14	0.458 20	0.549 3	0.568 19	0.716 2	0.601 45	0.480 9	0.646 25	0.575 11	0.922 16	0.364 19
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
PointRel	0.622 1	0.926 8	0.710 3	0.541 10	0.502 2	0.772 7	0.314 4	0.598 11	0.425 8	0.504 10	0.565 2	0.650 7	0.716 2	0.809 7	0.476 12	0.747 4	0.618 2	0.963 4	0.364 20
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
GraphCut	0.552 20	1.000 1	0.611 19	0.438 27	0.392 22	0.714 23	0.139 25	0.598 13	0.327 23	0.389 23	0.510 17	0.598 12	0.427 40	0.754 21	0.463 18	0.761 3	0.588 9	0.903 23	0.329 30

IPCA-Inst	0.520 23	0.889 14	0.551 31	0.548 9	0.418 19	0.665 33	0.064 36	0.585 14	0.260 33	0.277 38	0.471 25	0.500 24	0.644 9	0.785 11	0.369 26	0.591 35	0.511 22	0.878 32	0.362 23

PE	0.396 43	0.667 44	0.467 49	0.446 25	0.243 49	0.624 46	0.022 52	0.577 15	0.106 52	0.219 48	0.340 43	0.239 42	0.487 27	0.475 56	0.225 46	0.541 48	0.350 49	0.818 50	0.273 44
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
MG-Former	0.587 10	0.852 17	0.639 15	0.454 22	0.393 21	0.758 14	0.338 2	0.572 16	0.480 3	0.527 3	0.491 22	0.671 6	0.527 22	0.867 1	0.485 6	0.601 31	0.590 8	0.938 12	0.390 12

PointGroup	0.407 41	0.639 51	0.496 45	0.415 33	0.243 50	0.645 39	0.021 53	0.570 17	0.114 51	0.211 53	0.359 42	0.217 47	0.428 38	0.660 40	0.256 42	0.562 44	0.341 51	0.860 38	0.291 37
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]
SPFormer	0.549 21	0.745 28	0.640 14	0.484 16	0.395 20	0.739 17	0.311 5	0.566 18	0.335 21	0.468 18	0.492 21	0.555 20	0.478 28	0.747 23	0.436 20	0.712 12	0.540 21	0.893 27	0.343 27
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
Competitor-SPFormer	0.580 14	0.721 35	0.705 4	0.593 4	0.444 12	0.786 5	0.286 9	0.564 19	0.376 16	0.498 12	0.534 12	0.546 21	0.390 45	0.785 12	0.577 2	0.708 15	0.579 10	0.954 8	0.388 13

GICN	0.341 55	0.580 56	0.371 56	0.344 47	0.198 55	0.469 58	0.052 40	0.564 20	0.093 53	0.212 51	0.212 59	0.127 57	0.347 50	0.537 47	0.206 48	0.525 50	0.329 54	0.729 58	0.241 50

MAFT	0.596 8	0.889 14	0.721 2	0.448 23	0.460 9	0.768 12	0.251 16	0.558 21	0.408 11	0.504 9	0.539 9	0.616 11	0.618 12	0.858 3	0.482 8	0.684 19	0.551 18	0.931 13	0.450 2

TopoSeg	0.479 30	0.704 37	0.564 28	0.467 20	0.366 28	0.633 41	0.068 34	0.554 22	0.262 32	0.328 30	0.447 31	0.323 30	0.534 20	0.722 27	0.288 37	0.614 29	0.482 28	0.912 19	0.358 25

HAIS	0.457 34	0.704 37	0.561 29	0.457 21	0.364 29	0.673 30	0.046 45	0.547 23	0.194 40	0.308 33	0.426 32	0.288 35	0.454 32	0.711 30	0.262 41	0.563 43	0.434 35	0.889 29	0.344 26
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DualGroup	0.469 31	0.815 21	0.552 30	0.398 35	0.374 25	0.683 29	0.130 26	0.539 24	0.310 24	0.327 31	0.407 34	0.276 37	0.447 33	0.535 49	0.342 29	0.659 22	0.455 31	0.900 26	0.301 35

