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

BFANet ScanNet200	0.360 4	0.553 6	0.293 4	0.193 4	0.483 10	0.096 5	0.266 5	0.000 3	0.000 6	0.000 1	0.298 12	0.255 11	0.661 1	0.810 5	0.810 3	0.194 9	0.785 6	0.000 3	0.000 16	0.161 5	0.000 9	0.494 8	0.382 2	0.574 4	0.258 4	0.000 9	0.372 8	0.000 1	0.000 3	0.043 13	0.436 7	0.000 10	0.000 1	0.239 2	0.000 2	0.901 3	0.105 1	0.689 4	0.025 4	0.128 2	0.614 2	0.436 1	0.493 16	0.000 1	0.000 2	0.526 4	0.546 12	0.109 4	0.651 13	0.953 4	0.753 6	0.101 6	0.143 12	0.897 4	0.000 1	0.431 1	0.469 14	0.000 7	0.522 5	0.337 5	0.661 6	0.459 2	0.409 6	0.666 4	0.102 12	0.508 5	0.757 4	0.000 8	0.060 13	0.970 3	0.497 1	0.000 1	0.376 2	0.511 3	0.262 4	0.688 2	0.921 1	0.617 9	0.321 11	0.590 6	0.491 8	0.556 3	0.000 3	0.000 1	0.481 4	0.093 1	0.043 2	0.284 2	0.000 4	0.875 14	0.135 8	0.669 5	0.124 12	0.394 6	0.849 11	0.298 2	0.000 1	0.476 16	0.088 11	0.042 7	0.000 4	0.000 1	0.254 3	0.653 10	0.741 6	0.215 1	0.573 5	0.852 5	0.266 9	0.654 1	0.056 10	0.835 5	0.000 5	0.492 1	0.000 1	0.000 7	0.000 3	0.612 8	0.000 4	0.000 7	0.000 1	0.616 5	0.469 16	0.460 4	0.698 13	0.516 2	0.000 1	0.378 7	0.563 4	0.476 5	0.863 5	0.574 9	0.330 6	0.000 11	0.282 4	0.000 1	0.760 4	0.710 4	0.233 1	0.000 9	0.641 3	0.814 2	0.000 1	0.585 9	0.053 10	0.000 7	0.000 1	0.629 9	0.000 2	0.000 1	0.678 3	0.528 12	0.534 4	0.129 13	0.596 4	0.973 3	0.264 11	0.772 2	0.526 9	0.139 10	0.707 4	0.000 1	0.000 12	0.764 13	0.591 15	0.848 6	0.000 1	0.827 4	0.338 3	0.806 11	0.000 1	0.568 9	0.151 8	0.358 2	0.659 9	0.510 4

ALS-MinkowskiNet	0.414 1	0.610 2	0.322 3	0.271 1	0.542 2	0.153 2	0.159 10	0.000 3	0.000 6	0.000 1	0.404 4	0.503 3	0.532 6	0.672 16	0.804 5	0.285 1	0.888 1	0.000 3	0.900 1	0.226 1	0.087 2	0.598 3	0.342 4	0.671 1	0.217 9	0.087 3	0.449 3	0.000 1	0.000 3	0.253 2	0.477 5	1.000 1	0.000 1	0.118 4	0.000 2	0.905 1	0.071 11	0.710 2	0.076 1	0.047 15	0.665 1	0.376 8	0.981 1	0.000 1	0.000 2	0.466 6	0.632 7	0.113 3	0.769 1	0.956 3	0.795 1	0.031 9	0.314 1	0.936 1	0.000 1	0.390 2	0.601 1	0.000 7	0.458 7	0.366 2	0.719 3	0.440 4	0.564 1	0.699 3	0.314 1	0.464 6	0.784 2	0.200 1	0.283 5	0.973 1	0.142 8	0.000 1	0.250 7	0.285 5	0.220 5	0.718 1	0.752 5	0.723 2	0.460 1	0.248 15	0.475 9	0.463 13	0.000 3	0.000 1	0.446 7	0.021 4	0.025 9	0.285 1	0.000 4	0.972 1	0.149 7	0.769 1	0.230 2	0.535 2	0.879 2	0.252 6	0.000 1	0.693 1	0.129 2	0.000 12	0.000 4	0.000 1	0.447 2	0.958 1	0.662 9	0.159 2	0.598 3	0.780 11	0.344 2	0.646 2	0.106 4	0.893 2	0.135 2	0.455 3	0.000 1	0.194 3	0.259 1	0.726 3	0.475 3	0.000 7	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 2	0.230 11	0.916 2	0.728 1	0.635 1	1.000 1	0.252 5	0.000 1	0.804 1	0.697 5	0.137 11	0.043 6	0.717 1	0.807 3	0.000 1	0.510 13	0.245 1	0.000 7	0.000 1	0.709 3	0.000 2	0.000 1	0.703 2	0.572 3	0.646 1	0.223 10	0.531 5	0.984 1	0.397 2	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 9	0.000 1	0.666 5	0.359 4	0.252 7	0.777 1	0.690 2

DITR	0.409 2	0.616 1	0.351 1	0.215 3	0.651 1	0.238 1	0.400 2	0.000 3	0.340 1	0.000 1	0.534 2	0.476 4	0.585 2	0.687 15	0.853 1	0.143 12	0.854 2	0.000 3	0.865 2	0.167 4	0.000 9	0.175 16	0.573 1	0.617 2	0.372 1	0.362 1	0.591 1	0.000 1	0.000 3	0.330 1	0.494 2	0.247 9	0.000 1	0.385 1	0.000 2	0.878 6	0.037 13	0.791 1	0.053 2	0.118 3	0.479 11	0.429 4	0.940 3	0.000 1	0.000 2	0.461 7	0.562 10	0.093 5	0.628 14	0.991 1	0.762 2	0.135 3	0.270 3	0.917 3	0.000 1	0.140 4	0.597 2	0.000 7	0.361 11	0.375 1	0.730 2	0.431 5	0.459 3	0.410 12	0.008 15	0.656 1	0.814 1	0.036 6	0.554 3	0.947 5	0.139 10	0.000 1	0.263 5	0.896 1	0.191 8	0.615 4	0.839 3	0.757 1	0.399 5	0.877 1	0.504 5	0.524 6	0.000 3	0.000 1	0.587 3	0.000 10	0.022 11	0.077 9	0.921 1	0.928 2	0.132 9	0.670 4	0.759 1	0.652 1	0.862 7	0.091 10	0.000 1	0.662 3	0.072 15	0.000 12	0.000 4	0.000 1	0.496 1	0.852 2	0.752 4	0.152 3	0.743 1	0.953 1	0.301 3	0.625 3	0.053 11	0.913 1	0.399 1	0.452 4	0.000 1	0.000 7	0.000 3	0.742 2	0.000 4	0.000 7	0.000 1	0.694 2	0.643 4	0.444 6	0.784 7	0.000 8	0.000 1	0.571 1	0.614 3	0.491 4	0.938 1	0.559 10	0.357 5	0.107 7	0.404 1	0.000 1	0.796 2	0.688 6	0.148 8	0.186 1	0.629 4	0.827 1	0.000 1	0.558 11	0.198 4	0.000 7	0.000 1	0.723 2	0.000 2	0.000 1	0.833 1	0.619 1	0.609 2	0.478 4	0.617 1	0.959 4	0.370 3	0.597 8	0.737 2	0.191 5	0.752 2	0.000 1	0.118 1	0.853 1	0.925 2	0.670 13	0.000 1	0.831 3	0.000 16	0.873 3	0.000 1	0.699 1	0.005 11	0.360 1	0.723 3	0.235 12

PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 2	0.520 3	0.109 4	0.108 15	0.000 3	0.337 2	0.000 1	0.310 11	0.394 8	0.494 11	0.753 8	0.848 2	0.256 3	0.717 7	0.000 3	0.842 3	0.192 3	0.065 3	0.449 9	0.346 3	0.546 6	0.190 12	0.000 9	0.384 6	0.000 1	0.000 3	0.218 3	0.505 1	0.791 2	0.000 1	0.136 3	0.000 2	0.903 2	0.073 10	0.687 6	0.000 7	0.168 1	0.551 4	0.387 7	0.941 2	0.000 1	0.000 2	0.397 12	0.654 3	0.000 10	0.714 4	0.759 14	0.752 7	0.118 5	0.264 4	0.926 2	0.000 1	0.048 5	0.575 4	0.000 7	0.597 1	0.366 2	0.755 1	0.469 1	0.474 2	0.798 1	0.140 9	0.617 2	0.692 6	0.000 8	0.592 2	0.971 2	0.188 3	0.000 1	0.133 9	0.593 2	0.349 1	0.650 3	0.717 7	0.699 3	0.455 2	0.790 2	0.523 3	0.636 1	0.301 1	0.000 1	0.622 2	0.000 10	0.017 14	0.259 3	0.000 4	0.921 4	0.337 1	0.733 2	0.210 3	0.514 3	0.860 8	0.407 1	0.000 1	0.688 2	0.109 7	0.000 12	0.000 4	0.000 1	0.151 4	0.671 8	0.782 1	0.115 12	0.641 2	0.903 2	0.349 1	0.616 4	0.088 5	0.832 7	0.000 5	0.480 2	0.000 1	0.428 1	0.000 3	0.497 9	0.000 4	0.000 7	0.000 1	0.662 4	0.690 2	0.612 1	0.828 1	0.575 1	0.000 1	0.404 6	0.644 1	0.325 7	0.887 4	0.728 1	0.009 15	0.134 6	0.026 16	0.000 1	0.761 3	0.731 3	0.172 6	0.077 3	0.528 7	0.727 7	0.000 1	0.603 4	0.220 3	0.022 3	0.000 1	0.740 1	0.000 2	0.000 1	0.661 4	0.586 2	0.566 3	0.436 5	0.531 5	0.978 2	0.457 1	0.708 3	0.583 5	0.141 8	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 14	0.281 3	0.738 2	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)
PPT-SpUNet-F.T.	0.332 11	0.556 5	0.270 6	0.123 13	0.519 4	0.091 6	0.349 3	0.000 3	0.000 6	0.000 1	0.339 8	0.383 9	0.498 10	0.833 4	0.807 4	0.241 4	0.584 8	0.000 3	0.755 6	0.124 7	0.000 9	0.608 2	0.330 7	0.530 9	0.314 2	0.000 9	0.374 7	0.000 1	0.000 3	0.197 4	0.459 6	0.000 10	0.000 1	0.117 5	0.000 2	0.876 7	0.095 2	0.682 9	0.000 7	0.086 7	0.518 6	0.433 2	0.930 4	0.000 1	0.000 2	0.563 3	0.542 13	0.077 7	0.715 3	0.858 10	0.756 5	0.008 16	0.171 11	0.874 7	0.000 1	0.039 6	0.550 10	0.000 7	0.545 4	0.256 8	0.657 8	0.453 3	0.351 9	0.449 9	0.213 6	0.392 11	0.611 10	0.000 8	0.037 14	0.946 6	0.138 13	0.000 1	0.000 12	0.063 10	0.308 2	0.537 7	0.796 4	0.673 4	0.323 10	0.392 10	0.400 13	0.509 7	0.000 3	0.000 1	0.649 1	0.000 10	0.023 10	0.000 11	0.000 4	0.914 7	0.002 15	0.506 15	0.163 10	0.359 8	0.872 5	0.000 11	0.000 1	0.623 6	0.112 5	0.001 11	0.000 4	0.000 1	0.021 8	0.753 5	0.565 15	0.150 4	0.579 4	0.806 9	0.267 8	0.616 4	0.042 13	0.783 12	0.000 5	0.374 10	0.000 1	0.000 7	0.000 3	0.620 7	0.000 4	0.000 7	0.000 1	0.572 12	0.634 5	0.350 9	0.792 4	0.000 8	0.000 1	0.376 8	0.535 6	0.378 6	0.855 6	0.672 3	0.074 12	0.000 11	0.185 9	0.000 1	0.727 11	0.660 11	0.076 16	0.000 9	0.432 11	0.646 10	0.000 1	0.594 7	0.006 12	0.000 7	0.000 1	0.658 6	0.000 2	0.000 1	0.661 4	0.549 9	0.300 13	0.291 9	0.045 13	0.942 11	0.304 7	0.600 7	0.572 6	0.135 12	0.695 5	0.000 1	0.008 9	0.793 8	0.942 1	0.899 2	0.000 1	0.816 6	0.181 7	0.897 2	0.000 1	0.679 4	0.223 6	0.264 4	0.691 5	0.345 11
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
PonderV2 ScanNet200	0.346 5	0.552 7	0.270 7	0.175 8	0.497 7	0.070 11	0.239 6	0.000 3	0.000 6	0.000 1	0.232 16	0.412 7	0.584 3	0.842 3	0.804 5	0.212 6	0.540 9	0.000 3	0.433 15	0.106 9	0.000 9	0.590 4	0.290 11	0.548 5	0.243 6	0.000 9	0.356 10	0.000 1	0.000 3	0.062 9	0.398 12	0.441 8	0.000 1	0.104 9	0.000 2	0.888 4	0.076 9	0.682 9	0.030 3	0.094 6	0.491 10	0.351 12	0.869 9	0.000 1	0.063 1	0.403 11	0.700 2	0.000 10	0.660 12	0.881 8	0.761 3	0.050 8	0.186 9	0.852 12	0.000 1	0.007 8	0.570 7	0.100 2	0.565 2	0.326 6	0.641 9	0.431 5	0.290 13	0.621 5	0.259 3	0.408 10	0.622 9	0.125 2	0.082 11	0.950 4	0.179 4	0.000 1	0.263 5	0.424 4	0.193 7	0.558 6	0.880 2	0.545 12	0.375 6	0.727 3	0.445 11	0.499 8	0.000 3	0.000 1	0.475 6	0.002 8	0.034 5	0.083 8	0.000 4	0.924 3	0.290 3	0.636 6	0.115 13	0.400 5	0.874 4	0.186 8	0.000 1	0.611 7	0.128 3	0.113 2	0.000 4	0.000 1	0.000 10	0.584 11	0.636 10	0.103 13	0.385 9	0.843 6	0.283 4	0.603 6	0.080 6	0.825 9	0.000 5	0.377 9	0.000 1	0.000 7	0.000 3	0.457 10	0.000 4	0.000 7	0.000 1	0.574 11	0.608 8	0.481 3	0.792 4	0.394 4	0.000 1	0.357 9	0.503 10	0.261 10	0.817 12	0.504 13	0.304 7	0.472 4	0.115 10	0.000 1	0.750 6	0.677 8	0.202 2	0.000 9	0.509 8	0.729 6	0.000 1	0.519 12	0.000 13	0.000 7	0.000 1	0.620 11	0.000 2	0.000 1	0.660 6	0.560 6	0.486 5	0.384 7	0.346 9	0.952 5	0.247 13	0.667 4	0.436 11	0.269 3	0.691 6	0.000 1	0.010 7	0.787 9	0.889 3	0.880 4	0.000 1	0.810 7	0.336 4	0.860 7	0.000 1	0.606 8	0.009 9	0.248 8	0.681 6	0.392 8
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
OctFormer ScanNet200	0.326 12	0.539 9	0.265 9	0.131 11	0.499 6	0.110 3	0.522 1	0.000 3	0.000 6	0.000 1	0.318 10	0.427 6	0.455 14	0.743 10	0.765 12	0.175 10	0.842 3	0.000 3	0.828 4	0.204 2	0.033 6	0.429 10	0.335 5	0.601 3	0.312 3	0.000 9	0.357 9	0.000 1	0.000 3	0.047 10	0.423 8	0.000 10	0.000 1	0.105 8	0.000 2	0.873 9	0.079 8	0.670 12	0.000 7	0.117 4	0.471 13	0.432 3	0.829 10	0.000 1	0.000 2	0.584 2	0.417 16	0.089 6	0.684 8	0.837 11	0.705 15	0.021 12	0.178 10	0.892 5	0.000 1	0.028 7	0.505 12	0.000 7	0.457 8	0.200 13	0.662 4	0.412 9	0.244 14	0.496 7	0.000 16	0.451 7	0.626 8	0.000 8	0.102 10	0.943 9	0.138 13	0.000 1	0.000 12	0.149 7	0.291 3	0.534 8	0.722 6	0.632 6	0.331 9	0.253 14	0.453 10	0.487 11	0.000 3	0.000 1	0.479 5	0.000 10	0.022 11	0.000 11	0.000 4	0.900 10	0.128 10	0.684 3	0.164 9	0.413 4	0.854 10	0.000 11	0.000 1	0.512 15	0.074 13	0.003 10	0.000 4	0.000 1	0.000 10	0.469 14	0.613 12	0.132 8	0.529 7	0.871 3	0.227 15	0.582 7	0.026 16	0.787 11	0.000 5	0.339 14	0.000 1	0.000 7	0.000 3	0.626 6	0.000 4	0.029 6	0.000 1	0.587 8	0.612 7	0.411 7	0.724 9	0.000 8	0.000 1	0.407 5	0.552 5	0.513 3	0.849 9	0.655 4	0.408 3	0.000 11	0.296 3	0.000 1	0.686 14	0.645 13	0.145 9	0.022 7	0.414 13	0.633 11	0.000 1	0.637 1	0.224 2	0.000 7	0.000 1	0.650 7	0.000 2	0.000 1	0.622 8	0.535 11	0.343 11	0.483 3	0.230 12	0.943 10	0.289 9	0.618 6	0.596 4	0.140 9	0.679 7	0.000 1	0.022 6	0.783 10	0.620 11	0.906 1	0.000 1	0.806 8	0.137 10	0.865 4	0.000 1	0.378 12	0.000 14	0.168 14	0.680 7	0.227 13
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
OA-CNN-L_ScanNet200	0.333 10	0.558 4	0.269 8	0.124 12	0.448 14	0.080 8	0.272 4	0.000 3	0.000 6	0.000 1	0.342 7	0.515 2	0.524 7	0.713 12	0.789 9	0.158 11	0.384 11	0.000 3	0.806 5	0.125 6	0.000 9	0.496 7	0.332 6	0.498 13	0.227 7	0.024 6	0.474 2	0.000 1	0.003 2	0.071 8	0.487 3	0.000 10	0.000 1	0.110 7	0.000 2	0.876 7	0.013 16	0.703 3	0.000 7	0.076 8	0.473 12	0.355 11	0.906 6	0.000 1	0.000 2	0.476 5	0.706 1	0.000 10	0.672 9	0.835 12	0.748 8	0.015 13	0.223 6	0.860 10	0.000 1	0.000 10	0.572 6	0.000 7	0.509 6	0.313 7	0.662 4	0.398 13	0.396 7	0.411 11	0.276 2	0.527 3	0.711 5	0.000 8	0.076 12	0.946 6	0.166 5	0.000 1	0.022 10	0.160 6	0.183 12	0.493 12	0.699 8	0.637 5	0.403 4	0.330 12	0.406 12	0.526 5	0.024 2	0.000 1	0.392 10	0.000 10	0.016 15	0.000 11	0.196 3	0.915 6	0.112 11	0.557 10	0.197 5	0.352 9	0.877 3	0.000 11	0.000 1	0.592 11	0.103 9	0.000 12	0.067 1	0.000 1	0.089 6	0.735 7	0.625 11	0.130 9	0.568 6	0.836 7	0.271 7	0.534 8	0.043 12	0.799 10	0.001 4	0.445 5	0.000 1	0.000 7	0.024 2	0.661 4	0.000 4	0.262 2	0.000 1	0.591 7	0.517 12	0.373 8	0.788 6	0.021 7	0.000 1	0.455 3	0.517 8	0.320 8	0.823 11	0.200 16	0.001 16	0.150 5	0.100 11	0.000 1	0.736 8	0.668 9	0.103 14	0.052 5	0.662 2	0.720 8	0.000 1	0.602 5	0.112 6	0.002 6	0.000 1	0.637 8	0.000 2	0.000 1	0.621 9	0.569 4	0.398 8	0.412 6	0.234 11	0.949 6	0.363 4	0.492 14	0.495 10	0.251 4	0.665 8	0.000 1	0.001 11	0.805 7	0.833 6	0.794 10	0.000 1	0.821 5	0.314 5	0.843 10	0.000 1	0.560 10	0.245 5	0.262 5	0.713 4	0.370 10

