Benchmark Results - ScanNet Benchmark

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

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

ALS-MinkowskiNet	0.414 2	0.610 2	0.322 3	0.271 2	0.542 2	0.153 3	0.159 11	0.000 3	0.000 7	0.000 1	0.404 4	0.503 5	0.532 6	0.672 16	0.804 5	0.285 1	0.888 2	0.000 3	0.900 2	0.226 2	0.087 2	0.598 4	0.342 5	0.671 1	0.217 10	0.087 3	0.449 4	0.000 1	0.000 3	0.253 3	0.477 6	1.000 1	0.000 1	0.118 5	0.000 3	0.905 1	0.071 13	0.710 2	0.076 2	0.047 16	0.665 1	0.376 8	0.981 1	0.000 1	0.000 2	0.466 7	0.632 7	0.113 4	0.769 1	0.956 4	0.795 2	0.031 9	0.314 1	0.936 1	0.000 1	0.390 2	0.601 3	0.000 7	0.458 8	0.366 2	0.719 3	0.440 5	0.564 1	0.699 4	0.314 1	0.464 7	0.784 2	0.200 1	0.283 6	0.973 1	0.142 9	0.000 1	0.250 7	0.285 6	0.220 7	0.718 1	0.752 6	0.723 2	0.460 1	0.248 15	0.475 10	0.463 13	0.000 4	0.000 1	0.446 8	0.021 5	0.025 11	0.285 1	0.000 4	0.972 1	0.149 8	0.769 1	0.230 3	0.535 1	0.879 2	0.252 8	0.000 1	0.693 1	0.129 2	0.000 14	0.000 4	0.000 1	0.447 1	0.958 1	0.662 9	0.159 2	0.598 3	0.780 11	0.344 2	0.646 3	0.106 6	0.893 3	0.135 3	0.455 3	0.000 1	0.194 3	0.259 1	0.726 3	0.475 4	0.000 9	0.000 1	0.741 1	0.865 1	0.571 2	0.817 3	0.445 3	0.000 1	0.506 2	0.630 3	0.230 12	0.916 2	0.728 1	0.635 1	1.000 1	0.252 6	0.000 1	0.804 2	0.697 7	0.137 11	0.043 7	0.717 2	0.807 3	0.000 1	0.510 13	0.245 2	0.000 7	0.000 1	0.709 3	0.000 2	0.000 1	0.703 2	0.572 4	0.646 2	0.223 10	0.531 5	0.984 1	0.397 3	0.813 1	0.798 1	0.135 12	0.800 1	0.000 1	0.097 2	0.832 2	0.752 8	0.842 7	0.000 1	0.852 1	0.149 9	0.846 10	0.000 1	0.666 5	0.359 5	0.252 8	0.777 1	0.690 2
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. CVPR 2025
PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 3	0.520 3	0.109 5	0.108 16	0.000 3	0.337 1	0.000 1	0.310 12	0.394 9	0.494 11	0.753 9	0.848 2	0.256 3	0.717 8	0.000 3	0.842 4	0.192 5	0.065 3	0.449 10	0.346 4	0.546 6	0.190 13	0.000 9	0.384 7	0.000 1	0.000 3	0.218 4	0.505 2	0.791 3	0.000 1	0.136 4	0.000 3	0.903 2	0.073 12	0.687 6	0.000 7	0.168 2	0.551 5	0.387 7	0.941 3	0.000 1	0.000 2	0.397 12	0.654 3	0.000 10	0.714 5	0.759 15	0.752 7	0.118 4	0.264 4	0.926 3	0.000 1	0.048 6	0.575 5	0.000 7	0.597 2	0.366 2	0.755 1	0.469 2	0.474 3	0.798 2	0.140 10	0.617 3	0.692 7	0.000 7	0.592 4	0.971 2	0.188 4	0.000 1	0.133 9	0.593 2	0.349 1	0.650 3	0.717 8	0.699 3	0.455 2	0.790 2	0.523 4	0.636 1	0.301 1	0.000 1	0.622 2	0.000 11	0.017 15	0.259 3	0.000 4	0.921 3	0.337 1	0.733 2	0.210 4	0.514 2	0.860 8	0.407 1	0.000 1	0.688 2	0.109 8	0.000 14	0.000 4	0.000 1	0.151 5	0.671 8	0.782 2	0.115 13	0.641 2	0.903 2	0.349 1	0.616 4	0.088 7	0.832 8	0.000 6	0.480 2	0.000 1	0.428 1	0.000 3	0.497 10	0.000 5	0.000 9	0.000 1	0.662 3	0.690 2	0.612 1	0.828 1	0.575 1	0.000 1	0.404 7	0.644 2	0.325 7	0.887 4	0.728 1	0.009 16	0.134 7	0.026 17	0.000 1	0.761 3	0.731 4	0.172 6	0.077 4	0.528 8	0.727 7	0.000 1	0.603 5	0.220 5	0.022 3	0.000 1	0.740 1	0.000 2	0.000 1	0.661 4	0.586 2	0.566 4	0.436 4	0.531 5	0.978 3	0.457 2	0.708 3	0.583 6	0.141 7	0.748 3	0.000 1	0.026 5	0.822 3	0.871 4	0.879 5	0.000 1	0.851 2	0.405 2	0.914 1	0.000 1	0.682 3	0.000 15	0.281 4	0.738 3	0.463 6
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)
DITR	0.449 1	0.629 1	0.392 1	0.289 1	0.650 1	0.168 2	0.862 1	0.000 3	0.313 3	0.000 1	0.580 1	0.568 2	0.564 3	0.766 7	0.867 1	0.238 5	0.949 1	0.000 3	0.866 3	0.300 1	0.000 9	0.664 1	0.482 1	0.508 12	0.317 1	0.420 1	0.551 2	0.000 1	0.000 3	0.486 2	0.519 1	0.662 4	0.000 1	0.385 1	0.000 3	0.901 3	0.079 9	0.727 1	0.000 7	0.160 3	0.606 3	0.417 4	0.967 2	0.000 1	0.000 2	0.498 5	0.596 11	0.130 2	0.728 3	0.998 1	0.805 1	0.000 17	0.314 1	0.934 2	0.000 1	0.278 4	0.636 1	0.000 7	0.403 12	0.367 1	0.741 2	0.484 1	0.500 2	1.000 1	0.113 12	0.828 1	0.815 1	0.000 7	0.733 2	0.969 4	0.374 2	0.000 1	0.579 1	1.000 1	0.230 5	0.617 5	0.983 1	0.729 1	0.423 4	0.855 1	0.508 6	0.622 2	0.018 3	0.000 1	0.591 3	0.034 4	0.028 10	0.066 11	0.869 1	0.904 7	0.334 2	0.651 5	0.716 1	0.514 2	0.871 6	0.315 3	0.000 1	0.664 3	0.128 3	0.014 10	0.000 4	0.000 1	0.392 2	0.851 2	0.817 1	0.153 3	0.823 1	0.991 1	0.318 3	0.680 1	0.134 3	0.913 1	0.157 2	0.448 4	0.000 1	0.000 8	0.000 3	0.826 1	0.978 1	0.091 6	0.000 1	0.660 4	0.647 3	0.571 2	0.804 4	0.001 9	0.000 1	0.480 3	0.700 1	0.421 5	0.947 1	0.433 14	0.411 3	0.148 6	0.262 5	0.000 1	0.849 1	0.709 6	0.138 10	0.150 2	0.714 3	0.889 1	0.000 1	0.698 1	0.222 4	0.000 7	0.000 1	0.720 2	0.000 2	0.000 1	0.805 1	0.600 1	0.642 3	0.268 9	0.904 1	0.982 2	0.477 1	0.632 6	0.718 2	0.139 9	0.776 2	0.000 1	0.178 1	0.886 1	0.962 1	0.839 8	0.000 1	0.851 2	0.043 12	0.869 4	0.000 1	0.710 1	0.315 6	0.348 3	0.753 2	0.397 8
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.
BFANet ScanNet200	0.360 5	0.553 7	0.293 5	0.193 5	0.483 10	0.096 6	0.266 6	0.000 3	0.000 7	0.000 1	0.298 13	0.255 12	0.661 1	0.810 5	0.810 3	0.194 10	0.785 7	0.000 3	0.000 17	0.161 6	0.000 9	0.494 9	0.382 3	0.574 3	0.258 5	0.000 9	0.372 9	0.000 1	0.000 3	0.043 14	0.436 8	0.000 11	0.000 1	0.239 3	0.000 3	0.901 3	0.105 1	0.689 4	0.025 4	0.128 4	0.614 2	0.436 1	0.493 17	0.000 1	0.000 2	0.526 4	0.546 13	0.109 5	0.651 14	0.953 5	0.753 6	0.101 5	0.143 13	0.897 5	0.000 1	0.431 1	0.469 15	0.000 7	0.522 6	0.337 5	0.661 6	0.459 3	0.409 6	0.666 5	0.102 14	0.508 6	0.757 4	0.000 7	0.060 14	0.970 3	0.497 1	0.000 1	0.376 3	0.511 3	0.262 4	0.688 2	0.921 2	0.617 10	0.321 12	0.590 6	0.491 9	0.556 4	0.000 4	0.000 1	0.481 5	0.093 1	0.043 3	0.284 2	0.000 4	0.875 14	0.135 9	0.669 4	0.124 13	0.394 6	0.849 11	0.298 4	0.000 1	0.476 17	0.088 13	0.042 7	0.000 4	0.000 1	0.254 4	0.653 10	0.741 6	0.215 1	0.573 5	0.852 5	0.266 10	0.654 2	0.056 12	0.835 6	0.000 6	0.492 1	0.000 1	0.000 8	0.000 3	0.612 9	0.000 5	0.000 9	0.000 1	0.616 6	0.469 17	0.460 5	0.698 14	0.516 2	0.000 1	0.378 8	0.563 4	0.476 4	0.863 5	0.574 9	0.330 6	0.000 11	0.282 3	0.000 1	0.760 4	0.710 5	0.233 1	0.000 10	0.641 5	0.814 2	0.000 1	0.585 10	0.053 11	0.000 7	0.000 1	0.629 10	0.000 2	0.000 1	0.678 3	0.528 13	0.534 5	0.129 14	0.596 4	0.973 4	0.264 12	0.772 2	0.526 10	0.139 9	0.707 4	0.000 1	0.000 12	0.764 14	0.591 16	0.848 6	0.000 1	0.827 4	0.338 3	0.806 12	0.000 1	0.568 9	0.151 10	0.358 2	0.659 10	0.510 4
Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis. CVPR 2025
PonderV2 ScanNet200	0.346 6	0.552 8	0.270 8	0.175 9	0.497 7	0.070 12	0.239 7	0.000 3	0.000 7	0.000 1	0.232 17	0.412 8	0.584 2	0.842 3	0.804 5	0.212 7	0.540 10	0.000 3	0.433 16	0.106 10	0.000 9	0.590 5	0.290 12	0.548 5	0.243 7	0.000 9	0.356 11	0.000 1	0.000 3	0.062 10	0.398 13	0.441 10	0.000 1	0.104 10	0.000 3	0.888 5	0.076 11	0.682 9	0.030 3	0.094 7	0.491 11	0.351 12	0.869 10	0.000 1	0.063 1	0.403 11	0.700 2	0.000 10	0.660 13	0.881 9	0.761 3	0.050 8	0.186 10	0.852 13	0.000 1	0.007 9	0.570 8	0.100 2	0.565 3	0.326 6	0.641 10	0.431 6	0.290 14	0.621 6	0.259 3	0.408 11	0.622 10	0.125 2	0.082 12	0.950 5	0.179 5	0.000 1	0.263 6	0.424 5	0.193 9	0.558 7	0.880 4	0.545 13	0.375 7	0.727 3	0.445 12	0.499 8	0.000 4	0.000 1	0.475 7	0.002 9	0.034 6	0.083 8	0.000 4	0.924 2	0.290 4	0.636 6	0.115 14	0.400 5	0.874 4	0.186 10	0.000 1	0.611 8	0.128 3	0.113 2	0.000 4	0.000 1	0.000 11	0.584 12	0.636 10	0.103 14	0.385 10	0.843 6	0.283 4	0.603 6	0.080 8	0.825 10	0.000 6	0.377 10	0.000 1	0.000 8	0.000 3	0.457 11	0.000 5	0.000 9	0.000 1	0.574 12	0.608 9	0.481 4	0.792 5	0.394 5	0.000 1	0.357 10	0.503 11	0.261 10	0.817 13	0.504 12	0.304 7	0.472 4	0.115 11	0.000 1	0.750 7	0.677 9	0.202 2	0.000 10	0.509 9	0.729 6	0.000 1	0.519 12	0.000 14	0.000 7	0.000 1	0.620 12	0.000 2	0.000 1	0.660 6	0.560 7	0.486 6	0.384 6	0.346 10	0.952 5	0.247 14	0.667 4	0.436 12	0.269 3	0.691 6	0.000 1	0.010 7	0.787 10	0.889 3	0.880 4	0.000 1	0.810 7	0.336 4	0.860 8	0.000 1	0.606 8	0.009 11	0.248 9	0.681 7	0.392 9
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.
L3DETR-ScanNet_200	0.336 8	0.533 11	0.279 6	0.155 10	0.508 5	0.073 11	0.101 17	0.000 3	0.058 6	0.000 1	0.294 14	0.233 14	0.548 4	0.927 1	0.788 10	0.264 2	0.463 11	0.000 3	0.638 12	0.098 13	0.014 7	0.411 12	0.226 13	0.525 10	0.225 9	0.010 7	0.397 6	0.000 1	0.000 3	0.192 6	0.380 14	0.598 6	0.000 1	0.117 6	0.000 3	0.883 6	0.082 8	0.689 4	0.000 7	0.032 17	0.549 6	0.417 4	0.910 5	0.000 1	0.000 2	0.448 8	0.613 9	0.000 10	0.697 7	0.960 3	0.759 4	0.158 2	0.293 3	0.883 7	0.000 1	0.312 3	0.583 4	0.079 4	0.422 11	0.068 17	0.660 7	0.418 7	0.298 12	0.430 12	0.114 11	0.526 5	0.776 3	0.051 3	0.679 3	0.946 6	0.152 7	0.000 1	0.183 8	0.000 15	0.211 8	0.511 10	0.409 16	0.565 12	0.355 8	0.448 8	0.512 5	0.557 3	0.000 4	0.000 1	0.420 9	0.000 11	0.007 17	0.104 6	0.000 4	0.125 17	0.330 3	0.514 15	0.146 12	0.321 13	0.860 8	0.174 11	0.000 1	0.629 6	0.075 14	0.000 14	0.000 4	0.000 1	0.002 10	0.671 8	0.712 7	0.141 6	0.339 12	0.856 4	0.261 12	0.529 10	0.067 10	0.835 6	0.000 6	0.369 12	0.000 1	0.259 2	0.000 3	0.629 6	0.000 5	0.487 1	0.000 1	0.579 11	0.646 4	0.107 17	0.720 11	0.122 7	0.000 1	0.333 14	0.505 10	0.303 9	0.908 3	0.503 13	0.565 2	0.074 8	0.324 1	0.000 1	0.740 8	0.661 11	0.109 13	0.000 10	0.427 13	0.563 17	0.000 1	0.579 11	0.108 8	0.000 7	0.000 1	0.664 6	0.000 2	0.000 1	0.641 7	0.539 11	0.416 7	0.515 2	0.256 11	0.940 12	0.312 6	0.209 17	0.620 3	0.138 11	0.636 11	0.000 1	0.000 12	0.775 13	0.861 5	0.765 12	0.000 1	0.801 9	0.119 11	0.860 8	0.000 1	0.687 2	0.001 14	0.192 14	0.679 9	0.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
PPT-SpUNet-F.T.	0.332 12	0.556 6	0.270 7	0.123 14	0.519 4	0.091 7	0.349 4	0.000 3	0.000 7	0.000 1	0.339 9	0.383 10	0.498 10	0.833 4	0.807 4	0.241 4	0.584 9	0.000 3	0.755 7	0.124 8	0.000 9	0.608 3	0.330 8	0.530 9	0.314 2	0.000 9	0.374 8	0.000 1	0.000 3	0.197 5	0.459 7	0.000 11	0.000 1	0.117 6	0.000 3	0.876 7	0.095 2	0.682 9	0.000 7	0.086 8	0.518 7	0.433 2	0.930 4	0.000 1	0.000 2	0.563 3	0.542 14	0.077 7	0.715 4	0.858 11	0.756 5	0.008 16	0.171 12	0.874 8	0.000 1	0.039 7	0.550 11	0.000 7	0.545 5	0.256 8	0.657 8	0.453 4	0.351 10	0.449 11	0.213 6	0.392 12	0.611 11	0.000 7	0.037 15	0.946 6	0.138 13	0.000 1	0.000 12	0.063 11	0.308 2	0.537 8	0.796 5	0.673 4	0.323 11	0.392 10	0.400 14	0.509 7	0.000 4	0.000 1	0.649 1	0.000 11	0.023 12	0.000 12	0.000 4	0.914 6	0.002 16	0.506 16	0.163 11	0.359 8	0.872 5	0.000 12	0.000 1	0.623 7	0.112 6	0.001 12	0.000 4	0.000 1	0.021 9	0.753 5	0.565 15	0.150 4	0.579 4	0.806 9	0.267 9	0.616 4	0.042 14	0.783 13	0.000 6	0.374 11	0.000 1	0.000 8	0.000 3	0.620 8	0.000 5	0.000 9	0.000 1	0.572 13	0.634 5	0.350 9	0.792 5	0.000 10	0.000 1	0.376 9	0.535 6	0.378 6	0.855 7	0.672 3	0.074 13	0.000 11	0.185 10	0.000 1	0.727 12	0.660 12	0.076 17	0.000 10	0.432 12	0.646 10	0.000 1	0.594 8	0.006 13	0.000 7	0.000 1	0.658 7	0.000 2	0.000 1	0.661 4	0.549 10	0.300 14	0.291 8	0.045 14	0.942 11	0.304 8	0.600 8	0.572 7	0.135 12	0.695 5	0.000 1	0.008 9	0.793 9	0.942 2	0.899 2	0.000 1	0.816 6	0.181 7	0.897 2	0.000 1	0.679 4	0.223 8	0.264 5	0.691 5	0.345 12
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
OA-CNN-L_ScanNet200	0.333 11	0.558 5	0.269 9	0.124 13	0.448 14	0.080 9	0.272 5	0.000 3	0.000 7	0.000 1	0.342 8	0.515 4	0.524 7	0.713 13	0.789 9	0.158 12	0.384 12	0.000 3	0.806 6	0.125 7	0.000 9	0.496 8	0.332 7	0.498 14	0.227 8	0.024 6	0.474 3	0.000 1	0.003 2	0.071 9	0.487 3	0.000 11	0.000 1	0.110 8	0.000 3	0.876 7	0.013 17	0.703 3	0.000 7	0.076 9	0.473 12	0.355 11	0.906 6	0.000 1	0.000 2	0.476 6	0.706 1	0.000 10	0.672 10	0.835 13	0.748 9	0.015 13	0.223 7	0.860 11	0.000 1	0.000 11	0.572 7	0.000 7	0.509 7	0.313 7	0.662 4	0.398 13	0.396 8	0.411 13	0.276 2	0.527 4	0.711 5	0.000 7	0.076 13	0.946 6	0.166 6	0.000 1	0.022 10	0.160 7	0.183 13	0.493 13	0.699 9	0.637 6	0.403 6	0.330 12	0.406 13	0.526 6	0.024 2	0.000 1	0.392 11	0.000 11	0.016 16	0.000 12	0.196 3	0.915 5	0.112 12	0.557 10	0.197 6	0.352 10	0.877 3	0.000 12	0.000 1	0.592 12	0.103 11	0.000 14	0.067 1	0.000 1	0.089 7	0.735 7	0.625 11	0.130 9	0.568 6	0.836 7	0.271 8	0.534 9	0.043 13	0.799 11	0.001 5	0.445 5	0.000 1	0.000 8	0.024 2	0.661 4	0.000 5	0.262 3	0.000 1	0.591 8	0.517 13	0.373 8	0.788 7	0.021 8	0.000 1	0.455 4	0.517 9	0.320 8	0.823 12	0.200 16	0.001 17	0.150 5	0.100 12	0.000 1	0.736 9	0.668 10	0.103 14	0.052 6	0.662 4	0.720 8	0.000 1	0.602 6	0.112 7	0.002 6	0.000 1	0.637 9	0.000 2	0.000 1	0.621 10	0.569 5	0.398 9	0.412 5	0.234 12	0.949 6	0.363 5	0.492 14	0.495 11	0.251 4	0.665 9	0.000 1	0.001 11	0.805 7	0.833 6	0.794 11	0.000 1	0.821 5	0.314 5	0.843 11	0.000 1	0.560 10	0.245 7	0.262 6	0.713 4	0.370 11