ISBNet	0.559 19	0.939 7	0.655 11	0.383 40	0.426 17	0.763 13	0.180 21	0.534 25	0.386 13	0.499 11	0.509 18	0.621 10	0.427 39	0.704 34	0.467 14	0.649 23	0.571 14	0.948 10	0.401 9
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
KmaxOneFormerNet	0.581 13	0.745 28	0.692 8	0.551 8	0.458 10	0.798 3	0.264 15	0.531 26	0.369 19	0.513 5	0.531 14	0.632 9	0.494 25	0.798 9	0.567 3	0.648 24	0.558 17	0.950 9	0.362 22

UniPerception	0.588 9	0.963 3	0.667 10	0.493 15	0.472 7	0.750 15	0.229 19	0.528 27	0.468 4	0.498 13	0.542 8	0.643 8	0.530 21	0.661 39	0.463 16	0.695 18	0.599 3	0.972 1	0.420 7

Queryformer	0.583 12	0.926 8	0.702 5	0.393 37	0.504 1	0.733 21	0.276 11	0.527 28	0.373 17	0.479 15	0.534 11	0.533 23	0.697 7	0.720 29	0.436 21	0.745 7	0.592 7	0.958 7	0.363 21

Mask-Group	0.434 37	0.778 25	0.516 39	0.471 19	0.330 34	0.658 34	0.029 48	0.526 29	0.249 34	0.256 41	0.400 35	0.309 33	0.384 48	0.296 65	0.368 27	0.575 39	0.425 36	0.877 33	0.362 24
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
TD3D	0.489 28	0.852 17	0.511 41	0.434 29	0.322 35	0.735 20	0.101 30	0.512 30	0.355 20	0.349 28	0.468 26	0.283 36	0.514 24	0.676 38	0.268 40	0.671 21	0.510 23	0.908 21	0.329 31
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
DKNet	0.532 22	0.815 21	0.624 18	0.517 12	0.377 24	0.749 16	0.107 27	0.509 31	0.304 25	0.437 21	0.475 23	0.581 15	0.539 19	0.775 15	0.339 30	0.640 27	0.506 24	0.901 24	0.385 15
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
Competitor-MAFT	0.618 2	0.866 16	0.724 1	0.628 1	0.484 4	0.803 2	0.300 7	0.509 32	0.496 1	0.539 1	0.547 6	0.703 1	0.668 8	0.708 32	0.463 17	0.708 16	0.595 4	0.959 6	0.418 8

DCD	0.614 6	0.892 13	0.633 16	0.434 28	0.495 3	0.810 1	0.292 8	0.501 33	0.408 10	0.525 4	0.582 1	0.688 5	0.625 10	0.801 8	0.608 1	0.672 20	0.649 1	0.965 3	0.476 1

One_Thing_One_Click	0.326 56	0.472 62	0.361 57	0.232 57	0.183 57	0.555 53	0.000 69	0.498 34	0.038 64	0.195 56	0.226 58	0.362 27	0.168 63	0.469 57	0.251 43	0.553 46	0.335 53	0.846 44	0.117 64
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
RWSeg	0.348 54	0.475 61	0.456 52	0.320 50	0.275 44	0.476 57	0.020 54	0.491 35	0.056 62	0.212 52	0.320 46	0.261 39	0.302 53	0.520 50	0.182 53	0.557 45	0.285 61	0.867 36	0.197 53

ExtMask3D	0.598 7	0.852 17	0.692 7	0.433 31	0.461 8	0.791 4	0.264 14	0.488 36	0.493 2	0.508 6	0.528 15	0.594 13	0.706 6	0.791 10	0.483 7	0.734 9	0.595 5	0.911 20	0.437 4