L3DETR-ScanNet_200	0.336 7	0.533 10	0.279 5	0.155 9	0.508 5	0.073 10	0.101 16	0.000 3	0.058 5	0.000 1	0.294 13	0.233 13	0.548 4	0.927 1	0.788 10	0.264 2	0.463 10	0.000 3	0.638 11	0.098 12	0.014 7	0.411 11	0.226 12	0.525 10	0.225 8	0.010 7	0.397 5	0.000 1	0.000 3	0.192 5	0.380 13	0.598 4	0.000 1	0.117 5	0.000 2	0.883 5	0.082 7	0.689 4	0.000 7	0.032 16	0.549 5	0.417 5	0.910 5	0.000 1	0.000 2	0.448 8	0.613 9	0.000 10	0.697 6	0.960 2	0.759 4	0.158 2	0.293 2	0.883 6	0.000 1	0.312 3	0.583 3	0.079 4	0.422 10	0.068 16	0.660 7	0.418 7	0.298 11	0.430 10	0.114 10	0.526 4	0.776 3	0.051 3	0.679 1	0.946 6	0.152 6	0.000 1	0.183 8	0.000 14	0.211 6	0.511 9	0.409 15	0.565 11	0.355 7	0.448 8	0.512 4	0.557 2	0.000 3	0.000 1	0.420 8	0.000 10	0.007 16	0.104 6	0.000 4	0.125 16	0.330 2	0.514 14	0.146 11	0.321 12	0.860 8	0.174 9	0.000 1	0.629 5	0.075 12	0.000 12	0.000 4	0.000 1	0.002 9	0.671 8	0.712 7	0.141 6	0.339 11	0.856 4	0.261 11	0.529 9	0.067 8	0.835 5	0.000 5	0.369 11	0.000 1	0.259 2	0.000 3	0.629 5	0.000 4	0.487 1	0.000 1	0.579 10	0.646 3	0.107 16	0.720 10	0.122 6	0.000 1	0.333 13	0.505 9	0.303 9	0.908 3	0.503 14	0.565 2	0.074 8	0.324 2	0.000 1	0.740 7	0.661 10	0.109 13	0.000 9	0.427 12	0.563 16	0.000 1	0.579 10	0.108 7	0.000 7	0.000 1	0.664 5	0.000 2	0.000 1	0.641 7	0.539 10	0.416 6	0.515 2	0.256 10	0.940 12	0.312 5	0.209 16	0.620 3	0.138 11	0.636 10	0.000 1	0.000 12	0.775 12	0.861 5	0.765 11	0.000 1	0.801 9	0.119 11	0.860 7	0.000 1	0.687 2	0.001 13	0.192 13	0.679 8	0.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
AWCS	0.305 13	0.508 13	0.225 13	0.142 10	0.463 13	0.063 12	0.195 8	0.000 3	0.000 6	0.000 1	0.467 3	0.551 1	0.504 8	0.773 6	0.764 13	0.142 13	0.029 16	0.000 3	0.626 12	0.100 10	0.000 9	0.360 12	0.179 14	0.507 12	0.137 14	0.006 8	0.300 11	0.000 1	0.000 3	0.172 7	0.364 14	0.512 7	0.000 1	0.056 13	0.000 2	0.865 13	0.093 4	0.634 16	0.000 7	0.071 12	0.396 14	0.296 15	0.876 8	0.000 1	0.000 2	0.373 13	0.436 15	0.063 9	0.749 2	0.877 9	0.721 11	0.131 4	0.124 13	0.804 14	0.000 1	0.000 10	0.515 11	0.010 6	0.452 9	0.252 9	0.578 13	0.417 8	0.179 16	0.484 8	0.171 7	0.337 13	0.606 11	0.000 8	0.115 9	0.937 13	0.142 8	0.000 1	0.008 11	0.000 14	0.157 15	0.484 13	0.402 16	0.501 14	0.339 8	0.553 7	0.529 2	0.478 12	0.000 3	0.000 1	0.404 9	0.001 9	0.022 11	0.077 9	0.000 4	0.894 12	0.219 6	0.628 7	0.093 14	0.305 13	0.886 1	0.233 7	0.000 1	0.603 8	0.112 5	0.023 9	0.000 4	0.000 1	0.000 10	0.741 6	0.664 8	0.097 14	0.253 13	0.782 10	0.264 10	0.523 10	0.154 1	0.707 15	0.000 5	0.411 7	0.000 1	0.000 7	0.000 3	0.332 15	0.000 4	0.000 7	0.000 1	0.602 6	0.595 9	0.185 12	0.656 15	0.159 5	0.000 1	0.355 10	0.424 14	0.154 14	0.729 14	0.516 11	0.220 9	0.620 3	0.084 12	0.000 1	0.707 13	0.651 12	0.173 5	0.014 8	0.381 16	0.582 14	0.000 1	0.619 2	0.049 11	0.000 7	0.000 1	0.702 4	0.000 2	0.000 1	0.302 15	0.489 14	0.317 12	0.334 8	0.392 7	0.922 13	0.254 12	0.533 13	0.394 12	0.129 14	0.613 14	0.000 1	0.000 12	0.820 5	0.649 10	0.749 12	0.000 1	0.782 13	0.282 6	0.863 5	0.000 1	0.288 15	0.006 10	0.220 10	0.633 13	0.542 3