CeCo	0.340 7	0.551 9	0.247 13	0.181 6	0.475 12	0.057 15	0.142 12	0.000 3	0.000 7	0.000 1	0.387 6	0.463 6	0.499 9	0.924 2	0.774 11	0.213 6	0.257 13	0.000 3	0.546 15	0.100 11	0.006 8	0.615 2	0.177 17	0.534 7	0.246 6	0.000 9	0.400 5	0.000 1	0.338 1	0.006 16	0.484 5	0.609 5	0.000 1	0.083 11	0.000 3	0.873 9	0.089 5	0.661 14	0.000 7	0.048 15	0.560 4	0.408 6	0.892 8	0.000 1	0.000 2	0.586 1	0.616 8	0.000 10	0.692 8	0.900 8	0.721 12	0.162 1	0.228 6	0.860 11	0.000 1	0.000 11	0.575 5	0.083 3	0.550 4	0.347 4	0.624 13	0.410 10	0.360 9	0.740 3	0.109 13	0.321 15	0.660 8	0.000 7	0.121 9	0.939 13	0.143 8	0.000 1	0.400 2	0.003 13	0.190 11	0.564 6	0.652 10	0.615 11	0.421 5	0.304 13	0.579 1	0.547 5	0.000 4	0.000 1	0.296 14	0.000 11	0.030 9	0.096 7	0.000 4	0.916 4	0.037 13	0.551 12	0.171 9	0.376 7	0.865 7	0.286 5	0.000 1	0.633 5	0.102 12	0.027 8	0.011 3	0.000 1	0.000 11	0.474 14	0.742 5	0.133 7	0.311 13	0.824 8	0.242 13	0.503 14	0.068 9	0.828 9	0.000 6	0.429 7	0.000 1	0.063 5	0.000 3	0.781 2	0.000 5	0.000 9	0.000 1	0.665 2	0.633 6	0.450 6	0.818 2	0.000 10	0.000 1	0.429 5	0.532 7	0.226 13	0.825 11	0.510 11	0.377 5	0.709 2	0.079 14	0.000 1	0.753 5	0.683 8	0.102 15	0.063 5	0.401 16	0.620 13	0.000 1	0.619 3	0.000 14	0.000 7	0.000 1	0.595 13	0.000 2	0.000 1	0.345 14	0.564 6	0.411 8	0.603 1	0.384 8	0.945 9	0.266 11	0.643 5	0.367 14	0.304 1	0.663 10	0.000 1	0.010 7	0.726 15	0.767 7	0.898 3	0.000 1	0.784 13	0.435 1	0.861 7	0.000 1	0.447 11	0.000 15	0.257 7	0.656 11	0.377 10
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
OctFormer ScanNet200	0.326 13	0.539 10	0.265 10	0.131 12	0.499 6	0.110 4	0.522 3	0.000 3	0.000 7	0.000 1	0.318 11	0.427 7	0.455 15	0.743 11	0.765 13	0.175 11	0.842 4	0.000 3	0.828 5	0.204 4	0.033 6	0.429 11	0.335 6	0.601 2	0.312 3	0.000 9	0.357 10	0.000 1	0.000 3	0.047 11	0.423 9	0.000 11	0.000 1	0.105 9	0.000 3	0.873 9	0.079 9	0.670 12	0.000 7	0.117 5	0.471 13	0.432 3	0.829 11	0.000 1	0.000 2	0.584 2	0.417 17	0.089 6	0.684 9	0.837 12	0.705 16	0.021 12	0.178 11	0.892 6	0.000 1	0.028 8	0.505 13	0.000 7	0.457 9	0.200 14	0.662 4	0.412 9	0.244 15	0.496 8	0.000 17	0.451 8	0.626 9	0.000 7	0.102 11	0.943 9	0.138 13	0.000 1	0.000 12	0.149 8	0.291 3	0.534 9	0.722 7	0.632 7	0.331 10	0.253 14	0.453 11	0.487 11	0.000 4	0.000 1	0.479 6	0.000 11	0.022 13	0.000 12	0.000 4	0.900 10	0.128 11	0.684 3	0.164 10	0.413 4	0.854 10	0.000 12	0.000 1	0.512 16	0.074 15	0.003 11	0.000 4	0.000 1	0.000 11	0.469 15	0.613 12	0.132 8	0.529 7	0.871 3	0.227 16	0.582 7	0.026 17	0.787 12	0.000 6	0.339 15	0.000 1	0.000 8	0.000 3	0.626 7	0.000 5	0.029 8	0.000 1	0.587 9	0.612 8	0.411 7	0.724 10	0.000 10	0.000 1	0.407 6	0.552 5	0.513 3	0.849 10	0.655 4	0.408 4	0.000 11	0.296 2	0.000 1	0.686 15	0.645 14	0.145 8	0.022 8	0.414 14	0.633 11	0.000 1	0.637 2	0.224 3	0.000 7	0.000 1	0.650 8	0.000 2	0.000 1	0.622 9	0.535 12	0.343 12	0.483 3	0.230 13	0.943 10	0.289 10	0.618 7	0.596 5	0.140 8	0.679 8	0.000 1	0.022 6	0.783 11	0.620 12	0.906 1	0.000 1	0.806 8	0.137 10	0.865 5	0.000 1	0.378 12	0.000 15	0.168 15	0.680 8	0.227 13
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
IMFSegNet	0.334 9	0.532 13	0.251 11	0.179 7	0.486 9	0.041 16	0.139 13	0.003 1	0.283 4	0.000 1	0.274 15	0.191 15	0.457 14	0.704 14	0.795 7	0.197 9	0.830 6	0.000 3	0.710 9	0.055 16	0.064 4	0.518 6	0.305 10	0.458 17	0.216 12	0.027 5	0.284 13	0.000 1	0.000 3	0.044 12	0.406 10	0.561 7	0.000 1	0.080 12	0.000 3	0.873 9	0.021 15	0.683 8	0.000 7	0.076 9	0.494 10	0.363 9	0.648 16	0.000 1	0.000 2	0.425 9	0.649 4	0.000 10	0.668 12	0.908 7	0.740 11	0.010 14	0.206 8	0.862 10	0.000 1	0.000 11	0.560 9	0.000 7	0.359 13	0.237 11	0.631 12	0.408 11	0.411 4	0.322 15	0.246 4	0.439 10	0.599 13	0.047 4	0.213 7	0.940 10	0.139 11	0.000 1	0.369 5	0.124 10	0.188 12	0.495 11	0.624 11	0.626 8	0.320 14	0.595 4	0.495 8	0.496 10	0.000 4	0.000 1	0.340 12	0.014 6	0.032 7	0.135 5	0.000 4	0.903 8	0.277 6	0.612 8	0.196 7	0.344 12	0.848 13	0.260 6	0.000 1	0.574 13	0.073 16	0.062 4	0.000 4	0.000 1	0.091 6	0.839 3	0.776 3	0.123 12	0.392 9	0.756 12	0.274 5	0.518 12	0.029 16	0.842 4	0.000 6	0.357 13	0.000 1	0.035 7	0.000 3	0.444 12	0.793 2	0.245 5	0.000 1	0.512 16	0.512 15	0.159 15	0.713 13	0.000 10	0.000 1	0.336 13	0.484 12	0.569 2	0.852 9	0.615 6	0.120 12	0.068 10	0.228 8	0.000 1	0.733 10	0.773 2	0.190 4	0.000 10	0.608 6	0.792 4	0.000 1	0.597 7	0.000 14	0.025 2	0.000 1	0.573 17	0.000 2	0.000 1	0.508 11	0.555 8	0.363 10	0.139 12	0.610 2	0.947 8	0.305 7	0.594 9	0.527 9	0.009 17	0.633 13	0.000 1	0.060 3	0.820 5	0.604 15	0.799 9	0.000 1	0.799 11	0.034 14	0.784 13	0.000 1	0.618 6	0.424 2	0.134 16	0.646 13	0.214 14

GSTran	0.334 10	0.533 12	0.250 12	0.179 8	0.487 8	0.041 16	0.139 13	0.003 1	0.273 5	0.000 1	0.273 16	0.189 16	0.465 12	0.704 14	0.794 8	0.198 8	0.831 5	0.000 3	0.712 8	0.055 16	0.063 5	0.518 6	0.306 9	0.459 16	0.217 10	0.028 4	0.282 14	0.000 1	0.000 3	0.044 12	0.405 11	0.558 8	0.000 1	0.080 12	0.000 3	0.873 9	0.020 16	0.684 7	0.000 7	0.075 12	0.496 9	0.363 9	0.651 15	0.000 1	0.000 2	0.425 9	0.648 5	0.000 10	0.669 11	0.914 6	0.741 10	0.009 15	0.200 9	0.864 9	0.000 1	0.000 11	0.560 9	0.000 7	0.357 14	0.233 12	0.633 11	0.408 11	0.411 4	0.320 16	0.242 5	0.440 9	0.598 14	0.047 4	0.205 8	0.940 10	0.139 11	0.000 1	0.372 4	0.138 9	0.191 10	0.495 11	0.618 13	0.624 9	0.321 12	0.595 4	0.496 7	0.499 8	0.000 4	0.000 1	0.340 12	0.014 6	0.032 7	0.136 4	0.000 4	0.903 8	0.279 5	0.601 9	0.198 5	0.345 11	0.849 11	0.260 6	0.000 1	0.573 14	0.072 17	0.060 5	0.000 4	0.000 1	0.089 7	0.838 4	0.775 4	0.125 11	0.381 11	0.752 13	0.274 5	0.517 13	0.032 15	0.841 5	0.000 6	0.354 14	0.000 1	0.047 6	0.000 3	0.439 13	0.787 3	0.252 4	0.000 1	0.512 16	0.507 16	0.158 16	0.717 12	0.000 10	0.000 1	0.337 12	0.483 13	0.570 1	0.853 8	0.614 7	0.121 11	0.070 9	0.229 7	0.000 1	0.732 11	0.773 2	0.193 3	0.000 10	0.606 7	0.791 5	0.000 1	0.593 9	0.000 14	0.010 5	0.000 1	0.574 16	0.000 2	0.000 1	0.507 12	0.554 9	0.361 11	0.136 13	0.608 3	0.948 7	0.304 8	0.593 10	0.533 8	0.011 16	0.634 12	0.000 1	0.060 3	0.821 4	0.613 13	0.797 10	0.000 1	0.799 11	0.036 13	0.782 14	0.000 1	0.609 7	0.423 3	0.133 17	0.647 12	0.213 15