PBNet	0.573 15	0.926 8	0.575 27	0.619 2	0.472 6	0.736 19	0.239 18	0.487 37	0.383 15	0.459 19	0.506 19	0.533 22	0.585 14	0.767 18	0.404 24	0.717 10	0.559 16	0.969 2	0.381 16
Weiguang Zhao, Yuyao Yan, Chaolong Yang, Jianan Ye, Xi Yang, Kaizhu Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
AOIA	0.387 46	0.704 37	0.515 40	0.385 39	0.225 53	0.669 31	0.005 63	0.482 38	0.126 49	0.181 58	0.269 54	0.221 46	0.426 41	0.478 55	0.218 47	0.592 34	0.371 44	0.851 39	0.242 49

TST3D	0.569 16	0.778 25	0.675 9	0.598 3	0.451 11	0.727 22	0.280 10	0.476 39	0.395 12	0.472 17	0.457 28	0.583 14	0.580 16	0.777 13	0.462 19	0.735 8	0.547 20	0.919 17	0.333 28
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
InsSSM	0.586 11	1.000 1	0.593 21	0.440 26	0.480 5	0.771 8	0.345 1	0.437 40	0.444 7	0.495 14	0.548 5	0.579 16	0.621 11	0.720 28	0.409 23	0.712 11	0.593 6	0.960 5	0.395 10
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
DANCENET	0.504 26	0.926 8	0.579 23	0.472 18	0.367 27	0.626 43	0.165 23	0.432 41	0.221 35	0.408 22	0.449 30	0.411 26	0.564 17	0.746 24	0.421 22	0.707 17	0.438 33	0.846 43	0.288 40

PCJC	0.375 49	0.704 37	0.542 33	0.284 53	0.197 56	0.649 37	0.006 60	0.426 42	0.138 46	0.242 43	0.304 49	0.183 52	0.388 47	0.629 42	0.141 62	0.546 47	0.344 50	0.738 57	0.283 41

MTML	0.282 59	0.577 57	0.380 54	0.182 61	0.107 65	0.430 60	0.001 66	0.422 43	0.057 61	0.179 59	0.162 62	0.070 62	0.229 57	0.511 53	0.161 56	0.491 54	0.313 56	0.650 65	0.162 58
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
3D-BoNet	0.253 62	0.519 59	0.324 62	0.251 55	0.137 62	0.345 68	0.031 47	0.419 44	0.069 57	0.162 62	0.131 64	0.052 63	0.202 61	0.338 63	0.147 61	0.301 69	0.303 60	0.651 64	0.178 57
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
Occipital-SCS	0.320 57	0.679 43	0.352 58	0.334 48	0.229 51	0.436 59	0.025 49	0.412 45	0.058 60	0.161 63	0.240 56	0.085 59	0.262 54	0.496 54	0.187 52	0.467 57	0.328 55	0.775 52	0.231 51

OneFormer3D	0.566 17	0.781 24	0.697 6	0.562 5	0.431 15	0.770 9	0.331 3	0.400 46	0.373 18	0.529 2	0.504 20	0.568 18	0.475 29	0.732 26	0.470 13	0.762 2	0.550 19	0.871 35	0.379 17
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
SSEN	0.384 47	0.852 17	0.494 47	0.192 59	0.226 52	0.648 38	0.022 51	0.398 47	0.299 29	0.277 37	0.317 47	0.231 45	0.194 62	0.514 52	0.196 49	0.586 37	0.444 32	0.843 46	0.184 55
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
INS-Conv-instance	0.435 36	0.716 36	0.495 46	0.355 44	0.331 33	0.689 28	0.102 29	0.394 48	0.208 38	0.280 36	0.395 36	0.250 40	0.544 18	0.741 25	0.309 33	0.536 49	0.391 42	0.842 48	0.258 47