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

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

CeCo	0.340 6	0.551 8	0.247 12	0.181 5	0.475 12	0.057 14	0.142 11	0.000 3	0.000 6	0.000 1	0.387 5	0.463 5	0.499 9	0.924 2	0.774 11	0.213 5	0.257 12	0.000 3	0.546 14	0.100 10	0.006 8	0.615 1	0.177 16	0.534 7	0.246 5	0.000 9	0.400 4	0.000 1	0.338 1	0.006 15	0.484 4	0.609 3	0.000 1	0.083 10	0.000 2	0.873 9	0.089 5	0.661 13	0.000 7	0.048 14	0.560 3	0.408 6	0.892 7	0.000 1	0.000 2	0.586 1	0.616 8	0.000 10	0.692 7	0.900 7	0.721 11	0.162 1	0.228 5	0.860 10	0.000 1	0.000 10	0.575 4	0.083 3	0.550 3	0.347 4	0.624 12	0.410 10	0.360 8	0.740 2	0.109 11	0.321 14	0.660 7	0.000 8	0.121 8	0.939 12	0.143 7	0.000 1	0.400 1	0.003 12	0.190 10	0.564 5	0.652 9	0.615 10	0.421 3	0.304 13	0.579 1	0.547 4	0.000 3	0.000 1	0.296 13	0.000 10	0.030 8	0.096 7	0.000 4	0.916 5	0.037 12	0.551 11	0.171 8	0.376 7	0.865 6	0.286 3	0.000 1	0.633 4	0.102 10	0.027 8	0.011 3	0.000 1	0.000 10	0.474 13	0.742 5	0.133 7	0.311 12	0.824 8	0.242 12	0.503 13	0.068 7	0.828 8	0.000 5	0.429 6	0.000 1	0.063 4	0.000 3	0.781 1	0.000 4	0.000 7	0.000 1	0.665 3	0.633 6	0.450 5	0.818 2	0.000 8	0.000 1	0.429 4	0.532 7	0.226 12	0.825 10	0.510 12	0.377 4	0.709 2	0.079 13	0.000 1	0.753 5	0.683 7	0.102 15	0.063 4	0.401 15	0.620 13	0.000 1	0.619 2	0.000 13	0.000 7	0.000 1	0.595 12	0.000 2	0.000 1	0.345 13	0.564 5	0.411 7	0.603 1	0.384 8	0.945 9	0.266 10	0.643 5	0.367 13	0.304 1	0.663 9	0.000 1	0.010 7	0.726 14	0.767 7	0.898 3	0.000 1	0.784 12	0.435 1	0.861 6	0.000 1	0.447 11	0.000 14	0.257 6	0.656 10	0.377 9
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
LGround	0.272 14	0.485 14	0.184 14	0.106 14	0.476 11	0.077 9	0.218 7	0.000 3	0.000 6	0.000 1	0.547 1	0.295 10	0.540 5	0.746 9	0.745 14	0.058 15	0.112 15	0.005 1	0.658 10	0.077 14	0.000 9	0.322 13	0.178 15	0.512 11	0.190 12	0.199 2	0.277 14	0.000 1	0.000 3	0.173 6	0.399 11	0.000 10	0.000 1	0.039 15	0.000 2	0.858 14	0.085 6	0.676 11	0.002 5	0.103 5	0.498 7	0.323 13	0.703 11	0.000 1	0.000 2	0.296 14	0.549 11	0.216 1	0.702 5	0.768 13	0.718 13	0.028 10	0.092 15	0.786 15	0.000 1	0.000 10	0.453 15	0.022 5	0.251 16	0.252 9	0.572 14	0.348 14	0.321 10	0.514 6	0.063 13	0.279 15	0.552 14	0.000 8	0.019 15	0.932 14	0.132 15	0.000 1	0.000 12	0.000 14	0.156 16	0.457 14	0.623 11	0.518 13	0.265 15	0.358 11	0.381 14	0.395 14	0.000 3	0.000 1	0.127 16	0.012 7	0.051 1	0.000 11	0.000 4	0.886 13	0.014 13	0.437 16	0.179 7	0.244 14	0.826 14	0.000 11	0.000 1	0.599 9	0.136 1	0.085 3	0.000 4	0.000 1	0.000 10	0.565 12	0.612 13	0.143 5	0.207 14	0.566 14	0.232 14	0.446 14	0.127 2	0.708 14	0.000 5	0.384 8	0.000 1	0.000 7	0.000 3	0.402 13	0.000 4	0.059 5	0.000 1	0.525 14	0.566 10	0.229 11	0.659 14	0.000 8	0.000 1	0.265 14	0.446 13	0.147 15	0.720 16	0.597 8	0.066 13	0.000 11	0.187 8	0.000 1	0.726 12	0.467 16	0.134 12	0.000 9	0.413 14	0.629 12	0.000 1	0.363 15	0.055 9	0.022 3	0.000 1	0.626 10	0.000 2	0.000 1	0.323 14	0.479 16	0.154 15	0.117 14	0.028 15	0.901 14	0.243 14	0.415 15	0.295 16	0.143 7	0.610 15	0.000 1	0.000 12	0.777 11	0.397 16	0.324 15	0.000 1	0.778 14	0.179 8	0.702 15	0.000 1	0.274 16	0.404 3	0.233 9	0.622 14	0.398 7
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrain	0.249 16	0.455 16	0.171 15	0.079 16	0.418 15	0.059 13	0.186 9	0.000 3	0.000 6	0.000 1	0.335 9	0.250 12	0.316 15	0.766 7	0.697 16	0.142 13	0.170 13	0.003 2	0.553 13	0.112 8	0.097 1	0.201 15	0.186 13	0.476 14	0.081 15	0.000 9	0.216 16	0.000 1	0.000 3	0.001 16	0.314 16	0.000 10	0.000 1	0.055 14	0.000 2	0.832 16	0.094 3	0.659 14	0.002 5	0.076 8	0.310 16	0.293 16	0.664 13	0.000 1	0.000 2	0.175 16	0.634 6	0.130 2	0.552 16	0.686 16	0.700 16	0.076 7	0.110 14	0.770 16	0.000 1	0.000 10	0.430 16	0.000 7	0.319 14	0.166 14	0.542 16	0.327 15	0.205 15	0.332 13	0.052 14	0.375 12	0.444 16	0.000 8	0.012 16	0.930 16	0.203 2	0.000 1	0.000 12	0.046 11	0.175 13	0.413 15	0.592 13	0.471 15	0.299 14	0.152 16	0.340 15	0.247 16	0.000 3	0.000 1	0.225 14	0.058 3	0.037 3	0.000 11	0.207 2	0.862 15	0.014 13	0.548 12	0.033 15	0.233 15	0.816 15	0.000 11	0.000 1	0.542 14	0.123 4	0.121 1	0.019 2	0.000 1	0.000 10	0.463 15	0.454 16	0.045 16	0.128 16	0.557 15	0.235 13	0.441 15	0.063 9	0.484 16	0.000 5	0.308 16	0.000 1	0.000 7	0.000 3	0.318 16	0.000 4	0.000 7	0.000 1	0.545 13	0.543 11	0.164 13	0.734 8	0.000 8	0.000 1	0.215 16	0.371 15	0.198 13	0.743 13	0.205 15	0.062 14	0.000 11	0.079 13	0.000 1	0.683 15	0.547 15	0.142 10	0.000 9	0.441 10	0.579 15	0.000 1	0.464 14	0.098 8	0.041 1	0.000 1	0.590 13	0.000 2	0.000 1	0.373 12	0.494 13	0.174 14	0.105 15	0.001 16	0.895 15	0.222 15	0.537 12	0.307 15	0.180 6	0.625 13	0.000 1	0.000 12	0.591 16	0.609 13	0.398 14	0.000 1	0.766 16	0.014 15	0.638 16	0.000 1	0.377 13	0.004 12	0.206 12	0.609 16	0.465 5
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34D	0.253 15	0.463 15	0.154 16	0.102 15	0.381 16	0.084 7	0.134 14	0.000 3	0.000 6	0.000 1	0.386 6	0.141 16	0.279 16	0.737 11	0.703 15	0.014 16	0.164 14	0.000 3	0.663 9	0.092 13	0.000 9	0.224 14	0.291 10	0.531 8	0.056 16	0.000 9	0.242 15	0.000 1	0.000 3	0.013 14	0.331 15	0.000 10	0.000 1	0.035 16	0.001 1	0.858 14	0.059 12	0.650 15	0.000 7	0.056 13	0.353 15	0.299 14	0.670 12	0.000 1	0.000 2	0.284 15	0.484 14	0.071 8	0.594 15	0.720 15	0.710 14	0.027 11	0.068 16	0.813 13	0.000 1	0.005 9	0.492 13	0.164 1	0.274 15	0.111 15	0.571 15	0.307 16	0.293 12	0.307 16	0.150 8	0.163 16	0.531 15	0.002 7	0.545 4	0.932 14	0.093 16	0.000 1	0.000 12	0.002 13	0.159 14	0.368 16	0.581 14	0.440 16	0.228 16	0.406 9	0.282 16	0.294 15	0.000 3	0.000 1	0.189 15	0.060 2	0.036 4	0.000 11	0.000 4	0.897 11	0.000 16	0.525 13	0.025 16	0.205 16	0.771 16	0.000 11	0.000 1	0.593 10	0.108 8	0.044 6	0.000 4	0.000 1	0.000 10	0.282 16	0.589 14	0.094 15	0.169 15	0.466 16	0.227 15	0.419 16	0.125 3	0.757 13	0.002 3	0.334 15	0.000 1	0.000 7	0.000 3	0.357 14	0.000 4	0.000 7	0.000 1	0.582 9	0.513 13	0.337 10	0.612 16	0.000 8	0.000 1	0.250 15	0.352 16	0.136 16	0.724 15	0.655 4	0.280 8	0.000 11	0.046 15	0.000 1	0.606 16	0.559 14	0.159 7	0.102 2	0.445 9	0.655 9	0.000 1	0.310 16	0.117 5	0.000 7	0.000 1	0.581 14	0.026 1	0.000 1	0.265 16	0.483 15	0.084 16	0.097 16	0.044 14	0.865 16	0.142 16	0.588 11	0.351 14	0.272 2	0.596 16	0.000 1	0.003 10	0.622 15	0.720 9	0.096 16	0.000 1	0.771 15	0.016 14	0.772 14	0.000 1	0.302 14	0.194 7	0.214 11	0.621 15	0.197 16
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019