AWCS	0.305 14	0.508 14	0.225 14	0.142 11	0.463 13	0.063 13	0.195 9	0.000 3	0.000 7	0.000 1	0.467 3	0.551 3	0.504 8	0.773 6	0.764 14	0.142 13	0.029 17	0.000 3	0.626 13	0.100 11	0.000 9	0.360 13	0.179 15	0.507 13	0.137 15	0.006 8	0.300 12	0.000 1	0.000 3	0.172 8	0.364 15	0.512 9	0.000 1	0.056 14	0.000 3	0.865 13	0.093 4	0.634 17	0.000 7	0.071 13	0.396 14	0.296 16	0.876 9	0.000 1	0.000 2	0.373 13	0.436 16	0.063 9	0.749 2	0.877 10	0.721 12	0.131 3	0.124 14	0.804 15	0.000 1	0.000 11	0.515 12	0.010 6	0.452 10	0.252 9	0.578 14	0.417 8	0.179 17	0.484 10	0.171 7	0.337 14	0.606 12	0.000 7	0.115 10	0.937 14	0.142 9	0.000 1	0.008 11	0.000 15	0.157 16	0.484 14	0.402 17	0.501 15	0.339 9	0.553 7	0.529 3	0.478 12	0.000 4	0.000 1	0.404 10	0.001 10	0.022 13	0.077 9	0.000 4	0.894 12	0.219 7	0.628 7	0.093 15	0.305 14	0.886 1	0.233 9	0.000 1	0.603 9	0.112 6	0.023 9	0.000 4	0.000 1	0.000 11	0.741 6	0.664 8	0.097 15	0.253 14	0.782 10	0.264 11	0.523 11	0.154 2	0.707 16	0.000 6	0.411 8	0.000 1	0.000 8	0.000 3	0.332 16	0.000 5	0.000 9	0.000 1	0.602 7	0.595 10	0.185 13	0.656 16	0.159 6	0.000 1	0.355 11	0.424 15	0.154 15	0.729 15	0.516 10	0.220 10	0.620 3	0.084 13	0.000 1	0.707 14	0.651 13	0.173 5	0.014 9	0.381 17	0.582 14	0.000 1	0.619 3	0.049 12	0.000 7	0.000 1	0.702 4	0.000 2	0.000 1	0.302 16	0.489 15	0.317 13	0.334 7	0.392 7	0.922 14	0.254 13	0.533 13	0.394 13	0.129 14	0.613 15	0.000 1	0.000 12	0.820 5	0.649 11	0.749 13	0.000 1	0.782 14	0.282 6	0.863 6	0.000 1	0.288 15	0.006 12	0.220 11	0.633 14	0.542 3
: 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	0.476 11	0.077 10	0.218 8	0.000 3	0.000 7	0.000 1	0.547 2	0.295 11	0.540 5	0.746 10	0.745 15	0.058 16	0.112 16	0.005 1	0.658 11	0.077 15	0.000 9	0.322 14	0.178 16	0.512 11	0.190 13	0.199 2	0.277 15	0.000 1	0.000 3	0.173 7	0.399 12	0.000 11	0.000 1	0.039 16	0.000 3	0.858 14	0.085 7	0.676 11	0.002 5	0.103 6	0.498 8	0.323 14	0.703 12	0.000 1	0.000 2	0.296 15	0.549 12	0.216 1	0.702 6	0.768 14	0.718 14	0.028 10	0.092 16	0.786 16	0.000 1	0.000 11	0.453 16	0.022 5	0.251 17	0.252 9	0.572 15	0.348 14	0.321 11	0.514 7	0.063 15	0.279 16	0.552 15	0.000 7	0.019 16	0.932 15	0.132 15	0.000 1	0.000 12	0.000 15	0.156 17	0.457 15	0.623 12	0.518 14	0.265 16	0.358 11	0.381 15	0.395 14	0.000 4	0.000 1	0.127 17	0.012 8	0.051 1	0.000 12	0.000 4	0.886 13	0.014 14	0.437 17	0.179 8	0.244 15	0.826 15	0.000 12	0.000 1	0.599 10	0.136 1	0.085 3	0.000 4	0.000 1	0.000 11	0.565 13	0.612 13	0.143 5	0.207 15	0.566 14	0.232 15	0.446 15	0.127 4	0.708 15	0.000 6	0.384 9	0.000 1	0.000 8	0.000 3	0.402 14	0.000 5	0.059 7	0.000 1	0.525 15	0.566 11	0.229 12	0.659 15	0.000 10	0.000 1	0.265 15	0.446 14	0.147 16	0.720 17	0.597 8	0.066 14	0.000 11	0.187 9	0.000 1	0.726 13	0.467 17	0.134 12	0.000 10	0.413 15	0.629 12	0.000 1	0.363 16	0.055 10	0.022 3	0.000 1	0.626 11	0.000 2	0.000 1	0.323 15	0.479 17	0.154 16	0.117 15	0.028 16	0.901 15	0.243 15	0.415 16	0.295 17	0.143 6	0.610 16	0.000 1	0.000 12	0.777 12	0.397 17	0.324 16	0.000 1	0.778 15	0.179 8	0.702 16	0.000 1	0.274 16	0.404 4	0.233 10	0.622 15	0.398 7
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	0.381 16	0.084 8	0.134 15	0.000 3	0.000 7	0.000 1	0.386 7	0.141 17	0.279 17	0.737 12	0.703 16	0.014 17	0.164 15	0.000 3	0.663 10	0.092 14	0.000 9	0.224 15	0.291 11	0.531 8	0.056 17	0.000 9	0.242 16	0.000 1	0.000 3	0.013 15	0.331 16	0.000 11	0.000 1	0.035 17	0.001 2	0.858 14	0.059 14	0.650 16	0.000 7	0.056 14	0.353 15	0.299 15	0.670 13	0.000 1	0.000 2	0.284 16	0.484 15	0.071 8	0.594 15	0.720 16	0.710 15	0.027 11	0.068 17	0.813 14	0.000 1	0.005 10	0.492 14	0.164 1	0.274 16	0.111 16	0.571 16	0.307 17	0.293 13	0.307 17	0.150 9	0.163 17	0.531 16	0.002 6	0.545 5	0.932 15	0.093 17	0.000 1	0.000 12	0.002 14	0.159 15	0.368 17	0.581 15	0.440 17	0.228 17	0.406 9	0.282 17	0.294 16	0.000 4	0.000 1	0.189 16	0.060 2	0.036 5	0.000 12	0.000 4	0.897 11	0.000 17	0.525 14	0.025 17	0.205 17	0.771 17	0.000 12	0.000 1	0.593 11	0.108 9	0.044 6	0.000 4	0.000 1	0.000 11	0.282 17	0.589 14	0.094 16	0.169 16	0.466 17	0.227 16	0.419 17	0.125 5	0.757 14	0.002 4	0.334 16	0.000 1	0.000 8	0.000 3	0.357 15	0.000 5	0.000 9	0.000 1	0.582 10	0.513 14	0.337 10	0.612 17	0.000 10	0.000 1	0.250 16	0.352 17	0.136 17	0.724 16	0.655 4	0.280 8	0.000 11	0.046 16	0.000 1	0.606 17	0.559 15	0.159 7	0.102 3	0.445 10	0.655 9	0.000 1	0.310 17	0.117 6	0.000 7	0.000 1	0.581 15	0.026 1	0.000 1	0.265 17	0.483 16	0.084 17	0.097 17	0.044 15	0.865 17	0.142 17	0.588 11	0.351 15	0.272 2	0.596 17	0.000 1	0.003 10	0.622 16	0.720 10	0.096 17	0.000 1	0.771 16	0.016 15	0.772 15	0.000 1	0.302 14	0.194 9	0.214 12	0.621 16	0.197 16
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	0.418 15	0.059 14	0.186 10	0.000 3	0.000 7	0.000 1	0.335 10	0.250 13	0.316 16	0.766 7	0.697 17	0.142 13	0.170 14	0.003 2	0.553 14	0.112 9	0.097 1	0.201 16	0.186 14	0.476 15	0.081 16	0.000 9	0.216 17	0.000 1	0.000 3	0.001 17	0.314 17	0.000 11	0.000 1	0.055 15	0.000 3	0.832 16	0.094 3	0.659 15	0.002 5	0.076 9	0.310 16	0.293 17	0.664 14	0.000 1	0.000 2	0.175 17	0.634 6	0.130 2	0.552 17	0.686 17	0.700 17	0.076 7	0.110 15	0.770 17	0.000 1	0.000 11	0.430 17	0.000 7	0.319 15	0.166 15	0.542 17	0.327 16	0.205 16	0.332 14	0.052 16	0.375 13	0.444 17	0.000 7	0.012 17	0.930 17	0.203 3	0.000 1	0.000 12	0.046 12	0.175 14	0.413 16	0.592 14	0.471 16	0.299 15	0.152 16	0.340 16	0.247 17	0.000 4	0.000 1	0.225 15	0.058 3	0.037 4	0.000 12	0.207 2	0.862 15	0.014 14	0.548 13	0.033 16	0.233 16	0.816 16	0.000 12	0.000 1	0.542 15	0.123 5	0.121 1	0.019 2	0.000 1	0.000 11	0.463 16	0.454 17	0.045 17	0.128 17	0.557 15	0.235 14	0.441 16	0.063 11	0.484 17	0.000 6	0.308 17	0.000 1	0.000 8	0.000 3	0.318 17	0.000 5	0.000 9	0.000 1	0.545 14	0.543 12	0.164 14	0.734 9	0.000 10	0.000 1	0.215 17	0.371 16	0.198 14	0.743 14	0.205 15	0.062 15	0.000 11	0.079 14	0.000 1	0.683 16	0.547 16	0.142 9	0.000 10	0.441 11	0.579 15	0.000 1	0.464 14	0.098 9	0.041 1	0.000 1	0.590 14	0.000 2	0.000 1	0.373 13	0.494 14	0.174 15	0.105 16	0.001 17	0.895 16	0.222 16	0.537 12	0.307 16	0.180 5	0.625 14	0.000 1	0.000 12	0.591 17	0.609 14	0.398 15	0.000 1	0.766 17	0.014 16	0.638 17	0.000 1	0.377 13	0.004 13	0.206 13	0.609 17	0.465 5
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
ODIN - Sem200	0.368 4	0.562 4	0.297 4	0.207 4	0.380 17	0.196 1	0.828 2	0.000 3	0.321 2	0.000 1	0.400 5	0.775 1	0.460 13	0.501 17	0.769 12	0.065 15	0.870 3	0.000 3	0.913 1	0.213 3	0.000 9	0.000 17	0.389 2	0.554 4	0.312 3	0.000 9	0.591 1	0.000 1	0.000 3	0.491 1	0.487 3	0.894 2	0.000 1	0.378 2	0.303 1	0.796 17	0.088 6	0.669 13	0.081 1	0.216 1	0.256 17	0.334 13	0.898 7	0.000 1	0.000 2	0.370 14	0.599 10	0.000 10	0.581 16	0.988 2	0.749 8	0.090 6	0.242 5	0.921 4	0.000 1	0.202 5	0.609 2	0.000 7	0.655 1	0.214 13	0.654 9	0.346 15	0.408 7	0.485 9	0.169 8	0.631 2	0.704 6	0.000 7	0.814 1	0.940 10	0.127 16	0.000 1	0.000 12	0.462 4	0.227 6	0.641 4	0.885 3	0.657 5	0.434 3	0.000 17	0.550 2	0.393 15	0.000 4	0.000 1	0.590 4	0.000 11	0.048 2	0.077 9	0.000 4	0.784 16	0.131 10	0.557 10	0.316 2	0.359 8	0.833 14	0.373 2	0.000 1	0.661 4	0.108 9	0.001 12	0.000 4	0.000 1	0.301 3	0.612 11	0.565 15	0.129 10	0.482 8	0.468 16	0.274 5	0.561 8	0.376 1	0.912 2	0.181 1	0.440 6	0.000 1	0.166 4	0.000 3	0.641 5	0.000 5	0.426 2	0.000 1	0.642 5	0.626 7	0.259 11	0.787 8	0.429 4	0.000 1	0.589 1	0.523 8	0.246 11	0.857 6	0.000 17	0.228 9	0.000 11	0.265 4	0.000 1	0.752 6	0.832 1	0.090 16	0.157 1	0.791 1	0.578 16	0.000 1	0.373 15	0.539 1	0.000 7	0.000 1	0.685 5	0.000 2	0.000 1	0.632 8	0.575 3	0.663 1	0.152 11	0.358 9	0.926 13	0.397 3	0.454 15	0.610 4	0.119 15	0.685 7	0.000 1	0.000 12	0.803 8	0.740 9	0.441 14	0.000 1	0.800 10	0.000 17	0.871 3	0.000 1	0.220 17	0.487 1	0.862 1	0.682 6	0.054 17
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