ODIN - Ins	0.463 33	0.738 32	0.589 22	0.344 46	0.358 32	0.560 52	0.139 24	0.393 49	0.331 22	0.373 27	0.392 37	0.496 25	0.493 26	0.709 31	0.377 25	0.599 33	0.359 47	0.752 55	0.332 29
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024
SSTNet	0.506 25	0.738 32	0.549 32	0.497 14	0.316 36	0.693 27	0.178 22	0.377 50	0.198 39	0.330 29	0.463 27	0.576 17	0.515 23	0.857 4	0.494 4	0.637 28	0.457 30	0.943 11	0.290 39
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
MASC	0.254 61	0.463 63	0.249 67	0.113 63	0.167 58	0.412 63	0.000 68	0.374 51	0.073 56	0.173 61	0.243 55	0.130 56	0.228 58	0.368 61	0.160 57	0.356 64	0.208 64	0.711 61	0.136 62
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
PanopticFusion-inst	0.214 65	0.250 71	0.330 61	0.275 54	0.103 66	0.228 74	0.000 69	0.345 52	0.024 66	0.088 67	0.203 61	0.186 51	0.167 64	0.367 62	0.125 63	0.221 72	0.112 74	0.666 63	0.162 59
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
3D-MPA	0.355 52	0.457 64	0.484 48	0.299 51	0.277 43	0.591 50	0.047 44	0.332 53	0.212 37	0.217 49	0.278 50	0.193 50	0.413 44	0.410 59	0.195 50	0.574 41	0.352 48	0.849 41	0.213 52
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF	0.353 53	0.593 54	0.511 42	0.375 41	0.264 45	0.597 48	0.008 58	0.332 54	0.160 43	0.229 47	0.274 53	0.000 76	0.206 59	0.678 36	0.155 59	0.485 55	0.422 37	0.816 51	0.254 48
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
OccuSeg+instance	0.486 29	0.802 23	0.536 34	0.428 32	0.369 26	0.702 25	0.205 20	0.331 55	0.301 27	0.379 25	0.474 24	0.327 29	0.437 34	0.862 2	0.485 5	0.601 32	0.394 41	0.846 45	0.273 43
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
OSIS	0.392 45	0.778 25	0.530 35	0.220 58	0.278 42	0.567 51	0.083 32	0.330 56	0.299 28	0.270 39	0.310 48	0.143 55	0.260 55	0.624 43	0.277 38	0.568 42	0.361 46	0.865 37	0.301 34

SPG_WSIS	0.251 63	0.380 66	0.274 65	0.289 52	0.144 60	0.413 62	0.000 69	0.311 57	0.065 58	0.113 65	0.130 65	0.029 68	0.204 60	0.388 60	0.108 65	0.459 58	0.311 57	0.769 53	0.127 63

Region-18class	0.146 69	0.175 75	0.321 63	0.080 66	0.062 67	0.357 65	0.000 69	0.307 58	0.002 73	0.066 70	0.044 70	0.000 76	0.018 74	0.036 75	0.054 67	0.447 61	0.133 69	0.472 68	0.060 71

ClickSeg_Instance	0.366 50	0.654 48	0.375 55	0.184 60	0.302 39	0.592 49	0.050 43	0.300 59	0.093 54	0.283 34	0.277 51	0.249 41	0.426 42	0.615 44	0.299 34	0.504 52	0.367 45	0.832 49	0.191 54

Box2Mask	0.433 38	0.741 30	0.463 51	0.433 30	0.283 41	0.625 44	0.103 28	0.298 60	0.125 50	0.260 40	0.424 33	0.322 31	0.472 30	0.701 35	0.363 28	0.711 13	0.309 59	0.882 30	0.272 45
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
SegGroup_ins	0.246 64	0.556 58	0.335 60	0.062 71	0.115 64	0.490 56	0.000 69	0.297 61	0.018 68	0.186 57	0.142 63	0.083 60	0.233 56	0.216 67	0.153 60	0.469 56	0.251 62	0.744 56	0.083 67
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SphereSeg	0.357 51	0.651 49	0.411 53	0.345 45	0.264 46	0.630 42	0.059 38	0.289 62	0.212 36	0.240 44	0.336 44	0.158 54	0.305 52	0.557 46	0.159 58	0.455 59	0.341 52	0.726 59	0.294 36