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 1	0.237 1	0.610 1	0.091 1	0.125 5	0.000 1	0.000 1	0.000 1	0.065 3	0.668 1	0.451 1	1.000 1	0.955 1	0.640 1	0.500 1	0.039 1	0.125 2	0.063 2	0.409 1	0.311 2	0.291 1	0.609 3	0.266 1	0.000 1	0.163 1	0.000 1	0.008 1	0.044 2	0.496 1	1.000 1	0.000 1	0.018 2	0.000 1	0.756 1	0.573 1	0.808 2	0.000 1	0.010 1	0.042 3	0.130 3	0.552 1	0.042 1	0.000 1	1.000 1	0.725 4	0.750 1	0.883 1	1.000 1	0.832 4	0.024 2	0.107 1	0.614 3	0.226 1	0.250 1	0.628 2	0.792 1	0.677 2	0.400 1	0.741 1	0.278 1	0.511 1	0.077 5	0.111 1	0.313 2	0.715 2	0.302 1	0.017 3	0.200 2	0.000 1	0.188 1	0.000 1	0.178 2	0.736 1	1.000 1	0.615 1	0.514 1	0.409 2	0.380 5	0.600 1	0.000 1	0.000 1	0.400 1	0.013 2	0.254 1	0.381 1	0.000 1	0.123 4	0.400 1	0.839 1	0.258 1	0.463 1	0.926 1	0.265 1	0.000 1	0.857 2	0.099 1	0.021 2	0.500 1	0.027 1	0.028 1	1.000 1	0.502 5	0.016 1	0.076 4	0.500 1	0.612 1	0.578 1	0.005 2	0.597 2	0.194 1	0.497 1	0.000 1	0.500 1	0.000 2	0.323 4	0.000 1	1.000 1	0.000 1	0.748 1	0.708 2	0.050 4	0.890 2	1.000 1	0.008 2	0.151 3	0.301 1	1.000 1	1.000 1	0.792 3	0.945 1	1.000 1	0.511 1	0.004 2	0.753 1	0.776 2	0.287 2	0.020 2	0.003 4	0.974 3	0.033 1	0.412 5	0.000 1	0.000 2	0.000 2	0.667 1	0.000 1	0.000 1	0.491 1	0.676 2	0.352 1	0.335 1	0.060 2	0.822 5	0.527 2	1.000 1	0.517 1	0.606 1	0.853 1	0.000 1	0.004 1	0.806 1	1.000 1	0.727 1	0.000 1	0.042 2	0.739 2	0.000 1	0.399 3	0.391 1	0.504 1	0.591 1	0.571 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
TD3D Scannet200	0.320 2	0.501 2	0.264 2	0.164 2	0.506 3	0.062 2	0.500 1	0.000 1	0.000 1	0.000 1	0.208 1	0.431 2	0.252 3	1.000 1	0.733 3	0.587 2	0.000 2	0.008 2	0.000 3	0.106 1	0.000 2	0.356 1	0.123 4	0.686 1	0.101 2	0.000 1	0.152 2	0.000 1	0.000 2	0.226 1	0.280 3	0.000 2	0.000 1	0.250 1	0.000 1	0.619 2	0.061 3	0.841 1	0.000 1	0.000 2	0.167 1	0.194 1	0.333 2	0.000 2	0.000 1	0.667 2	0.820 1	0.250 3	0.790 4	1.000 1	0.879 2	0.077 1	0.094 3	0.708 1	0.217 2	0.049 2	0.634 1	0.792 1	0.331 4	0.033 5	0.716 2	0.159 2	0.396 2	0.331 4	0.099 2	0.415 1	0.842 1	0.000 2	0.458 1	0.542 1	0.000 1	0.101 2	0.000 1	0.218 1	0.513 2	0.500 2	0.458 2	0.104 2	0.516 1	0.456 1	0.268 4	0.000 1	0.000 1	0.400 1	0.022 1	0.233 2	0.143 2	0.000 1	0.677 1	0.400 1	0.504 5	0.095 3	0.083 5	0.890 2	0.061 2	0.000 1	0.906 1	0.076 2	0.231 1	0.125 2	0.000 2	0.003 2	0.792 3	0.881 1	0.000 2	0.098 3	0.125 4	0.498 5	0.459 2	0.063 1	0.715 1	0.000 2	0.241 4	0.000 1	0.396 2	0.063 1	0.605 1	0.000 1	0.000 2	0.000 1	0.448 5	0.629 3	0.202 2	0.967 1	0.250 2	0.038 1	0.192 1	0.185 2	0.083 4	1.000 1	1.000 1	0.857 2	0.000 2	0.470 2	0.012 1	0.565 3	0.798 1	0.621 1	0.111 1	0.500 1	1.000 1	0.017 2	0.509 1	0.000 1	0.008 1	1.000 1	0.525 2	0.000 1	0.000 1	0.332 3	0.679 1	0.264 2	0.333 2	0.267 1	1.000 1	0.549 1	0.299 5	0.387 2	0.328 3	0.744 4	0.000 1	0.000 2	0.435 5	1.000 1	0.283 4	0.000 1	0.196 1	0.817 1	0.000 1	0.472 1	0.222 3	0.123 4	0.560 2	0.156 2
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
LGround Inst.	0.246 3	0.413 3	0.170 3	0.130 3	0.455 5	0.003 5	0.500 1	0.000 1	0.000 1	0.000 1	0.017 4	0.333 4	0.111 5	1.000 1	0.681 4	0.400 3	0.000 2	0.000 3	1.000 1	0.003 5	0.000 2	0.167 3	0.190 2	0.637 2	0.067 3	0.000 1	0.081 3	0.000 1	0.000 2	0.000 3	0.264 4	0.000 2	0.000 1	0.000 3	0.000 1	0.387 4	0.031 5	0.754 3	0.000 1	0.000 2	0.151 2	0.135 2	0.056 4	0.000 2	0.000 1	0.582 4	0.589 5	0.500 2	0.815 2	1.000 1	0.903 1	0.000 3	0.097 2	0.588 4	0.000 3	0.000 3	0.234 3	0.000 3	0.500 3	0.400 1	0.682 4	0.156 3	0.159 4	0.750 1	0.046 3	0.125 4	0.660 3	0.000 2	0.200 2	0.000 5	0.000 1	0.000 3	0.000 1	0.164 3	0.402 3	0.500 2	0.373 3	0.025 3	0.143 5	0.426 3	0.317 2	0.000 1	0.000 1	0.000 3	0.000 3	0.063 3	0.000 3	0.000 1	0.000 5	0.000 4	0.575 3	0.250 2	0.241 2	0.772 3	0.000 3	0.000 1	0.653 4	0.034 3	0.000 3	0.000 3	0.000 2	0.000 3	1.000 1	0.561 4	0.000 2	0.100 2	0.500 1	0.541 4	0.452 3	0.000 3	0.581 3	0.000 2	0.364 2	0.000 1	0.000 3	0.000 2	0.571 2	0.000 1	0.000 2	0.000 1	0.568 4	0.511 4	0.167 3	0.857 3	0.000 3	0.000 3	0.164 2	0.112 3	0.000 5	0.530 5	1.000 1	0.286 3	0.000 2	0.125 3	0.000 3	0.464 5	0.706 3	0.208 4	0.000 3	0.125 2	0.744 4	0.000 3	0.500 2	0.000 1	0.000 2	0.000 2	0.511 3	0.000 1	0.000 1	0.344 2	0.541 3	0.068 3	0.333 2	0.000 3	1.000 1	0.196 4	0.533 3	0.318 3	0.000 4	0.748 3	0.000 1	0.000 2	0.690 2	1.000 1	0.400 3	0.000 1	0.000 3	0.667 3	0.000 1	0.333 4	0.333 2	0.270 3	0.399 3	0.083 4
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.	0.203 5	0.369 4	0.134 5	0.078 5	0.479 4	0.003 4	0.500 1	0.000 1	0.000 1	0.000 1	0.100 2	0.371 3	0.300 2	0.667 4	0.746 2	0.400 3	0.000 2	0.000 3	0.000 3	0.031 3	0.000 2	0.074 4	0.165 3	0.413 5	0.000 4	0.000 1	0.070 4	0.000 1	0.000 2	0.000 3	0.221 5	0.000 2	0.000 1	0.000 3	0.000 1	0.372 5	0.070 2	0.706 4	0.000 1	0.000 2	0.000 5	0.123 4	0.033 5	0.000 2	0.000 1	0.422 5	0.732 3	0.000 4	0.778 5	1.000 1	0.845 3	0.000 3	0.090 4	0.636 2	0.000 3	0.000 3	0.158 4	0.000 3	0.250 5	0.050 4	0.693 3	0.123 4	0.051 5	0.385 3	0.009 4	0.118 5	0.406 5	0.000 2	0.000 4	0.200 2	0.000 1	0.000 3	0.000 1	0.133 4	0.307 5	0.500 2	0.251 4	0.000 4	0.281 3	0.402 4	0.317 2	0.000 1	0.000 1	0.000 3	0.000 3	0.060 4	0.000 3	0.000 1	0.396 2	0.200 3	0.669 2	0.021 4	0.218 4	0.720 5	0.000 3	0.000 1	0.696 3	0.025 4	0.000 3	0.000 3	0.000 2	0.000 3	0.125 5	0.596 2	0.000 2	0.191 1	0.500 1	0.595 2	0.369 4	0.000 3	0.500 4	0.000 2	0.143 5	0.000 1	0.000 3	0.000 2	0.226 5	0.000 1	0.000 2	0.000 1	0.701 2	0.511 4	0.000 5	0.851 4	0.000 3	0.000 3	0.150 4	0.052 5	0.100 3	0.981 3	0.500 4	0.286 3	0.000 2	0.000 5	0.000 3	0.545 4	0.522 5	0.250 3	0.000 3	0.000 5	0.522 5	0.000 3	0.500 2	0.000 1	0.000 2	0.000 2	0.282 5	0.000 1	0.000 1	0.178 5	0.382 4	0.018 5	0.056 4	0.000 3	0.997 3	0.107 5	0.677 2	0.313 4	0.000 4	0.726 5	0.000 1	0.000 2	0.583 4	0.903 4	0.200 5	0.000 1	0.000 3	0.333 4	0.000 1	0.442 2	0.083 4	0.109 5	0.387 4	0.000 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.	0.209 4	0.361 5	0.157 4	0.085 4	0.506 2	0.007 3	0.500 1	0.000 1	0.000 1	0.000 1	0.000 5	0.093 5	0.221 4	0.667 4	0.524 5	0.400 3	0.000 2	0.000 3	0.000 3	0.004 4	0.000 2	0.000 5	0.109 5	0.589 4	0.000 4	0.000 1	0.059 5	0.000 1	0.000 2	0.000 3	0.322 2	0.000 2	0.000 1	0.000 3	0.000 1	0.405 3	0.055 4	0.700 5	0.000 1	0.000 2	0.028 4	0.091 5	0.083 3	0.000 2	0.000 1	0.667 2	0.768 2	0.000 4	0.807 3	1.000 1	0.776 5	0.000 3	0.000 5	0.340 5	0.000 3	0.000 3	0.103 5	0.000 3	0.750 1	0.200 3	0.634 5	0.053 5	0.246 3	0.677 2	0.006 5	0.198 3	0.432 4	0.000 2	0.000 4	0.050 4	0.000 1	0.000 3	0.000 1	0.111 5	0.356 4	0.500 2	0.188 5	0.000 4	0.220 4	0.448 2	0.050 5	0.000 1	0.000 1	0.000 3	0.000 3	0.032 5	0.000 3	0.000 1	0.396 2	0.000 4	0.573 4	0.000 5	0.228 3	0.747 4	0.000 3	0.000 1	0.573 5	0.021 5	0.000 3	0.000 3	0.000 2	0.000 3	0.500 4	0.573 3	0.000 2	0.000 5	0.125 4	0.592 3	0.364 5	0.000 3	0.450 5	0.000 2	0.364 2	0.000 1	0.000 3	0.000 2	0.340 3	0.000 1	0.000 2	0.000 1	0.610 3	0.833 1	0.221 1	0.702 5	0.000 3	0.000 3	0.135 5	0.094 4	0.125 2	0.571 4	0.500 4	0.143 5	0.000 2	0.125 3	0.000 3	0.618 2	0.667 4	0.115 5	0.000 3	0.125 2	1.000 1	0.000 3	0.500 2	0.000 1	0.000 2	0.000 2	0.502 4	0.000 1	0.000 1	0.312 4	0.248 5	0.050 4	0.000 5	0.000 3	0.997 3	0.420 3	0.500 4	0.149 5	0.451 2	0.748 2	0.000 1	0.000 2	0.636 3	0.667 5	0.600 2	0.000 1	0.000 3	0.278 5	0.000 1	0.333 4	0.000 5	0.294 2	0.381 5	0.110 3
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021

ScanNet Benchmark

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

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

PTv3-PPT-ALC	0.798 1	0.911 10	0.812 21	0.854 7	0.770 13	0.856 15	0.555 15	0.943 1	0.660 25	0.735 2	0.979 1	0.606 7	0.492 1	0.792 4	0.934 3	0.841 2	0.819 5	0.716 8	0.947 10	0.906 1	0.822 1