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

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

Mask3D Scannet200	0.388 1	0.542 1	0.357 2	0.237 2	0.610 1	0.091 2	0.125 6	0.000 1	0.000 2	0.000 1	0.065 3	0.668 1	0.451 1	1.000 1	0.955 1	0.640 1	0.500 2	0.039 1	0.125 3	0.063 3	0.409 1	0.311 2	0.291 1	0.609 4	0.266 2	0.000 1	0.163 2	0.000 1	0.008 1	0.044 3	0.496 2	1.000 1	0.000 1	0.018 3	0.000 2	0.756 1	0.573 1	0.808 2	0.000 2	0.010 2	0.042 4	0.130 4	0.552 2	0.042 1	0.000 1	1.000 1	0.725 4	0.750 1	0.883 1	1.000 1	0.832 4	0.024 3	0.107 2	0.614 3	0.226 1	0.250 1	0.628 2	0.792 1	0.677 3	0.400 1	0.741 2	0.278 2	0.511 2	0.077 6	0.111 2	0.313 3	0.715 2	0.302 1	0.017 4	0.200 2	0.000 1	0.188 1	0.000 2	0.178 3	0.736 2	1.000 1	0.615 1	0.514 1	0.409 2	0.380 6	0.600 1	0.000 1	0.000 1	0.400 2	0.013 2	0.254 1	0.381 1	0.000 1	0.123 5	0.400 1	0.839 2	0.258 2	0.463 1	0.926 1	0.265 2	0.000 1	0.857 2	0.099 1	0.021 2	0.500 1	0.027 1	0.028 2	1.000 1	0.502 6	0.016 2	0.076 5	0.500 1	0.612 1	0.578 1	0.005 3	0.597 3	0.194 2	0.497 1	0.000 1	0.500 1	0.000 2	0.323 5	0.000 1	1.000 1	0.000 1	0.748 1	0.708 2	0.050 5	0.890 2	1.000 1	0.008 2	0.151 4	0.301 2	1.000 1	1.000 1	0.792 3	0.945 1	1.000 1	0.511 1	0.004 2	0.753 1	0.776 3	0.287 2	0.020 2	0.003 5	0.974 3	0.033 1	0.412 6	0.000 2	0.000 2	0.000 2	0.667 2	0.000 1	0.000 1	0.491 2	0.676 2	0.352 2	0.335 1	0.060 3	0.822 6	0.527 3	1.000 1	0.517 2	0.606 1	0.853 2	0.000 1	0.004 1	0.806 1	1.000 1	0.727 1	0.000 1	0.042 2	0.739 2	0.000 1	0.399 3	0.391 1	0.504 2	0.591 1	0.571 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
TD3D Scannet200	0.320 3	0.501 3	0.264 3	0.164 3	0.506 3	0.062 3	0.500 1	0.000 1	0.000 2	0.000 1	0.208 1	0.431 3	0.252 4	1.000 1	0.733 4	0.587 2	0.000 3	0.008 2	0.000 4	0.106 1	0.000 2	0.356 1	0.123 5	0.686 1	0.101 3	0.000 1	0.152 3	0.000 1	0.000 2	0.226 2	0.280 4	0.000 3	0.000 1	0.250 2	0.000 2	0.619 2	0.061 4	0.841 1	0.000 2	0.000 3	0.167 2	0.194 2	0.333 3	0.000 2	0.000 1	0.667 2	0.820 1	0.250 3	0.790 4	1.000 1	0.879 2	0.077 1	0.094 4	0.708 1	0.217 2	0.049 3	0.634 1	0.792 1	0.331 5	0.033 6	0.716 3	0.159 3	0.396 3	0.331 5	0.099 3	0.415 1	0.842 1	0.000 2	0.458 2	0.542 1	0.000 1	0.101 2	0.000 2	0.218 2	0.513 3	0.500 3	0.458 3	0.104 3	0.516 1	0.456 1	0.268 5	0.000 1	0.000 1	0.400 2	0.022 1	0.233 2	0.143 3	0.000 1	0.677 1	0.400 1	0.504 6	0.095 4	0.083 6	0.890 2	0.061 3	0.000 1	0.906 1	0.076 2	0.231 1	0.125 3	0.000 2	0.003 3	0.792 4	0.881 1	0.000 3	0.098 4	0.125 5	0.498 5	0.459 3	0.063 1	0.715 2	0.000 3	0.241 4	0.000 1	0.396 2	0.063 1	0.605 2	0.000 1	0.000 3	0.000 1	0.448 6	0.629 4	0.202 3	0.967 1	0.250 3	0.038 1	0.192 2	0.185 3	0.083 4	1.000 1	1.000 1	0.857 2	0.000 2	0.470 2	0.012 1	0.565 4	0.798 2	0.621 1	0.111 1	0.500 1	1.000 1	0.017 2	0.509 2	0.000 2	0.008 1	1.000 1	0.525 3	0.000 1	0.000 1	0.332 4	0.679 1	0.264 3	0.333 2	0.267 1	1.000 1	0.549 2	0.299 6	0.387 3	0.328 3	0.744 5	0.000 1	0.000 2	0.435 6	1.000 1	0.283 5	0.000 1	0.196 1	0.817 1	0.000 1	0.472 1	0.222 4	0.123 5	0.560 2	0.156 3
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
ODIN - Ins200	0.381 2	0.507 2	0.375 1	0.237 1	0.484 4	0.108 1	0.500 1	0.000 1	0.125 1	0.000 1	0.058 4	0.647 2	0.385 2	0.667 4	0.853 2	0.542 3	1.000 1	0.000 3	1.000 1	0.093 2	0.000 2	0.028 5	0.274 2	0.682 2	0.550 1	0.000 1	0.269 1	0.000 1	0.000 2	0.714 1	0.566 1	1.000 1	0.000 1	0.500 1	0.125 1	0.585 3	0.066 3	0.653 6	0.083 1	0.049 1	0.264 1	0.227 1	0.667 1	0.000 2	0.000 1	0.278 6	0.723 5	0.250 3	0.786 5	1.000 1	0.744 6	0.039 2	0.209 1	0.494 5	0.000 3	0.250 1	0.446 3	0.500 3	0.750 1	0.200 3	0.780 1	0.333 1	0.602 1	0.469 3	0.163 1	0.406 2	0.530 4	0.000 2	0.668 1	0.200 2	0.000 1	0.000 3	0.500 1	0.313 1	0.769 1	1.000 1	0.511 2	0.196 2	0.286 3	0.393 5	0.337 2	0.000 1	0.000 1	0.600 1	0.000 3	0.174 3	0.226 2	0.000 1	0.579 2	0.200 3	0.887 1	0.750 1	0.428 2	0.782 3	0.438 1	0.000 1	0.795 3	0.063 3	0.003 3	0.500 1	0.000 2	0.333 1	1.000 1	0.742 2	0.083 1	0.585 1	0.417 4	0.448 6	0.496 2	0.055 2	0.734 1	0.472 1	0.174 5	0.000 1	0.250 3	0.000 2	0.688 1	0.000 1	1.000 1	0.000 1	0.631 3	0.667 3	0.275 1	0.694 6	1.000 1	0.000 3	0.328 1	0.422 1	0.000 5	1.000 1	0.500 4	0.638 3	0.000 2	0.391 3	0.000 3	0.582 3	0.800 1	0.208 5	0.000 3	0.246 2	0.667 5	0.000 3	0.638 1	0.167 1	0.000 2	0.000 2	0.778 1	0.000 1	0.000 1	0.563 1	0.614 3	0.841 1	0.333 2	0.250 2	0.938 5	0.569 1	0.500 4	0.695 1	0.264 4	0.863 1	0.000 1	0.000 2	0.550 5	1.000 1	0.668 2	0.000 1	0.000 3	0.667 3	0.000 1	0.333 4	0.333 2	0.665 1	0.434 3	0.264 2

CSC-Pretrain Inst.	0.209 5	0.361 6	0.157 5	0.085 5	0.506 2	0.007 4	0.500 1	0.000 1	0.000 2	0.000 1	0.000 6	0.093 6	0.221 5	0.667 4	0.524 6	0.400 4	0.000 3	0.000 3	0.000 4	0.004 5	0.000 2	0.000 6	0.109 6	0.589 5	0.000 5	0.000 1	0.059 6	0.000 1	0.000 2	0.000 4	0.322 3	0.000 3	0.000 1	0.000 4	0.000 2	0.405 4	0.055 5	0.700 5	0.000 2	0.000 3	0.028 5	0.091 6	0.083 4	0.000 2	0.000 1	0.667 2	0.768 2	0.000 5	0.807 3	1.000 1	0.776 5	0.000 4	0.000 6	0.340 6	0.000 3	0.000 4	0.103 6	0.000 4	0.750 1	0.200 3	0.634 6	0.053 6	0.246 4	0.677 2	0.006 6	0.198 4	0.432 5	0.000 2	0.000 5	0.050 5	0.000 1	0.000 3	0.000 2	0.111 6	0.356 5	0.500 3	0.188 6	0.000 5	0.220 5	0.448 2	0.050 6	0.000 1	0.000 1	0.000 4	0.000 3	0.032 6	0.000 4	0.000 1	0.396 3	0.000 5	0.573 5	0.000 6	0.228 4	0.747 5	0.000 4	0.000 1	0.573 6	0.021 6	0.000 4	0.000 4	0.000 2	0.000 4	0.500 5	0.573 4	0.000 3	0.000 6	0.125 5	0.592 3	0.364 6	0.000 4	0.450 6	0.000 3	0.364 2	0.000 1	0.000 4	0.000 2	0.340 4	0.000 1	0.000 3	0.000 1	0.610 4	0.833 1	0.221 2	0.702 5	0.000 4	0.000 3	0.135 6	0.094 5	0.125 2	0.571 5	0.500 4	0.143 6	0.000 2	0.125 4	0.000 3	0.618 2	0.667 5	0.115 6	0.000 3	0.125 3	1.000 1	0.000 3	0.500 3	0.000 2	0.000 2	0.000 2	0.502 5	0.000 1	0.000 1	0.312 5	0.248 6	0.050 5	0.000 6	0.000 4	0.997 3	0.420 4	0.500 4	0.149 6	0.451 2	0.748 3	0.000 1	0.000 2	0.636 3	0.667 6	0.600 3	0.000 1	0.000 3	0.278 6	0.000 1	0.333 4	0.000 6	0.294 3	0.381 6	0.110 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.	0.246 4	0.413 4	0.170 4	0.130 4	0.455 6	0.003 6	0.500 1	0.000 1	0.000 2	0.000 1	0.017 5	0.333 5	0.111 6	1.000 1	0.681 5	0.400 4	0.000 3	0.000 3	1.000 1	0.003 6	0.000 2	0.167 3	0.190 3	0.637 3	0.067 4	0.000 1	0.081 4	0.000 1	0.000 2	0.000 4	0.264 5	0.000 3	0.000 1	0.000 4	0.000 2	0.387 5	0.031 6	0.754 3	0.000 2	0.000 3	0.151 3	0.135 3	0.056 5	0.000 2	0.000 1	0.582 4	0.589 6	0.500 2	0.815 2	1.000 1	0.903 1	0.000 4	0.097 3	0.588 4	0.000 3	0.000 4	0.234 4	0.000 4	0.500 4	0.400 1	0.682 5	0.156 4	0.159 5	0.750 1	0.046 4	0.125 5	0.660 3	0.000 2	0.200 3	0.000 6	0.000 1	0.000 3	0.000 2	0.164 4	0.402 4	0.500 3	0.373 4	0.025 4	0.143 6	0.426 3	0.317 3	0.000 1	0.000 1	0.000 4	0.000 3	0.063 4	0.000 4	0.000 1	0.000 6	0.000 5	0.575 4	0.250 3	0.241 3	0.772 4	0.000 4	0.000 1	0.653 5	0.034 4	0.000 4	0.000 4	0.000 2	0.000 4	1.000 1	0.561 5	0.000 3	0.100 3	0.500 1	0.541 4	0.452 4	0.000 4	0.581 4	0.000 3	0.364 2	0.000 1	0.000 4	0.000 2	0.571 3	0.000 1	0.000 3	0.000 1	0.568 5	0.511 5	0.167 4	0.857 3	0.000 4	0.000 3	0.164 3	0.112 4	0.000 5	0.530 6	1.000 1	0.286 4	0.000 2	0.125 4	0.000 3	0.464 6	0.706 4	0.208 4	0.000 3	0.125 3	0.744 4	0.000 3	0.500 3	0.000 2	0.000 2	0.000 2	0.511 4	0.000 1	0.000 1	0.344 3	0.541 4	0.068 4	0.333 2	0.000 4	1.000 1	0.196 5	0.533 3	0.318 4	0.000 5	0.748 4	0.000 1	0.000 2	0.690 2	1.000 1	0.400 4	0.000 1	0.000 3	0.667 3	0.000 1	0.333 4	0.333 2	0.270 4	0.399 4	0.083 5
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.	0.203 6	0.369 5	0.134 6	0.078 6	0.479 5	0.003 5	0.500 1	0.000 1	0.000 2	0.000 1	0.100 2	0.371 4	0.300 3	0.667 4	0.746 3	0.400 4	0.000 3	0.000 3	0.000 4	0.031 4	0.000 2	0.074 4	0.165 4	0.413 6	0.000 5	0.000 1	0.070 5	0.000 1	0.000 2	0.000 4	0.221 6	0.000 3	0.000 1	0.000 4	0.000 2	0.372 6	0.070 2	0.706 4	0.000 2	0.000 3	0.000 6	0.123 5	0.033 6	0.000 2	0.000 1	0.422 5	0.732 3	0.000 5	0.778 6	1.000 1	0.845 3	0.000 4	0.090 5	0.636 2	0.000 3	0.000 4	0.158 5	0.000 4	0.250 6	0.050 5	0.693 4	0.123 5	0.051 6	0.385 4	0.009 5	0.118 6	0.406 6	0.000 2	0.000 5	0.200 2	0.000 1	0.000 3	0.000 2	0.133 5	0.307 6	0.500 3	0.251 5	0.000 5	0.281 4	0.402 4	0.317 3	0.000 1	0.000 1	0.000 4	0.000 3	0.060 5	0.000 4	0.000 1	0.396 3	0.200 3	0.669 3	0.021 5	0.218 5	0.720 6	0.000 4	0.000 1	0.696 4	0.025 5	0.000 4	0.000 4	0.000 2	0.000 4	0.125 6	0.596 3	0.000 3	0.191 2	0.500 1	0.595 2	0.369 5	0.000 4	0.500 5	0.000 3	0.143 6	0.000 1	0.000 4	0.000 2	0.226 6	0.000 1	0.000 3	0.000 1	0.701 2	0.511 5	0.000 6	0.851 4	0.000 4	0.000 3	0.150 5	0.052 6	0.100 3	0.981 4	0.500 4	0.286 4	0.000 2	0.000 6	0.000 3	0.545 5	0.522 6	0.250 3	0.000 3	0.000 6	0.522 6	0.000 3	0.500 3	0.000 2	0.000 2	0.000 2	0.282 6	0.000 1	0.000 1	0.178 6	0.382 5	0.018 6	0.056 5	0.000 4	0.997 3	0.107 6	0.677 2	0.313 5	0.000 5	0.726 6	0.000 1	0.000 2	0.583 4	0.903 5	0.200 6	0.000 1	0.000 3	0.333 5	0.000 1	0.442 2	0.083 5	0.109 6	0.387 5	0.000 6
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019

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

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. CVPR 2025
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 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)
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.
Mix3D	0.781 5	0.964 2	0.855 2	0.843 20	0.781 8	0.858 13	0.575 8	0.831 39	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)
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

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.
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

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
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 73	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
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 31	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

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 30	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

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 33	0.711 4	0.640 31	0.958 23	0.592 11	0.217 79	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
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

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 49	0.760 21	0.942 3	0.734 32	0.766 50	0.701 13	0.884 61	0.874 22	0.736 20

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

ConDaFormer	0.755 17	0.927 6	0.822 10	0.836 26	0.801 1	0.849 25	0.516 35	0.864 27	0.651 30	0.680 13	0.958 23	0.584 19	0.282 46	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
LSK3DNet	0.755 17	0.899 16	0.823 8	0.843 20	0.764 16	0.838 38	0.584 2	0.845 34	0.717 2	0.638 33	0.956 30	0.580 21	0.229 72	0.640 49	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
PNE	0.755 17	0.786 45	0.835 5	0.834 28	0.758 19	0.849 25	0.570 10	0.836 38	0.648 32	0.668 19	0.978 6	0.581 20	0.367 7	0.683 40	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.
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
DMF-Net	0.752 20	0.906 14	0.793 38	0.802 47	0.689 46	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
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 52	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.
BPNet	0.749 22	0.909 12	0.818 16	0.811 39	0.752 24	0.839 37	0.485 53	0.842 35	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)
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