Mask3D_evaluation	0.382 48	0.593 54	0.520 37	0.390 38	0.314 37	0.600 47	0.018 55	0.287 63	0.151 44	0.281 35	0.387 38	0.169 53	0.429 36	0.654 41	0.172 55	0.578 38	0.384 43	0.670 62	0.278 42

SALoss-ResNet	0.262 60	0.667 44	0.335 59	0.067 69	0.123 63	0.427 61	0.022 50	0.280 64	0.058 59	0.216 50	0.211 60	0.039 65	0.142 65	0.519 51	0.106 66	0.338 66	0.310 58	0.721 60	0.138 61
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)
Dyco3D	0.395 44	0.642 50	0.518 38	0.447 24	0.259 47	0.666 32	0.050 42	0.251 65	0.166 42	0.231 46	0.362 41	0.232 44	0.331 51	0.535 48	0.229 45	0.587 36	0.438 34	0.850 40	0.317 32
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
Sparse R-CNN	0.292 58	0.704 37	0.213 68	0.153 62	0.154 59	0.551 54	0.053 39	0.212 66	0.132 47	0.174 60	0.274 52	0.070 61	0.363 49	0.441 58	0.176 54	0.424 62	0.234 63	0.758 54	0.161 60

SemRegionNet-20cls	0.121 70	0.296 69	0.203 69	0.071 67	0.058 69	0.349 66	0.000 69	0.150 67	0.019 67	0.054 72	0.034 73	0.017 71	0.052 70	0.042 74	0.013 75	0.209 73	0.183 65	0.371 70	0.057 72

3D-SIS	0.161 66	0.407 65	0.155 73	0.068 68	0.043 72	0.346 67	0.001 65	0.134 68	0.005 71	0.088 66	0.106 67	0.037 66	0.135 67	0.321 64	0.028 72	0.339 65	0.116 73	0.466 69	0.093 66
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.117 71	0.222 73	0.161 72	0.054 73	0.027 74	0.289 71	0.000 69	0.124 69	0.001 75	0.079 68	0.061 69	0.027 69	0.141 66	0.240 66	0.005 76	0.310 68	0.129 70	0.153 76	0.081 68
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp	0.113 73	0.333 68	0.151 74	0.056 72	0.053 70	0.344 69	0.000 69	0.105 70	0.016 69	0.049 73	0.035 72	0.020 70	0.053 69	0.048 73	0.013 74	0.183 75	0.173 67	0.344 73	0.054 73

UNet-backbone	0.161 66	0.519 59	0.259 66	0.084 65	0.059 68	0.325 70	0.002 64	0.093 71	0.009 70	0.077 69	0.064 68	0.045 64	0.044 72	0.161 69	0.045 68	0.331 67	0.180 66	0.566 66	0.033 76

ASIS	0.085 75	0.037 76	0.080 76	0.066 70	0.047 71	0.282 72	0.000 69	0.052 72	0.002 74	0.047 74	0.026 74	0.001 75	0.046 71	0.194 68	0.031 71	0.264 70	0.140 68	0.167 75	0.047 75

Sem_Recon_ins	0.098 74	0.295 70	0.187 70	0.015 76	0.036 73	0.213 75	0.005 62	0.038 73	0.003 72	0.056 71	0.037 71	0.036 67	0.015 75	0.051 72	0.044 69	0.209 74	0.098 75	0.354 72	0.071 69

R-PointNet	0.158 68	0.356 67	0.173 71	0.113 64	0.140 61	0.359 64	0.012 57	0.023 74	0.039 63	0.134 64	0.123 66	0.008 72	0.089 68	0.149 70	0.117 64	0.221 71	0.128 71	0.563 67	0.094 65