PTv3 ScanNet	0.794 2	0.941 3	0.813 20	0.851 10	0.782 6	0.890 3	0.597 1	0.916 5	0.696 9	0.713 5	0.979 1	0.635 2	0.384 3	0.793 3	0.907 10	0.821 5	0.790 34	0.696 14	0.967 3	0.903 2	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)
DITR ScanNet	0.793 3	0.811 40	0.852 2	0.889 1	0.774 10	0.907 1	0.592 2	0.927 3	0.719 1	0.718 3	0.961 17	0.652 1	0.348 12	0.817 1	0.927 5	0.795 10	0.824 2	0.749 1	0.948 9	0.887 7	0.771 11

PonderV2	0.785 4	0.978 1	0.800 30	0.833 27	0.788 4	0.853 20	0.545 19	0.910 8	0.713 2	0.705 6	0.979 1	0.596 9	0.390 2	0.769 16	0.832 44	0.821 5	0.792 33	0.730 2	0.975 1	0.897 5	0.785 6
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 1	0.843 19	0.781 7	0.858 13	0.575 7	0.831 37	0.685 15	0.714 4	0.979 1	0.594 10	0.310 30	0.801 2	0.892 18	0.841 2	0.819 5	0.723 5	0.940 15	0.887 7	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 22	0.818 15	0.836 24	0.790 3	0.875 5	0.576 6	0.905 9	0.704 6	0.739 1	0.969 11	0.611 3	0.349 11	0.756 26	0.958 1	0.702 50	0.805 17	0.708 9	0.916 37	0.898 4	0.801 3

TTT-KD	0.773 7	0.646 96	0.818 15	0.809 39	0.774 10	0.878 4	0.581 3	0.943 1	0.687 13	0.704 7	0.978 5	0.607 6	0.336 18	0.775 12	0.912 8	0.838 4	0.823 3	0.694 15	0.967 3	0.899 3	0.794 5
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 7	0.771 12	0.840 35	0.564 11	0.900 11	0.686 14	0.677 14	0.961 17	0.537 35	0.348 12	0.769 16	0.903 12	0.785 14	0.815 8	0.676 26	0.939 16	0.880 13	0.772 10

OctFormer	0.766 9	0.925 7	0.808 26	0.849 12	0.786 5	0.846 30	0.566 10	0.876 19	0.690 11	0.674 16	0.960 19	0.576 21	0.226 71	0.753 28	0.904 11	0.777 16	0.815 8	0.722 6	0.923 32	0.877 16	0.776 9
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 36	0.829 29	0.751 26	0.854 18	0.540 23	0.903 10	0.630 38	0.672 18	0.963 15	0.565 25	0.357 9	0.788 5	0.900 14	0.737 30	0.802 18	0.685 20	0.950 7	0.887 7	0.780 7
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
OccuSeg+Semantic	0.764 11	0.758 61	0.796 34	0.839 22	0.746 30	0.907 1	0.562 12	0.850 29	0.680 17	0.672 18	0.978 5	0.610 4	0.335 20	0.777 10	0.819 48	0.847 1	0.830 1	0.691 17	0.972 2	0.885 10	0.727 26

CU-Hybrid Net	0.764 11	0.924 8	0.819 13	0.840 21	0.757 21	0.853 20	0.580 4	0.848 30	0.709 4	0.643 28	0.958 23	0.587 15	0.295 38	0.753 28	0.884 22	0.758 23	0.815 8	0.725 4	0.927 28	0.867 26	0.743 19

O-CNN	0.762 13	0.924 8	0.823 8	0.844 18	0.770 13	0.852 22	0.577 5	0.847 32	0.711 3	0.640 32	0.958 23	0.592 11	0.217 77	0.762 21	0.888 19	0.758 23	0.813 12	0.726 3	0.932 26	0.868 25	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 19	0.762 17	0.856 15	0.562 12	0.920 4	0.657 28	0.658 22	0.958 23	0.589 13	0.337 17	0.782 6	0.879 23	0.787 12	0.779 40	0.678 22	0.926 30	0.880 13	0.799 4

DTC	0.757 15	0.843 28	0.820 11	0.847 15	0.791 2	0.862 11	0.511 37	0.870 22	0.707 5	0.652 24	0.954 39	0.604 8	0.279 48	0.760 22	0.942 2	0.734 31	0.766 49	0.701 13	0.884 59	0.874 22	0.736 20

OA-CNN-L_ScanNet20	0.756 16	0.783 47	0.826 6	0.858 5	0.776 8	0.837 38	0.548 18	0.896 15	0.649 30	0.675 15	0.962 16	0.586 16	0.335 20	0.771 15	0.802 53	0.770 19	0.787 36	0.691 17	0.936 20	0.880 13	0.761 13

PNE	0.755 17	0.786 45	0.835 5	0.834 26	0.758 19	0.849 25	0.570 9	0.836 36	0.648 31	0.668 20	0.978 5	0.581 20	0.367 7	0.683 39	0.856 32	0.804 7	0.801 22	0.678 22	0.961 5	0.889 6	0.716 34
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer	0.755 17	0.927 6	0.822 9	0.836 24	0.801 1	0.849 25	0.516 34	0.864 26	0.651 29	0.680 13	0.958 23	0.584 18	0.282 45	0.759 24	0.855 34	0.728 33	0.802 18	0.678 22	0.880 64	0.873 23	0.756 15
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
DMF-Net	0.752 19	0.906 13	0.793 38	0.802 45	0.689 44	0.825 51	0.556 14	0.867 23	0.681 16	0.602 49	0.960 19	0.555 31	0.365 8	0.779 9	0.859 29	0.747 26	0.795 30	0.717 7	0.917 36	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
PointTransformerV2	0.752 19	0.742 68	0.809 25	0.872 2	0.758 19	0.860 12	0.552 16	0.891 17	0.610 45	0.687 8	0.960 19	0.559 29	0.304 33	0.766 19	0.926 6	0.767 20	0.797 26	0.644 37	0.942 13	0.876 19	0.722 30
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
BPNet	0.749 21	0.909 11	0.818 15	0.811 37	0.752 24	0.839 37	0.485 52	0.842 33	0.673 20	0.644 27	0.957 28	0.528 41	0.305 32	0.773 13	0.859 29	0.788 11	0.818 7	0.693 16	0.916 37	0.856 35	0.723 29
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer	0.749 21	0.793 43	0.790 39	0.807 41	0.750 28	0.856 15	0.524 30	0.881 18	0.588 57	0.642 31	0.977 9	0.591 12	0.274 51	0.781 8	0.929 4	0.804 7	0.796 27	0.642 38	0.947 10	0.885 10	0.715 35
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP	0.748 23	0.623 99	0.804 28	0.859 4	0.745 31	0.824 53	0.501 41	0.912 7	0.690 11	0.685 10	0.956 30	0.567 24	0.320 27	0.768 18	0.918 7	0.720 38	0.802 18	0.676 26	0.921 34	0.881 12	0.779 8

StratifiedFormer	0.747 24	0.901 14	0.803 29	0.845 17	0.757 21	0.846 30	0.512 36	0.825 40	0.696 9	0.645 26	0.956 30	0.576 21	0.262 62	0.744 34	0.861 28	0.742 28	0.770 47	0.705 11	0.899 49	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 25	0.771 55	0.819 13	0.848 14	0.702 42	0.865 10	0.397 89	0.899 12	0.699 7	0.664 21	0.948 60	0.588 14	0.330 22	0.746 33	0.851 38	0.764 21	0.796 27	0.704 12	0.935 21	0.866 27	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 25	0.870 20	0.838 3	0.858 5	0.729 36	0.850 24	0.501 41	0.874 20	0.587 58	0.658 22	0.956 30	0.564 26	0.299 35	0.765 20	0.900 14	0.716 41	0.812 13	0.631 43	0.939 16	0.858 33	0.709 36
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 27	0.725 78	0.814 19	0.837 23	0.751 26	0.831 45	0.514 35	0.896 15	0.674 19	0.684 11	0.960 19	0.564 26	0.303 34	0.773 13	0.820 47	0.713 44	0.798 25	0.690 19	0.923 32	0.875 20	0.757 14

Retro-FPN	0.744 28	0.842 29	0.800 30	0.767 59	0.740 32	0.836 40	0.541 21	0.914 6	0.672 21	0.626 37	0.958 23	0.552 32	0.272 53	0.777 10	0.886 21	0.696 51	0.801 22	0.674 29	0.941 14	0.858 33	0.717 32
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 29	0.620 100	0.799 33	0.849 12	0.730 35	0.822 55	0.493 49	0.897 13	0.664 22	0.681 12	0.955 33	0.562 28	0.378 4	0.760 22	0.903 12	0.738 29	0.801 22	0.673 30	0.907 41	0.877 16	0.745 16
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
MVF-GNN	0.743 29	0.731 73	0.810 24	0.726 66	0.775 9	0.843 33	0.528 29	0.897 13	0.679 18	0.674 16	0.954 39	0.583 19	0.322 26	0.782 6	0.720 68	0.802 9	0.785 37	0.707 10	0.935 21	0.863 29	0.745 16

SAT	0.742 31	0.860 23	0.765 54	0.819 32	0.769 15	0.848 27	0.533 25	0.829 38	0.663 23	0.631 36	0.955 33	0.586 16	0.274 51	0.753 28	0.896 16	0.729 32	0.760 55	0.666 32	0.921 34	0.855 37	0.733 22

LRPNet	0.742 31	0.816 37	0.806 27	0.807 41	0.752 24	0.828 49	0.575 7	0.839 35	0.699 7	0.637 34	0.954 39	0.520 44	0.320 27	0.755 27	0.834 42	0.760 22	0.772 44	0.676 26	0.915 39	0.862 30	0.717 32