StratifiedFormer	0.747 25	0.901 15	0.803 29	0.845 18	0.757 21	0.846 30	0.512 37	0.825 42	0.696 11	0.645 25	0.956 30	0.576 22	0.262 63	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
Virtual MVFusion	0.746 26	0.771 55	0.819 14	0.848 15	0.702 43	0.865 10	0.397 91	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
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)
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

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
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 54	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
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
SAT	0.742 32	0.860 24	0.765 55	0.819 34	0.769 14	0.848 27	0.533 27	0.829 40	0.663 24	0.631 36	0.955 34	0.586 17	0.274 52	0.753 27	0.896 17	0.729 33	0.760 56	0.666 33	0.921 33	0.855 37	0.733 22

LRPNet	0.742 32	0.816 38	0.806 27	0.807 43	0.752 24	0.828 50	0.575 8	0.839 37	0.699 9	0.637 34	0.954 40	0.520 46	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

LargeKernel3D	0.739 34	0.909 12	0.820 12	0.806 45	0.740 32	0.852 22	0.545 21	0.826 41	0.594 57	0.643 27	0.955 34	0.541 35	0.263 62	0.723 38	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
RPN	0.736 35	0.776 51	0.790 39	0.851 11	0.754 23	0.854 18	0.491 52	0.866 25	0.596 56	0.686 9	0.955 34	0.536 37	0.342 16	0.624 56	0.869 26	0.787 11	0.802 20	0.628 45	0.927 27	0.875 20	0.704 39

MinkowskiNet	0.736 35	0.859 25	0.818 16	0.832 30	0.709 41	0.840 35	0.521 33	0.853 29	0.660 26	0.643 27	0.951 51	0.544 34	0.286 44	0.731 36	0.893 18	0.675 61	0.772 45	0.683 21	0.874 73	0.852 41	0.727 26
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 37	0.890 17	0.837 4	0.864 4	0.726 37	0.873 5	0.530 30	0.824 43	0.489 93	0.647 24	0.978 6	0.609 5	0.336 19	0.624 56	0.733 64	0.758 23	0.776 43	0.570 71	0.949 9	0.877 16	0.728 24

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 66	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 67	0.938 18	0.871 25	0.719 32

PointTransformer++	0.725 39	0.727 76	0.811 24	0.819 34	0.765 15	0.841 34	0.502 41	0.814 48	0.621 42	0.623 39	0.955 34	0.556 31	0.284 45	0.620 58	0.866 27	0.781 14	0.757 60	0.648 36	0.932 25	0.862 30	0.709 37

SparseConvNet	0.725 39	0.647 96	0.821 11	0.846 17	0.721 38	0.869 6	0.533 27	0.754 64	0.603 52	0.614 42	0.955 34	0.572 24	0.325 25	0.710 39	0.870 25	0.724 37	0.823 4	0.628 45	0.934 22	0.865 29	0.683 45

MatchingNet	0.724 41	0.812 40	0.812 22	0.810 40	0.735 34	0.834 43	0.495 49	0.860 28	0.572 66	0.602 50	0.954 40	0.512 48	0.280 48	0.757 24	0.845 41	0.725 36	0.780 40	0.606 55	0.937 19	0.851 42	0.700 41

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 35	0.609 48	0.608 46	0.953 44	0.534 39	0.293 39	0.616 59	0.864 28	0.719 41	0.793 33	0.640 40	0.933 23	0.845 47	0.663 51

PointMetaBase	0.714 43	0.835 31	0.785 43	0.821 32	0.684 48	0.846 30	0.531 29	0.865 26	0.614 43	0.596 54	0.953 44	0.500 51	0.246 68	0.674 41	0.888 20	0.692 53	0.764 52	0.624 47	0.849 88	0.844 48	0.675 47

contrastBoundary	0.705 44	0.769 58	0.775 49	0.809 41	0.687 47	0.820 59	0.439 79	0.812 49	0.661 25	0.591 56	0.945 70	0.515 47	0.171 98	0.633 53	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
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 52	0.463 100	0.634 35	0.968 14	0.491 54	0.271 56	0.726 37	0.910 9	0.706 47	0.815 9	0.551 83	0.878 67	0.833 49	0.570 83

RFCR	0.702 46	0.889 18	0.745 70	0.813 37	0.672 51	0.818 63	0.493 50	0.815 47	0.623 40	0.610 44	0.947 64	0.470 63	0.249 67	0.594 63	0.848 40	0.705 48	0.779 41	0.646 37	0.892 56	0.823 55	0.611 66
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click	0.701 47	0.825 35	0.796 34	0.723 68	0.716 39	0.832 45	0.433 81	0.816 45	0.634 37	0.609 45	0.969 12	0.418 89	0.344 14	0.559 75	0.833 44	0.715 43	0.808 18	0.560 77	0.902 48	0.847 44	0.680 46

JSENet	0.699 48	0.881 20	0.762 56	0.821 32	0.667 52	0.800 76	0.522 32	0.792 55	0.613 44	0.607 47	0.935 90	0.492 53	0.205 85	0.576 68	0.853 37	0.691 55	0.758 58	0.652 35	0.872 76	0.828 52	0.649 55
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click	0.693 49	0.743 67	0.794 36	0.655 91	0.684 48	0.822 56	0.497 47	0.719 74	0.622 41	0.617 41	0.977 10	0.447 76	0.339 17	0.750 30	0.664 81	0.703 50	0.790 36	0.596 60	0.946 12	0.855 37	0.647 56
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-II	0.692 50	0.732 72	0.772 50	0.786 53	0.677 50	0.866 9	0.517 34	0.848 31	0.509 86	0.626 37	0.952 49	0.536 37	0.225 75	0.545 81	0.704 71	0.689 58	0.810 16	0.564 76	0.903 47	0.854 39	0.729 23
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 51	0.884 19	0.754 62	0.795 50	0.647 59	0.818 63	0.422 83	0.802 52	0.612 45	0.604 48	0.945 70	0.462 66	0.189 93	0.563 74	0.853 37	0.726 35	0.765 51	0.632 43	0.904 45	0.821 58	0.606 70
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 52	0.704 85	0.741 74	0.754 65	0.656 54	0.829 48	0.501 42	0.741 69	0.609 48	0.548 64	0.950 55	0.522 45	0.371 5	0.633 53	0.756 59	0.715 43	0.771 47	0.623 48	0.861 84	0.814 61	0.658 52
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 53	0.866 22	0.748 67	0.819 34	0.645 61	0.794 79	0.450 69	0.802 52	0.587 59	0.604 48	0.945 70	0.464 65	0.201 88	0.554 77	0.840 42	0.723 38	0.732 71	0.602 58	0.907 43	0.822 57	0.603 73

VACNN++	0.684 54	0.728 75	0.757 61	0.776 58	0.690 44	0.804 74	0.464 62	0.816 45	0.577 65	0.587 57	0.945 70	0.508 50	0.276 51	0.671 42	0.710 69	0.663 66	0.750 64	0.589 65	0.881 64	0.832 51	0.653 54

KP-FCNN	0.684 54	0.847 28	0.758 60	0.784 55	0.647 59	0.814 66	0.473 56	0.772 58	0.605 50	0.594 55	0.935 90	0.450 74	0.181 96	0.587 64	0.805 52	0.690 56	0.785 39	0.614 51	0.882 63	0.819 59	0.632 62
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet	0.684 54	0.712 84	0.784 44	0.782 57	0.658 53	0.835 42	0.499 46	0.823 44	0.641 34	0.597 53	0.950 55	0.487 56	0.281 47	0.575 69	0.619 85	0.647 74	0.764 52	0.620 50	0.871 79	0.846 46	0.688 44

PointContrast_LA_SEM	0.683 57	0.757 62	0.784 44	0.786 53	0.639 63	0.824 54	0.408 86	0.775 57	0.604 51	0.541 66	0.934 94	0.532 40	0.269 58	0.552 78	0.777 57	0.645 77	0.793 33	0.640 40	0.913 42	0.824 54	0.671 48

Superpoint Network	0.683 57	0.851 27	0.728 78	0.800 49	0.653 56	0.806 72	0.468 59	0.804 50	0.572 66	0.602 50	0.946 67	0.453 73	0.239 71	0.519 86	0.822 46	0.689 58	0.762 55	0.595 62	0.895 54	0.827 53	0.630 63

VI-PointConv	0.676 59	0.770 57	0.754 62	0.783 56	0.621 67	0.814 66	0.552 18	0.758 62	0.571 69	0.557 62	0.954 40	0.529 41	0.268 60	0.530 84	0.682 75	0.675 61	0.719 74	0.603 57	0.888 59	0.833 49	0.665 50
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D	0.673 60	0.789 44	0.748 67	0.763 63	0.635 65	0.814 66	0.407 88	0.747 66	0.581 63	0.573 59	0.950 55	0.484 57	0.271 56	0.607 60	0.754 60	0.649 71	0.774 44	0.596 60	0.883 62	0.823 55	0.606 70

SALANet	0.670 61	0.816 38	0.770 53	0.768 60	0.652 57	0.807 71	0.451 66	0.747 66	0.659 28	0.545 65	0.924 100	0.473 62	0.149 108	0.571 71	0.811 51	0.635 81	0.746 65	0.623 48	0.892 56	0.794 75	0.570 83

O3DSeg	0.668 62	0.822 36	0.771 52	0.496 112	0.651 58	0.833 44	0.541 23	0.761 61	0.555 75	0.611 43	0.966 15	0.489 55	0.370 6	0.388 105	0.580 88	0.776 17	0.751 62	0.570 71	0.956 7	0.817 60	0.646 57

PointConv	0.666 63	0.781 48	0.759 58	0.699 76	0.644 62	0.822 56	0.475 55	0.779 56	0.564 72	0.504 83	0.953 44	0.428 83	0.203 87	0.586 66	0.754 60	0.661 67	0.753 61	0.588 66	0.902 48	0.813 63	0.642 58
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 63	0.703 86	0.781 46	0.751 67	0.655 55	0.830 47	0.471 57	0.769 59	0.474 96	0.537 68	0.951 51	0.475 61	0.279 49	0.635 51	0.698 74	0.675 61	0.751 62	0.553 82	0.816 95	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
PPCNN++	0.663 65	0.746 65	0.708 81	0.722 69	0.638 64	0.820 59	0.451 66	0.566 102	0.599 54	0.541 66	0.950 55	0.510 49	0.313 29	0.648 47	0.819 49	0.616 86	0.682 89	0.590 64	0.869 80	0.810 64	0.656 53
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN	0.658 66	0.558 108	0.751 65	0.655 91	0.690 44	0.722 101	0.453 65	0.867 23	0.579 64	0.576 58	0.893 112	0.523 43	0.293 39	0.733 35	0.571 90	0.692 53	0.659 96	0.606 55	0.875 70	0.804 67	0.668 49

DCM-Net	0.658 66	0.778 49	0.702 84	0.806 45	0.619 68	0.813 69	0.468 59	0.693 82	0.494 89	0.524 74	0.941 82	0.449 75	0.298 36	0.510 88	0.821 47	0.675 61	0.727 73	0.568 74	0.826 93	0.803 68	0.637 60
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 68	0.698 88	0.743 72	0.650 93	0.564 85	0.820 59	0.505 40	0.758 62	0.631 38	0.479 87	0.945 70	0.480 59	0.226 73	0.572 70	0.774 58	0.690 56	0.735 69	0.614 51	0.853 87	0.776 90	0.597 76
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 69	0.752 63	0.734 76	0.664 89	0.583 80	0.815 65	0.399 90	0.754 64	0.639 35	0.535 70	0.942 80	0.470 63	0.309 31	0.665 43	0.539 92	0.650 70	0.708 79	0.635 42	0.857 86	0.793 77	0.642 58
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 70	0.778 49	0.731 77	0.699 76	0.577 81	0.829 48	0.446 71	0.736 70	0.477 95	0.523 76	0.945 70	0.454 70	0.269 58	0.484 95	0.749 63	0.618 84	0.738 67	0.599 59	0.827 92	0.792 80	0.621 65

PointConv-SFPN	0.641 71	0.776 51	0.703 83	0.721 70	0.557 88	0.826 51	0.451 66	0.672 87	0.563 73	0.483 86	0.943 79	0.425 86	0.162 103	0.644 48	0.726 65	0.659 68	0.709 78	0.572 70	0.875 70	0.786 85	0.559 89

MVPNet	0.641 71	0.831 32	0.715 79	0.671 86	0.590 76	0.781 85	0.394 92	0.679 84	0.642 33	0.553 63	0.937 87	0.462 66	0.256 64	0.649 46	0.406 105	0.626 82	0.691 86	0.666 33	0.877 68	0.792 80	0.608 69
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 73	0.717 82	0.701 85	0.692 79	0.576 82	0.801 75	0.467 61	0.716 75	0.563 73	0.459 93	0.953 44	0.429 82	0.169 100	0.581 67	0.854 36	0.605 87	0.710 76	0.550 84	0.894 55	0.793 77	0.575 81

FPConv	0.639 74	0.785 46	0.760 57	0.713 74	0.603 71	0.798 77	0.392 94	0.534 107	0.603 52	0.524 74	0.948 62	0.457 68	0.250 66	0.538 82	0.723 67	0.598 91	0.696 84	0.614 51	0.872 76	0.799 70	0.567 86
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net	0.638 75	0.797 42	0.769 54	0.641 98	0.590 76	0.820 59	0.461 63	0.537 106	0.637 36	0.536 69	0.947 64	0.388 96	0.206 84	0.656 44	0.668 79	0.647 74	0.732 71	0.585 68	0.868 81	0.793 77	0.473 109

PointSPNet	0.637 76	0.734 71	0.692 92	0.714 73	0.576 82	0.797 78	0.446 71	0.743 68	0.598 55	0.437 98	0.942 80	0.403 92	0.150 107	0.626 55	0.800 55	0.649 71	0.697 83	0.557 80	0.846 89	0.777 89	0.563 87

SConv	0.636 77	0.830 33	0.697 88	0.752 66	0.572 84	0.780 87	0.445 73	0.716 75	0.529 79	0.530 71	0.951 51	0.446 77	0.170 99	0.507 90	0.666 80	0.636 80	0.682 89	0.541 90	0.886 60	0.799 70	0.594 77

Supervoxel-CNN	0.635 78	0.656 94	0.711 80	0.719 71	0.613 69	0.757 96	0.444 76	0.765 60	0.534 78	0.566 60	0.928 98	0.478 60	0.272 54	0.636 50	0.531 94	0.664 65	0.645 100	0.508 98	0.864 83	0.792 80	0.611 66

joint point-based	0.634 79	0.614 102	0.778 47	0.667 88	0.633 66	0.825 52	0.420 84	0.804 50	0.467 98	0.561 61	0.951 51	0.494 52	0.291 41	0.566 72	0.458 100	0.579 97	0.764 52	0.559 79	0.838 90	0.814 61	0.598 75
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL	0.632 80	0.731 73	0.688 95	0.675 83	0.591 75	0.784 84	0.444 76	0.565 103	0.610 46	0.492 84	0.949 59	0.456 69	0.254 65	0.587 64	0.706 70	0.599 90	0.665 95	0.612 54	0.868 81	0.791 83	0.579 80