3D-BEVIS	0.117 71	0.250 71	0.308 64	0.020 75	0.009 77	0.269 73	0.006 61	0.008 75	0.029 65	0.037 75	0.014 76	0.003 74	0.036 73	0.147 71	0.042 70	0.381 63	0.118 72	0.362 71	0.069 70
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet	0.049 76	0.023 77	0.134 75	0.031 74	0.013 76	0.144 76	0.006 59	0.008 76	0.000 76	0.028 76	0.017 75	0.003 73	0.009 77	0.000 76	0.021 73	0.122 76	0.095 76	0.175 74	0.054 74

MaskRCNN 2d->3d Proj	0.022 77	0.185 74	0.000 77	0.000 77	0.015 75	0.000 77	0.000 67	0.006 77	0.000 76	0.010 77	0.006 77	0.107 58	0.012 76	0.000 76	0.002 77	0.027 77	0.004 77	0.022 77	0.001 77

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 2	0.512 1	0.422 17	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
CMX	0.613 5	0.681 8	0.725 12	0.502 12	0.634 6	0.297 18	0.478 10	0.830 2	0.651 4	0.537 7	0.924 4	0.375 7	0.315 14	0.686 7	0.451 14	0.714 5	0.543 21	0.504 6	0.894 7	0.823 5	0.688 4

MVF-GNN（2D）	0.636 3	0.606 14	0.794 4	0.434 16	0.688 1	0.337 8	0.464 12	0.798 3	0.632 5	0.589 3	0.908 8	0.420 2	0.329 12	0.743 2	0.594 2	0.738 2	0.676 5	0.527 4	0.906 2	0.818 6	0.715 3

BPNet_2D	0.670 2	0.822 3	0.795 3	0.836 2	0.659 3	0.481 2	0.451 13	0.769 4	0.656 3	0.567 4	0.931 3	0.395 6	0.390 5	0.700 4	0.534 4	0.689 10	0.770 2	0.574 3	0.865 9	0.831 3	0.675 5
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
FAN_NV_RVC	0.586 10	0.510 21	0.764 6	0.079 26	0.620 8	0.330 11	0.494 8	0.753 5	0.573 9	0.556 5	0.884 16	0.405 4	0.303 16	0.718 3	0.452 13	0.672 13	0.658 7	0.509 5	0.898 5	0.813 8	0.727 2

MIX6D_RVC	0.582 12	0.695 5	0.687 17	0.225 21	0.632 7	0.328 13	0.550 1	0.748 6	0.623 6	0.494 15	0.890 14	0.350 15	0.254 23	0.688 6	0.454 12	0.716 4	0.597 17	0.489 9	0.881 8	0.768 16	0.575 15

CU-Hybrid-2D Net	0.636 3	0.825 2	0.820 2	0.179 23	0.648 4	0.463 3	0.549 2	0.742 7	0.676 2	0.628 2	0.961 1	0.420 2	0.379 6	0.684 8	0.381 18	0.732 3	0.723 3	0.599 2	0.827 16	0.851 2	0.634 7

DMMF_3d	0.605 6	0.651 9	0.744 10	0.782 3	0.637 5	0.387 4	0.536 3	0.732 8	0.590 7	0.540 6	0.856 21	0.359 11	0.306 15	0.596 14	0.539 3	0.627 20	0.706 4	0.497 8	0.785 21	0.757 19	0.476 22

DMMF	0.567 14	0.623 10	0.767 5	0.238 20	0.571 13	0.347 6	0.413 19	0.719 9	0.472 20	0.418 22	0.895 13	0.357 12	0.260 22	0.696 5	0.523 7	0.666 17	0.642 11	0.437 18	0.895 6	0.793 10	0.603 12