LargeKernel3D	0.739 33	0.909 11	0.820 11	0.806 43	0.740 32	0.852 22	0.545 19	0.826 39	0.594 56	0.643 28	0.955 33	0.541 34	0.263 61	0.723 37	0.858 31	0.775 18	0.767 48	0.678 22	0.933 24	0.848 42	0.694 41
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 34	0.776 51	0.790 39	0.851 10	0.754 23	0.854 18	0.491 51	0.866 24	0.596 55	0.686 9	0.955 33	0.536 36	0.342 15	0.624 54	0.869 25	0.787 12	0.802 18	0.628 44	0.927 28	0.875 20	0.704 38

MinkowskiNet	0.736 34	0.859 24	0.818 15	0.832 28	0.709 40	0.840 35	0.521 32	0.853 28	0.660 25	0.643 28	0.951 50	0.544 33	0.286 43	0.731 35	0.893 17	0.675 59	0.772 44	0.683 21	0.874 70	0.852 40	0.727 26
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 36	0.890 16	0.837 4	0.864 3	0.726 37	0.873 6	0.530 28	0.824 41	0.489 91	0.647 25	0.978 5	0.609 5	0.336 18	0.624 54	0.733 62	0.758 23	0.776 42	0.570 69	0.949 8	0.877 16	0.728 24

online3d	0.727 37	0.715 83	0.777 47	0.854 7	0.748 29	0.858 13	0.497 46	0.872 21	0.572 64	0.639 33	0.957 28	0.523 42	0.297 37	0.750 31	0.803 52	0.744 27	0.810 14	0.587 65	0.938 18	0.871 24	0.719 31

SparseConvNet	0.725 38	0.647 95	0.821 10	0.846 16	0.721 38	0.869 7	0.533 25	0.754 62	0.603 51	0.614 41	0.955 33	0.572 23	0.325 24	0.710 38	0.870 24	0.724 36	0.823 3	0.628 44	0.934 23	0.865 28	0.683 44

PointTransformer++	0.725 38	0.727 77	0.811 23	0.819 32	0.765 16	0.841 34	0.502 40	0.814 46	0.621 41	0.623 39	0.955 33	0.556 30	0.284 44	0.620 56	0.866 26	0.781 15	0.757 59	0.648 35	0.932 26	0.862 30	0.709 36

MatchingNet	0.724 40	0.812 39	0.812 21	0.810 38	0.735 34	0.834 42	0.495 48	0.860 27	0.572 64	0.602 49	0.954 39	0.512 46	0.280 47	0.757 25	0.845 40	0.725 35	0.780 39	0.606 54	0.937 19	0.851 41	0.700 40

INS-Conv-semantic	0.717 41	0.751 64	0.759 58	0.812 36	0.704 41	0.868 8	0.537 24	0.842 33	0.609 47	0.608 45	0.953 44	0.534 38	0.293 39	0.616 57	0.864 27	0.719 40	0.793 31	0.640 39	0.933 24	0.845 46	0.663 49

PointMetaBase	0.714 42	0.835 30	0.785 42	0.821 30	0.684 46	0.846 30	0.531 27	0.865 25	0.614 42	0.596 53	0.953 44	0.500 49	0.246 67	0.674 40	0.888 19	0.692 52	0.764 51	0.624 46	0.849 86	0.844 47	0.675 46

contrastBoundary	0.705 43	0.769 58	0.775 48	0.809 39	0.687 45	0.820 58	0.439 77	0.812 47	0.661 24	0.591 55	0.945 68	0.515 45	0.171 96	0.633 51	0.856 32	0.720 38	0.796 27	0.668 31	0.889 56	0.847 43	0.689 42
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 44	0.774 53	0.800 30	0.793 50	0.760 18	0.847 29	0.471 56	0.802 50	0.463 98	0.634 35	0.968 13	0.491 52	0.271 55	0.726 36	0.910 9	0.706 46	0.815 8	0.551 81	0.878 65	0.833 48	0.570 81

RFCR	0.702 45	0.889 17	0.745 68	0.813 35	0.672 49	0.818 63	0.493 49	0.815 45	0.623 39	0.610 43	0.947 62	0.470 61	0.249 66	0.594 60	0.848 39	0.705 47	0.779 40	0.646 36	0.892 54	0.823 54	0.611 64
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 46	0.825 34	0.796 34	0.723 67	0.716 39	0.832 44	0.433 79	0.816 43	0.634 36	0.609 44	0.969 11	0.418 87	0.344 14	0.559 72	0.833 43	0.715 42	0.808 16	0.560 75	0.902 46	0.847 43	0.680 45

JSENet	0.699 47	0.881 19	0.762 55	0.821 30	0.667 50	0.800 75	0.522 31	0.792 53	0.613 43	0.607 46	0.935 88	0.492 51	0.205 82	0.576 65	0.853 36	0.691 53	0.758 57	0.652 34	0.872 73	0.828 51	0.649 53
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 48	0.743 67	0.794 36	0.655 90	0.684 46	0.822 55	0.497 46	0.719 72	0.622 40	0.617 40	0.977 9	0.447 74	0.339 16	0.750 31	0.664 80	0.703 49	0.790 34	0.596 58	0.946 12	0.855 37	0.647 54
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 49	0.732 72	0.772 49	0.786 51	0.677 48	0.866 9	0.517 33	0.848 30	0.509 84	0.626 37	0.952 48	0.536 36	0.225 73	0.545 78	0.704 71	0.689 56	0.810 14	0.564 74	0.903 45	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 50	0.884 18	0.754 62	0.795 48	0.647 57	0.818 63	0.422 81	0.802 50	0.612 44	0.604 47	0.945 68	0.462 64	0.189 90	0.563 71	0.853 36	0.726 34	0.765 50	0.632 42	0.904 43	0.821 57	0.606 68
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 51	0.704 85	0.741 72	0.754 63	0.656 52	0.829 47	0.501 41	0.741 67	0.609 47	0.548 62	0.950 54	0.522 43	0.371 5	0.633 51	0.756 57	0.715 42	0.771 46	0.623 47	0.861 81	0.814 60	0.658 50
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 52	0.866 21	0.748 65	0.819 32	0.645 59	0.794 78	0.450 67	0.802 50	0.587 58	0.604 47	0.945 68	0.464 63	0.201 85	0.554 74	0.840 41	0.723 37	0.732 70	0.602 56	0.907 41	0.822 56	0.603 71

DGNet	0.684 53	0.712 84	0.784 43	0.782 55	0.658 51	0.835 41	0.499 45	0.823 42	0.641 33	0.597 52	0.950 54	0.487 54	0.281 46	0.575 66	0.619 84	0.647 72	0.764 51	0.620 49	0.871 76	0.846 45	0.688 43

VACNN++	0.684 53	0.728 76	0.757 61	0.776 56	0.690 43	0.804 73	0.464 61	0.816 43	0.577 63	0.587 56	0.945 68	0.508 48	0.276 50	0.671 41	0.710 69	0.663 64	0.750 63	0.589 63	0.881 62	0.832 50	0.653 52

KP-FCNN	0.684 53	0.847 27	0.758 60	0.784 53	0.647 57	0.814 66	0.473 55	0.772 56	0.605 49	0.594 54	0.935 88	0.450 72	0.181 93	0.587 61	0.805 51	0.690 54	0.785 37	0.614 50	0.882 61	0.819 58	0.632 60
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
Superpoint Network	0.683 56	0.851 26	0.728 76	0.800 47	0.653 54	0.806 71	0.468 58	0.804 48	0.572 64	0.602 49	0.946 65	0.453 71	0.239 70	0.519 84	0.822 45	0.689 56	0.762 54	0.595 60	0.895 52	0.827 52	0.630 61

PointContrast_LA_SEM	0.683 56	0.757 62	0.784 43	0.786 51	0.639 61	0.824 53	0.408 84	0.775 55	0.604 50	0.541 64	0.934 92	0.532 39	0.269 57	0.552 75	0.777 55	0.645 75	0.793 31	0.640 39	0.913 40	0.824 53	0.671 47

VI-PointConv	0.676 58	0.770 57	0.754 62	0.783 54	0.621 65	0.814 66	0.552 16	0.758 60	0.571 67	0.557 60	0.954 39	0.529 40	0.268 59	0.530 81	0.682 75	0.675 59	0.719 73	0.603 55	0.888 57	0.833 48	0.665 48
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 59	0.789 44	0.748 65	0.763 61	0.635 63	0.814 66	0.407 86	0.747 64	0.581 62	0.573 57	0.950 54	0.484 55	0.271 55	0.607 58	0.754 58	0.649 69	0.774 43	0.596 58	0.883 60	0.823 54	0.606 68

SALANet	0.670 60	0.816 37	0.770 52	0.768 58	0.652 55	0.807 70	0.451 64	0.747 64	0.659 27	0.545 63	0.924 99	0.473 60	0.149 106	0.571 68	0.811 50	0.635 78	0.746 64	0.623 47	0.892 54	0.794 72	0.570 81

O3DSeg	0.668 61	0.822 35	0.771 51	0.496 110	0.651 56	0.833 43	0.541 21	0.761 59	0.555 73	0.611 42	0.966 14	0.489 53	0.370 6	0.388 103	0.580 87	0.776 17	0.751 61	0.570 69	0.956 6	0.817 59	0.646 55