PointNet2-SFPN	0.631 81	0.771 55	0.692 92	0.672 84	0.524 94	0.837 39	0.440 78	0.706 80	0.538 77	0.446 95	0.944 76	0.421 88	0.219 78	0.552 78	0.751 62	0.591 93	0.737 68	0.543 89	0.901 50	0.768 92	0.557 90

APCF-Net	0.631 81	0.742 68	0.687 97	0.672 84	0.557 88	0.792 82	0.408 86	0.665 89	0.545 76	0.508 80	0.952 49	0.428 83	0.186 94	0.634 52	0.702 72	0.620 83	0.706 80	0.555 81	0.873 74	0.798 72	0.581 79
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF	0.631 81	0.626 99	0.745 70	0.801 48	0.607 70	0.751 97	0.506 39	0.729 73	0.565 71	0.491 85	0.866 115	0.434 78	0.197 91	0.595 62	0.630 84	0.709 46	0.705 81	0.560 77	0.875 70	0.740 100	0.491 104

FusionAwareConv	0.630 84	0.604 104	0.741 74	0.766 62	0.590 76	0.747 98	0.501 42	0.734 71	0.503 88	0.527 72	0.919 104	0.454 70	0.323 26	0.550 80	0.420 104	0.678 60	0.688 87	0.544 87	0.896 53	0.795 74	0.627 64
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 85	0.800 41	0.625 107	0.719 71	0.545 91	0.806 72	0.445 73	0.597 97	0.448 103	0.519 78	0.938 86	0.481 58	0.328 24	0.489 94	0.499 99	0.657 69	0.759 57	0.592 63	0.881 64	0.797 73	0.634 61

SegGroup_sem	0.627 86	0.818 37	0.747 69	0.701 75	0.602 72	0.764 93	0.385 98	0.629 94	0.490 91	0.508 80	0.931 97	0.409 91	0.201 88	0.564 73	0.725 66	0.618 84	0.692 85	0.539 91	0.873 74	0.794 75	0.548 93
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv	0.625 87	0.830 33	0.694 90	0.757 64	0.563 86	0.772 91	0.448 70	0.647 92	0.520 82	0.509 79	0.949 59	0.431 81	0.191 92	0.496 92	0.614 86	0.647 74	0.672 93	0.535 94	0.876 69	0.783 86	0.571 82

dtc_net	0.625 87	0.703 86	0.751 65	0.794 51	0.535 92	0.848 27	0.480 54	0.676 86	0.528 80	0.469 90	0.944 76	0.454 70	0.004 120	0.464 97	0.636 83	0.704 49	0.758 58	0.548 86	0.924 30	0.787 84	0.492 103

Weakly-Openseg v3	0.625 87	0.924 8	0.787 42	0.620 100	0.555 90	0.811 70	0.393 93	0.666 88	0.382 111	0.520 77	0.953 44	0.250 115	0.208 82	0.604 61	0.670 77	0.644 78	0.742 66	0.538 92	0.919 36	0.803 68	0.513 101

HPEIN	0.618 90	0.729 74	0.668 98	0.647 95	0.597 74	0.766 92	0.414 85	0.680 83	0.520 82	0.525 73	0.946 67	0.432 79	0.215 80	0.493 93	0.599 87	0.638 79	0.617 105	0.570 71	0.897 52	0.806 65	0.605 72
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCN	0.610 91	0.858 26	0.772 50	0.489 113	0.532 93	0.792 82	0.404 89	0.643 93	0.570 70	0.507 82	0.935 90	0.414 90	0.046 117	0.510 88	0.702 72	0.602 89	0.705 81	0.549 85	0.859 85	0.773 91	0.534 96
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 92	0.760 60	0.667 99	0.649 94	0.521 95	0.793 80	0.457 64	0.648 91	0.528 80	0.434 100	0.947 64	0.401 93	0.153 106	0.454 98	0.721 68	0.648 73	0.717 75	0.536 93	0.904 45	0.765 93	0.485 105
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer	0.600 93	0.634 98	0.743 72	0.697 78	0.601 73	0.781 85	0.437 80	0.585 100	0.493 90	0.446 95	0.933 95	0.394 94	0.011 119	0.654 45	0.661 82	0.603 88	0.733 70	0.526 95	0.832 91	0.761 95	0.480 106

LAP-D	0.594 94	0.720 80	0.692 92	0.637 99	0.456 104	0.773 90	0.391 96	0.730 72	0.587 59	0.445 97	0.940 84	0.381 97	0.288 42	0.434 101	0.453 102	0.591 93	0.649 98	0.581 69	0.777 99	0.749 99	0.610 68

DPC	0.592 95	0.720 80	0.700 86	0.602 104	0.480 100	0.762 95	0.380 99	0.713 78	0.585 62	0.437 98	0.940 84	0.369 99	0.288 42	0.434 101	0.509 98	0.590 95	0.639 103	0.567 75	0.772 100	0.755 97	0.592 78
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet	0.589 96	0.766 59	0.659 102	0.683 81	0.470 103	0.740 100	0.387 97	0.620 96	0.490 91	0.476 88	0.922 102	0.355 102	0.245 69	0.511 87	0.511 97	0.571 98	0.643 101	0.493 102	0.872 76	0.762 94	0.600 74

ROSMRF	0.580 97	0.772 54	0.707 82	0.681 82	0.563 86	0.764 93	0.362 101	0.515 108	0.465 99	0.465 92	0.936 89	0.427 85	0.207 83	0.438 99	0.577 89	0.536 101	0.675 92	0.486 103	0.723 106	0.779 87	0.524 98

SD-DETR	0.576 98	0.746 65	0.609 111	0.445 117	0.517 96	0.643 112	0.366 100	0.714 77	0.456 101	0.468 91	0.870 114	0.432 79	0.264 61	0.558 76	0.674 76	0.586 96	0.688 87	0.482 104	0.739 104	0.733 102	0.537 95

SQN_0.1%	0.569 99	0.676 90	0.696 89	0.657 90	0.497 97	0.779 88	0.424 82	0.548 104	0.515 84	0.376 105	0.902 111	0.422 87	0.357 10	0.379 106	0.456 101	0.596 92	0.659 96	0.544 87	0.685 109	0.665 113	0.556 91

TextureNet	0.566 100	0.672 92	0.664 100	0.671 86	0.494 98	0.719 102	0.445 73	0.678 85	0.411 109	0.396 103	0.935 90	0.356 101	0.225 75	0.412 103	0.535 93	0.565 99	0.636 104	0.464 106	0.794 98	0.680 110	0.568 85
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet	0.562 101	0.648 95	0.700 86	0.770 59	0.586 79	0.687 106	0.333 105	0.650 90	0.514 85	0.475 89	0.906 108	0.359 100	0.223 77	0.340 108	0.442 103	0.422 112	0.668 94	0.501 99	0.708 107	0.779 87	0.534 96

Pointnet++ & Feature	0.557 102	0.735 70	0.661 101	0.686 80	0.491 99	0.744 99	0.392 94	0.539 105	0.451 102	0.375 106	0.946 67	0.376 98	0.205 85	0.403 104	0.356 108	0.553 100	0.643 101	0.497 100	0.824 94	0.756 96	0.515 99

GMLPs	0.538 103	0.495 113	0.693 91	0.647 95	0.471 102	0.793 80	0.300 108	0.477 109	0.505 87	0.358 107	0.903 110	0.327 105	0.081 114	0.472 96	0.529 95	0.448 110	0.710 76	0.509 96	0.746 102	0.737 101	0.554 92

PanopticFusion-label	0.529 104	0.491 114	0.688 95	0.604 103	0.386 109	0.632 113	0.225 119	0.705 81	0.434 106	0.293 113	0.815 117	0.348 103	0.241 70	0.499 91	0.669 78	0.507 103	0.649 98	0.442 112	0.796 97	0.602 117	0.561 88
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak	0.516 105	0.676 90	0.591 114	0.609 101	0.442 105	0.774 89	0.335 104	0.597 97	0.422 108	0.357 108	0.932 96	0.341 104	0.094 113	0.298 110	0.528 96	0.473 108	0.676 91	0.495 101	0.602 115	0.721 105	0.349 117

Online SegFusion	0.515 106	0.607 103	0.644 105	0.579 106	0.434 106	0.630 114	0.353 102	0.628 95	0.440 104	0.410 101	0.762 120	0.307 107	0.167 101	0.520 85	0.403 106	0.516 102	0.565 108	0.447 110	0.678 110	0.701 107	0.514 100
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF	0.501 107	0.558 108	0.608 112	0.424 119	0.478 101	0.690 105	0.246 115	0.586 99	0.468 97	0.450 94	0.911 106	0.394 94	0.160 104	0.438 99	0.212 115	0.432 111	0.541 113	0.475 105	0.742 103	0.727 103	0.477 107

PCNN	0.498 108	0.559 107	0.644 105	0.560 108	0.420 108	0.711 104	0.229 117	0.414 110	0.436 105	0.352 109	0.941 82	0.324 106	0.155 105	0.238 115	0.387 107	0.493 104	0.529 114	0.509 96	0.813 96	0.751 98	0.504 102

3DMV	0.484 109	0.484 115	0.538 117	0.643 97	0.424 107	0.606 117	0.310 106	0.574 101	0.433 107	0.378 104	0.796 118	0.301 108	0.214 81	0.537 83	0.208 116	0.472 109	0.507 117	0.413 115	0.693 108	0.602 117	0.539 94
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 110	0.577 106	0.611 110	0.356 121	0.321 117	0.715 103	0.299 110	0.376 114	0.328 117	0.319 111	0.944 76	0.285 110	0.164 102	0.216 118	0.229 113	0.484 106	0.545 112	0.456 108	0.755 101	0.709 106	0.475 108
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 111	0.679 89	0.604 113	0.578 107	0.380 110	0.682 107	0.291 111	0.106 121	0.483 94	0.258 119	0.920 103	0.258 114	0.025 118	0.231 117	0.325 109	0.480 107	0.560 110	0.463 107	0.725 105	0.666 112	0.231 121
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 112	0.474 116	0.623 108	0.463 115	0.366 112	0.651 110	0.310 106	0.389 113	0.349 115	0.330 110	0.937 87	0.271 112	0.126 110	0.285 111	0.224 114	0.350 117	0.577 107	0.445 111	0.625 113	0.723 104	0.394 113
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET2	0.442 113	0.548 110	0.548 116	0.597 105	0.363 113	0.628 115	0.300 108	0.292 116	0.374 112	0.307 112	0.881 113	0.268 113	0.186 94	0.238 115	0.204 117	0.407 113	0.506 118	0.449 109	0.667 111	0.620 116	0.462 111

SurfaceConvPF	0.442 113	0.505 112	0.622 109	0.380 120	0.342 115	0.654 109	0.227 118	0.397 112	0.367 113	0.276 115	0.924 100	0.240 116	0.198 90	0.359 107	0.262 111	0.366 114	0.581 106	0.435 113	0.640 112	0.668 111	0.398 112
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 115	0.437 118	0.646 104	0.474 114	0.369 111	0.645 111	0.353 102	0.258 118	0.282 120	0.279 114	0.918 105	0.298 109	0.147 109	0.283 112	0.294 110	0.487 105	0.562 109	0.427 114	0.619 114	0.633 115	0.352 116
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 116	0.525 111	0.647 103	0.522 109	0.324 116	0.488 121	0.077 122	0.712 79	0.353 114	0.401 102	0.636 122	0.281 111	0.176 97	0.340 108	0.565 91	0.175 121	0.551 111	0.398 116	0.370 122	0.602 117	0.361 115

SPLAT Net	0.393 117	0.472 117	0.511 118	0.606 102	0.311 118	0.656 108	0.245 116	0.405 111	0.328 117	0.197 120	0.927 99	0.227 118	0.000 122	0.001 123	0.249 112	0.271 120	0.510 115	0.383 118	0.593 116	0.699 108	0.267 119
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF	0.383 118	0.297 120	0.491 119	0.432 118	0.358 114	0.612 116	0.274 113	0.116 120	0.411 109	0.265 116	0.904 109	0.229 117	0.079 115	0.250 113	0.185 118	0.320 118	0.510 115	0.385 117	0.548 117	0.597 120	0.394 113

PointNet++	0.339 119	0.584 105	0.478 120	0.458 116	0.256 120	0.360 122	0.250 114	0.247 119	0.278 121	0.261 118	0.677 121	0.183 119	0.117 111	0.212 119	0.145 120	0.364 115	0.346 122	0.232 122	0.548 117	0.523 121	0.252 120
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
GrowSP++	0.323 120	0.114 122	0.589 115	0.499 111	0.147 122	0.555 118	0.290 112	0.336 115	0.290 119	0.262 117	0.865 116	0.102 122	0.000 122	0.037 121	0.000 123	0.000 123	0.462 119	0.381 119	0.389 121	0.664 114	0.473 109

SSC-UNet	0.308 121	0.353 119	0.290 122	0.278 122	0.166 121	0.553 119	0.169 121	0.286 117	0.147 122	0.148 122	0.908 107	0.182 120	0.064 116	0.023 122	0.018 122	0.354 116	0.363 120	0.345 120	0.546 119	0.685 109	0.278 118

ScanNet	0.306 122	0.203 121	0.366 121	0.501 110	0.311 118	0.524 120	0.211 120	0.002 123	0.342 116	0.189 121	0.786 119	0.145 121	0.102 112	0.245 114	0.152 119	0.318 119	0.348 121	0.300 121	0.460 120	0.437 122	0.182 122
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 123	0.000 123	0.041 123	0.172 123	0.030 123	0.062 123	0.001 123	0.035 122	0.004 123	0.051 123	0.143 123	0.019 123	0.003 121	0.041 120	0.050 121	0.003 122	0.054 123	0.018 123	0.005 123	0.264 123	0.082 123

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

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

Competitor-MAFT	0.816 1	1.000 1	0.983 4	0.872 11	0.718 5	0.941 1	0.588 5	0.652 40	0.819 2	0.776 3	0.720 5	0.780 5	0.769 12	1.000 1	0.797 11	0.813 30	0.798 8	1.000 1	0.659 4

PointRel	0.816 1	1.000 1	0.971 9	0.908 6	0.743 2	0.923 8	0.573 9	0.714 22	0.695 19	0.734 11	0.747 2	0.725 12	0.809 1	1.000 1	0.814 9	0.899 4	0.820 4	1.000 1	0.610 18
: Relation3D: Enhancing Relation Modeling for Point Cloud Instance Segmentation. CVPR 2025
Spherical Mask(CtoF)	0.812 3	1.000 1	0.973 8	0.852 15	0.718 6	0.917 10	0.574 7	0.677 31	0.748 12	0.729 15	0.715 8	0.795 2	0.809 1	1.000 1	0.831 4	0.854 10	0.787 12	1.000 1	0.638 7

EV3D	0.811 4	1.000 1	0.968 10	0.852 15	0.717 7	0.921 9	0.574 8	0.677 31	0.748 12	0.730 14	0.703 14	0.795 2	0.809 1	1.000 1	0.831 4	0.854 10	0.778 16	1.000 1	0.638 8