SSMA	0.577 13	0.695 5	0.716 15	0.439 14	0.563 14	0.314 14	0.444 15	0.719 9	0.551 12	0.503 10	0.887 15	0.346 16	0.348 10	0.603 12	0.353 20	0.709 6	0.600 15	0.457 14	0.901 3	0.786 11	0.599 13
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
EMSANet	0.600 7	0.716 4	0.746 9	0.395 18	0.614 9	0.382 5	0.523 4	0.713 11	0.571 11	0.503 10	0.922 6	0.404 5	0.397 4	0.655 9	0.400 16	0.626 21	0.663 6	0.469 13	0.900 4	0.827 4	0.577 14
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
UNIV_CNP_RVC_UE	0.566 15	0.569 19	0.686 19	0.435 15	0.524 17	0.294 19	0.421 18	0.712 12	0.543 14	0.463 17	0.872 17	0.320 17	0.363 8	0.611 11	0.477 11	0.686 11	0.627 12	0.443 17	0.862 10	0.775 14	0.639 6

segfomer with 6d	0.542 19	0.594 15	0.687 17	0.146 24	0.579 12	0.308 16	0.515 6	0.703 13	0.472 20	0.498 13	0.868 18	0.369 9	0.282 17	0.589 15	0.390 17	0.701 9	0.556 20	0.416 21	0.860 12	0.759 18	0.539 19

EMSAFormer	0.564 16	0.581 16	0.736 11	0.564 10	0.546 16	0.219 23	0.517 5	0.675 14	0.486 19	0.427 21	0.904 11	0.352 14	0.320 13	0.589 15	0.528 5	0.708 7	0.464 24	0.413 22	0.847 14	0.786 11	0.611 11

MCA-Net	0.595 8	0.533 20	0.756 8	0.746 4	0.590 10	0.334 10	0.506 7	0.670 15	0.587 8	0.500 12	0.905 10	0.366 10	0.352 9	0.601 13	0.506 8	0.669 16	0.648 9	0.501 7	0.839 15	0.769 15	0.516 21

DCRedNet	0.583 11	0.682 7	0.723 13	0.542 11	0.510 20	0.310 15	0.451 13	0.668 16	0.549 13	0.520 9	0.920 7	0.375 7	0.446 2	0.528 20	0.417 15	0.670 15	0.577 18	0.478 11	0.862 10	0.806 9	0.628 9

FuseNet	0.535 20	0.570 18	0.681 20	0.182 22	0.512 19	0.290 20	0.431 16	0.659 17	0.504 18	0.495 14	0.903 12	0.308 19	0.428 3	0.523 21	0.365 19	0.676 12	0.621 14	0.470 12	0.762 22	0.779 13	0.541 17
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
RFBNet	0.592 9	0.616 11	0.758 7	0.659 5	0.581 11	0.330 11	0.469 11	0.655 18	0.543 14	0.524 8	0.924 4	0.355 13	0.336 11	0.572 17	0.479 10	0.671 14	0.648 9	0.480 10	0.814 19	0.814 7	0.614 10

MSeg1080_RVC	0.485 23	0.505 22	0.709 16	0.092 25	0.427 23	0.241 22	0.411 20	0.654 19	0.385 26	0.457 18	0.861 20	0.053 26	0.279 18	0.503 22	0.481 9	0.645 18	0.626 13	0.365 24	0.748 24	0.725 22	0.529 20
John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
UDSSEG_RVC	0.545 18	0.610 13	0.661 22	0.588 8	0.556 15	0.268 21	0.482 9	0.642 20	0.572 10	0.475 16	0.836 23	0.312 18	0.367 7	0.630 10	0.189 23	0.639 19	0.495 23	0.452 15	0.826 17	0.756 20	0.541 17

SN_RN152pyrx8_RVC	0.546 17	0.572 17	0.663 21	0.638 7	0.518 18	0.298 17	0.366 24	0.633 21	0.510 17	0.446 19	0.864 19	0.296 20	0.267 19	0.542 19	0.346 21	0.704 8	0.575 19	0.431 19	0.853 13	0.766 17	0.630 8