VDG-Uni3DSeg	0.804 5	1.000 1	0.990 1	0.886 9	0.688 20	0.912 12	0.602 2	0.703 26	0.786 7	0.771 4	0.708 11	0.700 17	0.669 26	0.981 40	0.789 17	0.903 1	0.772 19	1.000 1	0.609 19

SIM3D	0.803 6	1.000 1	0.967 11	0.863 14	0.692 19	0.924 7	0.552 13	0.732 21	0.667 24	0.732 13	0.662 18	0.796 1	0.789 9	1.000 1	0.803 10	0.864 7	0.766 22	1.000 1	0.643 6

OneFormer3D	0.801 7	1.000 1	0.973 7	0.909 5	0.698 15	0.928 5	0.582 6	0.668 36	0.685 20	0.780 2	0.687 16	0.698 21	0.702 15	1.000 1	0.794 13	0.900 3	0.784 14	0.986 54	0.635 9
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
Competitor-SPFormer	0.800 8	1.000 1	0.986 3	0.845 17	0.705 13	0.915 11	0.532 15	0.733 20	0.757 11	0.733 12	0.708 10	0.698 20	0.648 38	0.981 40	0.890 1	0.830 20	0.796 9	0.997 41	0.644 5

UniPerception	0.800 8	1.000 1	0.930 13	0.872 11	0.727 4	0.862 26	0.454 21	0.764 13	0.820 1	0.746 8	0.706 12	0.750 7	0.772 10	0.926 48	0.764 20	0.818 28	0.826 2	0.997 41	0.660 3

InsSSM	0.799 10	1.000 1	0.915 15	0.710 43	0.729 3	0.925 6	0.664 1	0.670 34	0.770 8	0.766 5	0.739 3	0.737 8	0.700 16	1.000 1	0.792 14	0.829 22	0.815 5	0.997 41	0.625 11
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
DCD	0.798 11	1.000 1	0.878 22	0.792 29	0.693 18	0.936 2	0.596 3	0.685 30	0.663 26	0.736 9	0.717 6	0.788 4	0.693 21	1.000 1	0.825 7	0.840 16	0.837 1	1.000 1	0.689 1

TST3D	0.795 12	1.000 1	0.929 14	0.918 4	0.709 10	0.884 21	0.596 4	0.704 25	0.769 9	0.734 10	0.644 23	0.699 19	0.751 13	1.000 1	0.794 12	0.876 6	0.757 25	0.997 41	0.550 35
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
MG-Former	0.791 13	1.000 1	0.980 6	0.837 20	0.626 28	0.897 14	0.543 14	0.759 15	0.800 6	0.766 6	0.659 19	0.769 6	0.697 19	1.000 1	0.791 15	0.707 51	0.791 11	1.000 1	0.610 17

ExtMask3D	0.789 14	1.000 1	0.988 2	0.756 36	0.706 12	0.912 13	0.429 22	0.647 42	0.806 5	0.755 7	0.673 17	0.689 22	0.772 11	1.000 1	0.789 16	0.852 12	0.811 6	1.000 1	0.617 14

Queryformer	0.787 15	1.000 1	0.933 12	0.601 53	0.754 1	0.886 19	0.558 12	0.661 38	0.767 10	0.665 21	0.716 7	0.639 28	0.808 5	1.000 1	0.844 3	0.897 5	0.804 7	1.000 1	0.624 12

MAFT	0.786 16	1.000 1	0.894 20	0.807 24	0.694 17	0.893 17	0.486 17	0.674 33	0.740 14	0.786 1	0.704 13	0.727 11	0.739 14	1.000 1	0.707 27	0.849 14	0.756 26	1.000 1	0.685 2

KmaxOneFormerNet	0.783 17	0.903 58	0.981 5	0.794 28	0.706 11	0.931 4	0.561 11	0.701 27	0.706 17	0.727 16	0.697 15	0.731 10	0.689 23	1.000 1	0.856 2	0.750 42	0.761 24	1.000 1	0.599 23

Mask3D	0.780 18	1.000 1	0.786 46	0.716 41	0.696 16	0.885 20	0.500 16	0.714 22	0.810 4	0.672 20	0.715 8	0.679 23	0.809 1	1.000 1	0.831 4	0.833 19	0.787 12	1.000 1	0.602 21
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormer	0.770 19	0.903 58	0.903 17	0.806 25	0.609 35	0.886 18	0.568 10	0.815 6	0.705 18	0.711 17	0.655 20	0.652 27	0.685 24	1.000 1	0.789 18	0.809 31	0.776 18	1.000 1	0.583 27
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++	0.769 20	1.000 1	0.803 39	0.937 1	0.684 21	0.865 23	0.213 38	0.870 2	0.664 25	0.571 28	0.758 1	0.702 16	0.807 6	1.000 1	0.653 34	0.902 2	0.792 10	1.000 1	0.626 10

SoftGroup	0.761 21	1.000 1	0.808 35	0.845 17	0.716 8	0.862 25	0.243 35	0.824 4	0.655 28	0.620 22	0.734 4	0.699 18	0.791 8	0.981 40	0.716 24	0.844 15	0.769 20	1.000 1	0.594 25
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNet	0.757 22	1.000 1	0.904 16	0.731 39	0.678 22	0.895 15	0.458 19	0.644 44	0.670 23	0.710 18	0.620 28	0.732 9	0.650 28	1.000 1	0.756 21	0.778 34	0.779 15	1.000 1	0.614 15
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
TD3D	0.751 23	1.000 1	0.774 47	0.867 13	0.621 30	0.934 3	0.404 23	0.706 24	0.812 3	0.605 25	0.633 26	0.626 29	0.690 22	1.000 1	0.640 36	0.820 25	0.777 17	1.000 1	0.612 16
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNet	0.747 24	1.000 1	0.818 31	0.837 21	0.713 9	0.844 28	0.457 20	0.647 42	0.711 16	0.614 23	0.617 30	0.657 26	0.650 28	1.000 1	0.692 28	0.822 24	0.765 23	1.000 1	0.595 24
Weiguang Zhao, Yuyao Yan, Chaolong Yang, Jianan Ye, Xi Yang, Kaizhu Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut	0.732 25	1.000 1	0.788 44	0.724 40	0.642 27	0.859 27	0.248 34	0.787 11	0.618 31	0.596 26	0.653 22	0.722 14	0.583 50	1.000 1	0.766 19	0.861 8	0.825 3	1.000 1	0.504 41

IPCA-Inst	0.731 26	1.000 1	0.788 45	0.884 10	0.698 14	0.788 44	0.252 33	0.760 14	0.646 29	0.511 36	0.637 25	0.665 25	0.804 7	1.000 1	0.644 35	0.778 35	0.747 28	1.000 1	0.561 31

TopoSeg	0.725 27	1.000 1	0.806 38	0.933 2	0.668 24	0.758 49	0.272 32	0.734 19	0.630 30	0.549 32	0.654 21	0.606 30	0.697 20	0.966 45	0.612 40	0.839 17	0.754 27	1.000 1	0.573 28

DKNet	0.718 28	1.000 1	0.814 32	0.782 30	0.619 32	0.872 22	0.224 36	0.751 17	0.569 35	0.677 19	0.585 35	0.724 13	0.633 40	0.981 40	0.515 50	0.819 26	0.736 29	1.000 1	0.617 13
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC	0.707 29	1.000 1	0.850 24	0.924 3	0.648 25	0.747 52	0.162 40	0.862 3	0.572 34	0.520 34	0.624 27	0.549 33	0.649 37	1.000 1	0.560 45	0.706 52	0.768 21	1.000 1	0.591 26

HAIS	0.699 30	1.000 1	0.849 25	0.820 22	0.675 23	0.808 38	0.279 30	0.757 16	0.465 41	0.517 35	0.596 32	0.559 32	0.600 44	1.000 1	0.654 33	0.767 37	0.676 33	0.994 50	0.560 32
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet	0.698 31	1.000 1	0.697 63	0.888 8	0.556 42	0.803 39	0.387 24	0.626 46	0.417 46	0.556 31	0.585 36	0.702 15	0.600 44	1.000 1	0.824 8	0.720 50	0.692 31	1.000 1	0.509 40
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup	0.694 32	1.000 1	0.799 41	0.811 23	0.622 29	0.817 33	0.376 25	0.805 9	0.590 33	0.487 40	0.568 39	0.525 37	0.650 28	0.835 58	0.600 41	0.829 21	0.655 36	1.000 1	0.526 37

ODIN - Ins	0.693 33	1.000 1	0.880 21	0.647 48	0.620 31	0.779 46	0.336 27	0.501 61	0.681 21	0.577 27	0.595 33	0.679 24	0.683 25	1.000 1	0.709 26	0.816 29	0.637 40	0.770 70	0.557 33
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
DANCENET	0.680 34	1.000 1	0.807 36	0.733 38	0.600 36	0.768 48	0.375 26	0.543 54	0.538 36	0.610 24	0.599 31	0.498 38	0.632 42	0.981 40	0.739 23	0.856 9	0.633 43	0.882 65	0.454 50

SphereSeg	0.680 34	1.000 1	0.856 23	0.744 37	0.618 33	0.893 16	0.151 41	0.651 41	0.713 15	0.537 33	0.579 38	0.430 47	0.651 27	1.000 1	0.389 61	0.744 45	0.697 30	0.991 52	0.601 22

Box2Mask	0.677 36	1.000 1	0.847 26	0.771 32	0.509 51	0.816 34	0.277 31	0.558 53	0.482 38	0.562 30	0.640 24	0.448 43	0.700 16	1.000 1	0.666 29	0.852 13	0.578 50	0.997 41	0.488 45
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance	0.672 37	1.000 1	0.758 55	0.682 45	0.576 40	0.842 29	0.477 18	0.504 60	0.524 37	0.567 29	0.585 37	0.451 42	0.557 52	1.000 1	0.751 22	0.797 32	0.563 53	1.000 1	0.467 49
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group	0.664 38	1.000 1	0.822 30	0.764 35	0.616 34	0.815 35	0.139 45	0.694 29	0.597 32	0.459 44	0.566 40	0.599 31	0.600 44	0.516 68	0.715 25	0.819 27	0.635 41	1.000 1	0.603 20
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance	0.657 39	1.000 1	0.760 53	0.667 47	0.581 38	0.863 24	0.323 28	0.655 39	0.477 39	0.473 42	0.549 42	0.432 46	0.650 28	1.000 1	0.655 32	0.738 46	0.585 49	0.944 57	0.472 48

CSC-Pretrained	0.648 40	1.000 1	0.810 33	0.768 33	0.523 49	0.813 36	0.143 44	0.819 5	0.389 49	0.422 53	0.511 46	0.443 44	0.650 28	1.000 1	0.624 38	0.732 47	0.634 42	1.000 1	0.375 57

PE	0.645 41	1.000 1	0.773 49	0.798 27	0.538 44	0.786 45	0.088 53	0.799 10	0.350 53	0.435 51	0.547 43	0.545 34	0.646 39	0.933 47	0.562 44	0.761 40	0.556 58	0.997 41	0.501 43
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 42	1.000 1	0.758 54	0.582 59	0.539 43	0.826 32	0.046 58	0.765 12	0.372 51	0.436 50	0.588 34	0.539 36	0.650 28	1.000 1	0.577 42	0.750 43	0.653 38	0.997 41	0.495 44
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3D	0.641 43	1.000 1	0.841 27	0.893 7	0.531 46	0.802 40	0.115 50	0.588 51	0.448 43	0.438 48	0.537 45	0.430 48	0.550 53	0.857 50	0.534 48	0.764 39	0.657 35	0.987 53	0.568 29
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN	0.638 44	1.000 1	0.895 19	0.800 26	0.480 55	0.676 57	0.144 43	0.737 18	0.354 52	0.447 45	0.400 59	0.365 54	0.700 16	1.000 1	0.569 43	0.836 18	0.599 45	1.000 1	0.473 47

PointGroup	0.636 45	1.000 1	0.765 50	0.624 50	0.505 53	0.797 41	0.116 49	0.696 28	0.384 50	0.441 46	0.559 41	0.476 40	0.596 47	1.000 1	0.666 29	0.756 41	0.556 57	0.997 41	0.513 39
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group	0.635 46	0.667 61	0.797 43	0.714 42	0.562 41	0.774 47	0.146 42	0.810 8	0.429 45	0.476 41	0.546 44	0.399 50	0.633 40	1.000 1	0.632 37	0.722 49	0.609 44	1.000 1	0.514 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
Mask3D_evaluation	0.631 47	1.000 1	0.829 29	0.606 52	0.646 26	0.836 30	0.068 54	0.511 58	0.462 42	0.507 37	0.619 29	0.389 52	0.610 43	1.000 1	0.432 56	0.828 23	0.673 34	0.788 69	0.552 34

DENet	0.629 48	1.000 1	0.797 42	0.608 51	0.589 37	0.627 61	0.219 37	0.882 1	0.310 55	0.402 58	0.383 61	0.396 51	0.650 28	1.000 1	0.663 31	0.543 69	0.691 32	1.000 1	0.568 30

3D-MPA	0.611 49	1.000 1	0.833 28	0.765 34	0.526 48	0.756 50	0.136 47	0.588 51	0.470 40	0.438 49	0.432 55	0.358 56	0.650 28	0.857 50	0.429 57	0.765 38	0.557 56	1.000 1	0.430 52
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS	0.605 50	1.000 1	0.801 40	0.599 54	0.535 45	0.728 54	0.286 29	0.436 65	0.679 22	0.491 38	0.433 53	0.256 58	0.404 65	0.857 50	0.620 39	0.724 48	0.510 63	1.000 1	0.539 36

AOIA	0.601 51	1.000 1	0.761 52	0.687 44	0.485 54	0.828 31	0.008 65	0.663 37	0.405 48	0.405 57	0.425 56	0.490 39	0.596 47	0.714 61	0.553 47	0.779 33	0.597 46	0.992 51	0.424 54

PCJC	0.578 52	1.000 1	0.810 34	0.583 58	0.449 58	0.813 37	0.042 59	0.603 49	0.341 54	0.490 39	0.465 50	0.410 49	0.650 28	0.835 58	0.264 67	0.694 56	0.561 54	0.889 62	0.504 42

SSEN	0.575 53	1.000 1	0.761 51	0.473 61	0.477 56	0.795 42	0.066 55	0.529 56	0.658 27	0.460 43	0.461 51	0.380 53	0.331 67	0.859 49	0.401 60	0.692 58	0.653 37	1.000 1	0.348 59
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg	0.567 54	0.528 71	0.708 62	0.626 49	0.580 39	0.745 53	0.063 56	0.627 45	0.240 59	0.400 59	0.497 47	0.464 41	0.515 54	1.000 1	0.475 52	0.745 44	0.571 51	1.000 1	0.429 53

NeuralBF	0.555 55	0.667 61	0.896 18	0.843 19	0.517 50	0.751 51	0.029 60	0.519 57	0.414 47	0.439 47	0.465 49	0.000 77	0.484 56	0.857 50	0.287 65	0.693 57	0.651 39	1.000 1	0.485 46
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML	0.549 56	1.000 1	0.807 37	0.588 57	0.327 63	0.647 59	0.004 67	0.815 7	0.180 62	0.418 54	0.364 63	0.182 61	0.445 59	1.000 1	0.442 55	0.688 59	0.571 52	1.000 1	0.396 55
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance	0.539 57	1.000 1	0.621 66	0.300 64	0.530 47	0.698 55	0.127 48	0.533 55	0.222 60	0.430 52	0.400 58	0.365 54	0.574 51	0.938 46	0.472 53	0.659 61	0.543 59	0.944 57	0.347 60