3DMV (2d proj)	0.498 22	0.481 24	0.612 23	0.579 9	0.456 22	0.343 7	0.384 21	0.623 22	0.525 16	0.381 23	0.845 22	0.254 22	0.264 21	0.557 18	0.182 24	0.581 24	0.598 16	0.429 20	0.760 23	0.661 25	0.446 24
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
ILC-PSPNet	0.475 24	0.490 23	0.581 24	0.289 19	0.507 21	0.067 26	0.379 22	0.610 23	0.417 24	0.435 20	0.822 25	0.278 21	0.267 19	0.503 22	0.228 22	0.616 23	0.533 22	0.375 23	0.820 18	0.729 21	0.560 16

AdapNet++	0.503 21	0.613 12	0.722 14	0.418 17	0.358 26	0.337 8	0.370 23	0.479 24	0.443 22	0.368 24	0.907 9	0.207 23	0.213 25	0.464 24	0.525 6	0.618 22	0.657 8	0.450 16	0.788 20	0.721 23	0.408 25
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
Enet (reimpl)	0.376 25	0.264 26	0.452 26	0.452 13	0.365 24	0.181 24	0.143 26	0.456 25	0.409 25	0.346 25	0.769 26	0.164 24	0.218 24	0.359 25	0.123 26	0.403 26	0.381 26	0.313 26	0.571 25	0.685 24	0.472 23
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 26	0.293 25	0.521 25	0.657 6	0.361 25	0.161 25	0.250 25	0.004 26	0.440 23	0.183 26	0.836 23	0.125 25	0.060 26	0.319 26	0.132 25	0.417 25	0.412 25	0.344 25	0.541 26	0.427 26	0.109 26
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17

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

FKNet	0.204 3	0.334 3	0.358 2	0.038 2	0.234 2	0.184 2	0.025 3	0.318 1	0.042 4	0.088 2	0.141 2	0.053 4	0.300 3	0.207 1	0.171 3	0.292 2	0.149 2	0.636 3	0.109 2

EMSANet (Instance)	0.241 1	0.401 1	0.439 1	0.085 1	0.242 1	0.220 1	0.081 1	0.289 2	0.117 2	0.121 1	0.182 1	0.126 1	0.346 1	0.181 2	0.181 2	0.358 1	0.156 1	0.675 2	0.131 1
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
UniDet_RVC	0.205 2	0.381 2	0.323 3	0.037 3	0.226 3	0.177 3	0.063 2	0.277 3	0.120 1	0.067 3	0.131 3	0.074 3	0.317 2	0.080 3	0.235 1	0.289 3	0.141 3	0.678 1	0.080 3

MaskRCNN_ScanNet	0.119 4	0.129 4	0.212 4	0.002 4	0.112 4	0.148 4	0.014 4	0.205 4	0.044 3	0.066 4	0.078 4	0.095 2	0.142 4	0.030 4	0.128 4	0.139 4	0.080 4	0.459 4	0.057 4
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

LAST-PCL-type	0.780 1	0.250 3	1.000 1	1.000 1	1.000 1	1.000 1	1.000 1	0.500 2	1.000 1	0.500 2	0.889 1	0.000 2	1.000 1	1.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, and Jian Zhang: Language-Assisted 3D Scene Understanding. arxiv23.12
multi-task	0.700 2	0.500 1	1.000 1	0.882 3	0.500 3	1.000 1	1.000 1	0.500 2	1.000 1	1.000 1	0.778 2	0.000 2	0.938 2	0.000 3
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
SE-ResNeXt-SSMA	0.498 4	0.000 5	0.812 4	0.941 2	0.500 3	0.500 4	0.500 3	0.500 2	0.429 5	0.500 2	0.667 3	0.500 1	0.625 4	0.000 3
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
3DASPP-SCE	0.691 3	0.500 1	0.938 3	0.824 4	1.000 1	1.000 1	0.500 3	1.000 1	0.857 3	0.500 2	0.556 4	0.000 2	0.812 3	0.500 2

resnet50_scannet	0.353 5	0.250 3	0.812 4	0.529 5	0.500 3	0.500 4	0.000 5	0.500 2	0.571 4	0.000 5	0.556 4	0.000 2	0.375 5	0.000 3