One_Thing_One_Click	0.529 58	0.667 61	0.718 58	0.777 31	0.399 59	0.683 56	0.000 70	0.669 35	0.138 65	0.391 60	0.374 62	0.539 35	0.360 66	0.641 65	0.556 46	0.774 36	0.593 47	0.997 41	0.251 65
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN	0.515 59	1.000 1	0.538 71	0.282 65	0.468 57	0.790 43	0.173 39	0.345 67	0.429 44	0.413 56	0.484 48	0.176 62	0.595 49	0.591 66	0.522 49	0.668 60	0.476 64	0.986 55	0.327 61

Occipital-SCS	0.512 60	1.000 1	0.716 59	0.509 60	0.506 52	0.611 62	0.092 52	0.602 50	0.177 63	0.346 63	0.383 60	0.165 63	0.442 60	0.850 57	0.386 62	0.618 65	0.543 60	0.889 62	0.389 56

3D-BoNet	0.488 61	1.000 1	0.672 65	0.590 56	0.301 65	0.484 72	0.098 51	0.620 47	0.306 56	0.341 64	0.259 67	0.125 65	0.434 62	0.796 60	0.402 59	0.499 71	0.513 62	0.909 61	0.439 51
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst	0.478 62	0.667 61	0.712 61	0.595 55	0.259 68	0.550 68	0.000 70	0.613 48	0.175 64	0.250 69	0.434 52	0.437 45	0.411 64	0.857 50	0.485 51	0.591 68	0.267 74	0.944 57	0.359 58
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS	0.470 63	0.667 61	0.685 64	0.677 46	0.372 61	0.562 66	0.000 70	0.482 62	0.244 58	0.316 66	0.298 64	0.052 72	0.442 61	0.857 50	0.267 66	0.702 53	0.559 55	1.000 1	0.287 63

SALoss-ResNet	0.459 64	1.000 1	0.737 57	0.159 75	0.259 67	0.587 64	0.138 46	0.475 63	0.217 61	0.416 55	0.408 57	0.128 64	0.315 68	0.714 61	0.411 58	0.536 70	0.590 48	0.873 66	0.304 62
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.447 65	0.528 71	0.555 69	0.381 62	0.382 60	0.633 60	0.002 68	0.509 59	0.260 57	0.361 62	0.432 54	0.327 57	0.451 58	0.571 67	0.367 63	0.639 63	0.386 65	0.980 56	0.276 64
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_ins	0.445 66	0.667 61	0.773 48	0.185 72	0.317 64	0.656 58	0.000 70	0.407 66	0.134 66	0.381 61	0.267 66	0.217 60	0.476 57	0.714 61	0.452 54	0.629 64	0.514 61	1.000 1	0.222 68
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SIS	0.382 67	1.000 1	0.432 74	0.245 67	0.190 69	0.577 65	0.013 64	0.263 69	0.033 72	0.320 65	0.240 68	0.075 68	0.422 63	0.857 50	0.117 72	0.699 54	0.271 73	0.883 64	0.235 67
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.323 68	0.667 61	0.542 70	0.264 66	0.157 72	0.550 67	0.000 70	0.205 72	0.009 74	0.270 68	0.218 69	0.075 68	0.500 55	0.688 64	0.007 78	0.698 55	0.301 70	0.459 75	0.200 69
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone	0.319 69	0.667 61	0.715 60	0.233 68	0.189 70	0.479 73	0.008 65	0.218 70	0.067 71	0.201 71	0.173 70	0.107 66	0.123 73	0.438 69	0.150 69	0.615 66	0.355 66	0.916 60	0.093 77

R-PointNet	0.306 70	0.500 73	0.405 75	0.311 63	0.348 62	0.589 63	0.054 57	0.068 75	0.126 67	0.283 67	0.290 65	0.028 73	0.219 71	0.214 72	0.331 64	0.396 75	0.275 71	0.821 68	0.245 66

Region-18class	0.284 71	0.250 77	0.751 56	0.228 70	0.270 66	0.521 69	0.000 70	0.468 64	0.008 76	0.205 70	0.127 71	0.000 77	0.068 75	0.070 76	0.262 68	0.652 62	0.323 68	0.740 71	0.173 70

SemRegionNet-20cls	0.250 72	0.333 74	0.613 67	0.229 69	0.163 71	0.493 70	0.000 70	0.304 68	0.107 68	0.147 74	0.100 73	0.052 71	0.231 69	0.119 74	0.039 74	0.445 73	0.325 67	0.654 72	0.141 73

tmp	0.248 73	0.667 61	0.437 73	0.188 71	0.153 73	0.491 71	0.000 70	0.208 71	0.094 70	0.153 73	0.099 74	0.057 70	0.217 72	0.119 74	0.039 74	0.466 72	0.302 69	0.640 73	0.140 74

3D-BEVIS	0.248 73	0.667 61	0.566 68	0.076 76	0.035 78	0.394 76	0.027 62	0.035 77	0.098 69	0.099 76	0.030 77	0.025 74	0.098 74	0.375 71	0.126 71	0.604 67	0.181 76	0.854 67	0.171 71
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sem_Recon_ins	0.227 75	0.764 60	0.486 72	0.069 77	0.098 75	0.426 75	0.017 63	0.067 76	0.015 73	0.172 72	0.100 72	0.096 67	0.054 77	0.183 73	0.135 70	0.366 76	0.260 75	0.614 74	0.168 72

ASIS	0.199 76	0.333 74	0.253 77	0.167 74	0.140 74	0.438 74	0.000 70	0.177 73	0.008 75	0.121 75	0.069 75	0.004 76	0.231 70	0.429 70	0.036 76	0.445 74	0.273 72	0.333 77	0.119 76

Sgpn_scannet	0.143 77	0.208 78	0.390 76	0.169 73	0.065 76	0.275 77	0.029 61	0.069 74	0.000 77	0.087 77	0.043 76	0.014 75	0.027 78	0.000 77	0.112 73	0.351 77	0.168 77	0.438 76	0.138 75

MaskRCNN 2d->3d Proj	0.058 78	0.333 74	0.002 78	0.000 78	0.053 77	0.002 78	0.002 69	0.021 78	0.000 77	0.045 78	0.024 78	0.238 59	0.065 76	0.000 77	0.014 77	0.107 78	0.020 78	0.110 78	0.006 78

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 18	0.898 1	0.723 1	0.714 1	0.954 2	0.454 1	0.509 1	0.773 1	0.895 1	0.756 1	0.820 1	0.653 1	0.935 1	0.891 1	0.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2D	0.670 2	0.822 3	0.795 3	0.836 2	0.659 3	0.481 2	0.451 14	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 11	0.770 2	0.574 3	0.865 10	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)
CU-Hybrid-2D Net	0.636 3	0.825 2	0.820 2	0.179 24	0.648 4	0.463 3	0.549 2	0.742 8	0.676 2	0.628 2	0.961 1	0.420 2	0.379 6	0.684 8	0.381 19	0.732 3	0.723 3	0.599 2	0.827 17	0.851 2	0.634 8

MVF-GNN（2D）	0.636 3	0.606 15	0.794 4	0.434 16	0.688 1	0.337 8	0.464 13	0.798 3	0.632 5	0.589 3	0.908 9	0.420 2	0.329 13	0.743 2	0.594 2	0.738 2	0.676 5	0.527 4	0.906 2	0.818 6	0.715 3

CMX	0.613 5	0.681 9	0.725 12	0.502 12	0.634 6	0.297 18	0.478 11	0.830 2	0.651 4	0.537 7	0.924 4	0.375 7	0.315 15	0.686 7	0.451 14	0.714 5	0.543 22	0.504 6	0.894 7	0.823 5	0.688 4

DMMF_3d	0.605 6	0.651 10	0.744 10	0.782 3	0.637 5	0.387 4	0.536 4	0.732 9	0.590 7	0.540 6	0.856 22	0.359 11	0.306 16	0.596 15	0.539 3	0.627 21	0.706 4	0.497 8	0.785 22	0.757 20	0.476 23

EMSANet	0.600 7	0.716 4	0.746 9	0.395 19	0.614 9	0.382 5	0.523 5	0.713 12	0.571 11	0.503 10	0.922 7	0.404 5	0.397 4	0.655 9	0.400 16	0.626 22	0.663 6	0.469 13	0.900 4	0.827 4	0.577 15
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
MCA-Net	0.595 8	0.533 21	0.756 8	0.746 4	0.590 10	0.334 10	0.506 8	0.670 16	0.587 8	0.500 12	0.905 11	0.366 10	0.352 9	0.601 14	0.506 8	0.669 17	0.648 9	0.501 7	0.839 16	0.769 16	0.516 22

RFBNet	0.592 9	0.616 12	0.758 7	0.659 5	0.581 11	0.330 11	0.469 12	0.655 19	0.543 14	0.524 8	0.924 4	0.355 13	0.336 11	0.572 18	0.479 10	0.671 15	0.648 9	0.480 10	0.814 20	0.814 7	0.614 11

FAN_NV_RVC	0.586 10	0.510 22	0.764 6	0.079 27	0.620 8	0.330 11	0.494 9	0.753 6	0.573 9	0.556 5	0.884 17	0.405 4	0.303 17	0.718 3	0.452 13	0.672 14	0.658 7	0.509 5	0.898 5	0.813 8	0.727 2

WSGFormer	0.585 11	0.706 5	0.708 17	0.434 16	0.574 13	0.283 21	0.538 3	0.759 5	0.542 16	0.482 16	0.924 4	0.351 15	0.333 12	0.614 11	0.393 17	0.692 10	0.551 21	0.461 14	0.874 9	0.809 9	0.673 6

DCRedNet	0.583 12	0.682 8	0.723 13	0.542 11	0.510 21	0.310 15	0.451 14	0.668 17	0.549 13	0.520 9	0.920 8	0.375 7	0.446 2	0.528 21	0.417 15	0.670 16	0.577 18	0.478 11	0.862 11	0.806 10	0.628 10

MIX6D_RVC	0.582 13	0.695 6	0.687 18	0.225 22	0.632 7	0.328 13	0.550 1	0.748 7	0.623 6	0.494 15	0.890 15	0.350 16	0.254 24	0.688 6	0.454 12	0.716 4	0.597 17	0.489 9	0.881 8	0.768 17	0.575 16

SSMA	0.577 14	0.695 6	0.716 15	0.439 14	0.563 15	0.314 14	0.444 16	0.719 10	0.551 12	0.503 10	0.887 16	0.346 17	0.348 10	0.603 13	0.353 21	0.709 6	0.600 15	0.457 15	0.901 3	0.786 12	0.599 14
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
DMMF	0.567 15	0.623 11	0.767 5	0.238 21	0.571 14	0.347 6	0.413 20	0.719 10	0.472 21	0.418 23	0.895 14	0.357 12	0.260 23	0.696 5	0.523 7	0.666 18	0.642 11	0.437 19	0.895 6	0.793 11	0.603 13

UNIV_CNP_RVC_UE	0.566 16	0.569 20	0.686 20	0.435 15	0.524 18	0.294 19	0.421 19	0.712 13	0.543 14	0.463 18	0.872 18	0.320 18	0.363 8	0.611 12	0.477 11	0.686 12	0.627 12	0.443 18	0.862 11	0.775 15	0.639 7

EMSAFormer	0.564 17	0.581 17	0.736 11	0.564 10	0.546 17	0.219 24	0.517 6	0.675 15	0.486 20	0.427 22	0.904 12	0.352 14	0.320 14	0.589 16	0.528 5	0.708 7	0.464 25	0.413 23	0.847 15	0.786 12	0.611 12

SN_RN152pyrx8_RVC	0.546 18	0.572 18	0.663 22	0.638 7	0.518 19	0.298 17	0.366 25	0.633 22	0.510 18	0.446 20	0.864 20	0.296 21	0.267 20	0.542 20	0.346 22	0.704 8	0.575 19	0.431 20	0.853 14	0.766 18	0.630 9

UDSSEG_RVC	0.545 19	0.610 14	0.661 23	0.588 8	0.556 16	0.268 22	0.482 10	0.642 21	0.572 10	0.475 17	0.836 24	0.312 19	0.367 7	0.630 10	0.189 24	0.639 20	0.495 24	0.452 16	0.826 18	0.756 21	0.541 18

segfomer with 6d	0.542 20	0.594 16	0.687 18	0.146 25	0.579 12	0.308 16	0.515 7	0.703 14	0.472 21	0.498 13	0.868 19	0.369 9	0.282 18	0.589 16	0.390 18	0.701 9	0.556 20	0.416 22	0.860 13	0.759 19	0.539 20

FuseNet	0.535 21	0.570 19	0.681 21	0.182 23	0.512 20	0.290 20	0.431 17	0.659 18	0.504 19	0.495 14	0.903 13	0.308 20	0.428 3	0.523 22	0.365 20	0.676 13	0.621 14	0.470 12	0.762 23	0.779 14	0.541 18
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++	0.503 22	0.613 13	0.722 14	0.418 18	0.358 27	0.337 8	0.370 24	0.479 25	0.443 23	0.368 25	0.907 10	0.207 24	0.213 26	0.464 25	0.525 6	0.618 23	0.657 8	0.450 17	0.788 21	0.721 24	0.408 26
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)	0.498 23	0.481 25	0.612 24	0.579 9	0.456 23	0.343 7	0.384 22	0.623 23	0.525 17	0.381 24	0.845 23	0.254 23	0.264 22	0.557 19	0.182 25	0.581 25	0.598 16	0.429 21	0.760 24	0.661 26	0.446 25
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVC	0.485 24	0.505 23	0.709 16	0.092 26	0.427 24	0.241 23	0.411 21	0.654 20	0.385 27	0.457 19	0.861 21	0.053 27	0.279 19	0.503 23	0.481 9	0.645 19	0.626 13	0.365 25	0.748 25	0.725 23	0.529 21
John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet	0.475 25	0.490 24	0.581 25	0.289 20	0.507 22	0.067 27	0.379 23	0.610 24	0.417 25	0.435 21	0.822 26	0.278 22	0.267 20	0.503 23	0.228 23	0.616 24	0.533 23	0.375 24	0.820 19	0.729 22	0.560 17

Enet (reimpl)	0.376 26	0.264 27	0.452 27	0.452 13	0.365 25	0.181 25	0.143 27	0.456 26	0.409 26	0.346 26	0.769 27	0.164 25	0.218 25	0.359 26	0.123 27	0.403 27	0.381 27	0.313 27	0.571 26	0.685 25	0.472 24
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 27	0.293 26	0.521 26	0.657 6	0.361 26	0.161 26	0.250 26	0.004 27	0.440 24	0.183 27	0.836 24	0.125 26	0.060 27	0.319 27	0.132 26	0.417 26	0.412 26	0.344 26	0.541 27	0.427 27	0.109 27
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

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

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

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
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

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
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