PointConv	0.666 62	0.781 48	0.759 58	0.699 75	0.644 60	0.822 55	0.475 54	0.779 54	0.564 70	0.504 81	0.953 44	0.428 81	0.203 84	0.586 63	0.754 58	0.661 65	0.753 60	0.588 64	0.902 46	0.813 62	0.642 56
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 62	0.703 86	0.781 45	0.751 65	0.655 53	0.830 46	0.471 56	0.769 57	0.474 94	0.537 66	0.951 50	0.475 59	0.279 48	0.635 49	0.698 74	0.675 59	0.751 61	0.553 80	0.816 93	0.806 64	0.703 39
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 64	0.746 65	0.708 79	0.722 68	0.638 62	0.820 58	0.451 64	0.566 100	0.599 53	0.541 64	0.950 54	0.510 47	0.313 29	0.648 46	0.819 48	0.616 83	0.682 88	0.590 62	0.869 77	0.810 63	0.656 51
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net	0.658 65	0.778 49	0.702 82	0.806 43	0.619 66	0.813 69	0.468 58	0.693 80	0.494 87	0.524 73	0.941 80	0.449 73	0.298 36	0.510 86	0.821 46	0.675 59	0.727 72	0.568 72	0.826 91	0.803 66	0.637 58
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 66	0.698 88	0.743 70	0.650 91	0.564 83	0.820 58	0.505 39	0.758 60	0.631 37	0.479 85	0.945 68	0.480 57	0.226 71	0.572 67	0.774 56	0.690 54	0.735 68	0.614 50	0.853 85	0.776 87	0.597 74
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 67	0.752 63	0.734 74	0.664 88	0.583 78	0.815 65	0.399 88	0.754 62	0.639 34	0.535 68	0.942 78	0.470 61	0.309 31	0.665 42	0.539 90	0.650 68	0.708 78	0.635 41	0.857 84	0.793 74	0.642 56
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 68	0.778 49	0.731 75	0.699 75	0.577 79	0.829 47	0.446 69	0.736 68	0.477 93	0.523 75	0.945 68	0.454 68	0.269 57	0.484 93	0.749 61	0.618 81	0.738 65	0.599 57	0.827 90	0.792 77	0.621 63

PointConv-SFPN	0.641 69	0.776 51	0.703 81	0.721 69	0.557 86	0.826 50	0.451 64	0.672 86	0.563 71	0.483 84	0.943 77	0.425 84	0.162 101	0.644 47	0.726 63	0.659 66	0.709 77	0.572 68	0.875 68	0.786 82	0.559 87

MVPNet	0.641 69	0.831 31	0.715 77	0.671 85	0.590 74	0.781 84	0.394 90	0.679 83	0.642 32	0.553 61	0.937 85	0.462 64	0.256 63	0.649 45	0.406 103	0.626 79	0.691 85	0.666 32	0.877 66	0.792 77	0.608 67
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 71	0.717 82	0.701 83	0.692 78	0.576 80	0.801 74	0.467 60	0.716 73	0.563 71	0.459 91	0.953 44	0.429 80	0.169 98	0.581 64	0.854 35	0.605 84	0.710 75	0.550 82	0.894 53	0.793 74	0.575 79

FPConv	0.639 72	0.785 46	0.760 57	0.713 73	0.603 69	0.798 76	0.392 91	0.534 105	0.603 51	0.524 73	0.948 60	0.457 66	0.250 65	0.538 79	0.723 66	0.598 88	0.696 83	0.614 50	0.872 73	0.799 67	0.567 84
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 73	0.797 42	0.769 53	0.641 96	0.590 74	0.820 58	0.461 62	0.537 104	0.637 35	0.536 67	0.947 62	0.388 94	0.206 81	0.656 43	0.668 78	0.647 72	0.732 70	0.585 66	0.868 78	0.793 74	0.473 107

PointSPNet	0.637 74	0.734 71	0.692 90	0.714 72	0.576 80	0.797 77	0.446 69	0.743 66	0.598 54	0.437 96	0.942 78	0.403 90	0.150 105	0.626 53	0.800 54	0.649 69	0.697 82	0.557 78	0.846 87	0.777 86	0.563 85

SConv	0.636 75	0.830 32	0.697 86	0.752 64	0.572 82	0.780 86	0.445 71	0.716 73	0.529 77	0.530 69	0.951 50	0.446 75	0.170 97	0.507 88	0.666 79	0.636 77	0.682 88	0.541 88	0.886 58	0.799 67	0.594 75

Supervoxel-CNN	0.635 76	0.656 93	0.711 78	0.719 70	0.613 67	0.757 95	0.444 74	0.765 58	0.534 76	0.566 58	0.928 97	0.478 58	0.272 53	0.636 48	0.531 92	0.664 63	0.645 98	0.508 96	0.864 80	0.792 77	0.611 64

joint point-based	0.634 77	0.614 101	0.778 46	0.667 87	0.633 64	0.825 51	0.420 82	0.804 48	0.467 96	0.561 59	0.951 50	0.494 50	0.291 40	0.566 69	0.458 98	0.579 95	0.764 51	0.559 77	0.838 88	0.814 60	0.598 73
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 78	0.731 73	0.688 93	0.675 82	0.591 73	0.784 83	0.444 74	0.565 101	0.610 45	0.492 82	0.949 58	0.456 67	0.254 64	0.587 61	0.706 70	0.599 87	0.665 94	0.612 53	0.868 78	0.791 80	0.579 78

APCF-Net	0.631 79	0.742 68	0.687 95	0.672 83	0.557 86	0.792 81	0.408 84	0.665 87	0.545 74	0.508 78	0.952 48	0.428 81	0.186 91	0.634 50	0.702 72	0.620 80	0.706 79	0.555 79	0.873 71	0.798 69	0.581 77
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF	0.631 79	0.626 98	0.745 68	0.801 46	0.607 68	0.751 96	0.506 38	0.729 71	0.565 69	0.491 83	0.866 113	0.434 76	0.197 88	0.595 59	0.630 83	0.709 45	0.705 80	0.560 75	0.875 68	0.740 98	0.491 102

PointNet2-SFPN	0.631 79	0.771 55	0.692 90	0.672 83	0.524 91	0.837 38	0.440 76	0.706 78	0.538 75	0.446 93	0.944 74	0.421 86	0.219 76	0.552 75	0.751 60	0.591 91	0.737 66	0.543 87	0.901 48	0.768 90	0.557 88

FusionAwareConv	0.630 82	0.604 103	0.741 72	0.766 60	0.590 74	0.747 97	0.501 41	0.734 69	0.503 86	0.527 71	0.919 103	0.454 68	0.323 25	0.550 77	0.420 102	0.678 58	0.688 86	0.544 85	0.896 51	0.795 71	0.627 62
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 83	0.800 41	0.625 105	0.719 70	0.545 88	0.806 71	0.445 71	0.597 95	0.448 101	0.519 76	0.938 84	0.481 56	0.328 23	0.489 92	0.499 97	0.657 67	0.759 56	0.592 61	0.881 62	0.797 70	0.634 59

SegGroup_sem	0.627 84	0.818 36	0.747 67	0.701 74	0.602 70	0.764 92	0.385 95	0.629 92	0.490 89	0.508 78	0.931 96	0.409 89	0.201 85	0.564 70	0.725 64	0.618 81	0.692 84	0.539 89	0.873 71	0.794 72	0.548 91
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 85	0.830 32	0.694 88	0.757 62	0.563 84	0.772 90	0.448 68	0.647 90	0.520 80	0.509 77	0.949 58	0.431 79	0.191 89	0.496 90	0.614 85	0.647 72	0.672 92	0.535 91	0.876 67	0.783 83	0.571 80

dtc_net	0.625 85	0.703 86	0.751 64	0.794 49	0.535 89	0.848 27	0.480 53	0.676 85	0.528 78	0.469 88	0.944 74	0.454 68	0.004 118	0.464 95	0.636 82	0.704 48	0.758 57	0.548 84	0.924 31	0.787 81	0.492 101

HPEIN	0.618 87	0.729 75	0.668 96	0.647 93	0.597 72	0.766 91	0.414 83	0.680 82	0.520 80	0.525 72	0.946 65	0.432 77	0.215 78	0.493 91	0.599 86	0.638 76	0.617 103	0.570 69	0.897 50	0.806 64	0.605 70
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 88	0.858 25	0.772 49	0.489 111	0.532 90	0.792 81	0.404 87	0.643 91	0.570 68	0.507 80	0.935 88	0.414 88	0.046 115	0.510 86	0.702 72	0.602 86	0.705 80	0.549 83	0.859 82	0.773 88	0.534 94
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 89	0.760 60	0.667 97	0.649 92	0.521 92	0.793 79	0.457 63	0.648 89	0.528 78	0.434 98	0.947 62	0.401 91	0.153 104	0.454 96	0.721 67	0.648 71	0.717 74	0.536 90	0.904 43	0.765 91	0.485 103
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
Weakly-Openseg v3	0.604 90	0.901 14	0.762 55	0.627 98	0.478 98	0.820 58	0.346 101	0.689 81	0.353 111	0.528 70	0.933 93	0.217 116	0.172 95	0.530 81	0.725 64	0.593 90	0.737 66	0.515 93	0.858 83	0.772 89	0.515 97

wsss-transformer	0.600 91	0.634 97	0.743 70	0.697 77	0.601 71	0.781 84	0.437 78	0.585 98	0.493 88	0.446 93	0.933 93	0.394 92	0.011 117	0.654 44	0.661 81	0.603 85	0.733 69	0.526 92	0.832 89	0.761 93	0.480 104

LAP-D	0.594 92	0.720 80	0.692 90	0.637 97	0.456 102	0.773 89	0.391 93	0.730 70	0.587 58	0.445 95	0.940 82	0.381 95	0.288 41	0.434 99	0.453 100	0.591 91	0.649 96	0.581 67	0.777 97	0.749 97	0.610 66

DPC	0.592 93	0.720 80	0.700 84	0.602 102	0.480 97	0.762 94	0.380 96	0.713 76	0.585 61	0.437 96	0.940 82	0.369 97	0.288 41	0.434 99	0.509 96	0.590 93	0.639 101	0.567 73	0.772 98	0.755 95	0.592 76
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 94	0.766 59	0.659 100	0.683 80	0.470 101	0.740 99	0.387 94	0.620 94	0.490 89	0.476 86	0.922 101	0.355 100	0.245 68	0.511 85	0.511 95	0.571 96	0.643 99	0.493 100	0.872 73	0.762 92	0.600 72

ROSMRF	0.580 95	0.772 54	0.707 80	0.681 81	0.563 84	0.764 92	0.362 98	0.515 106	0.465 97	0.465 90	0.936 87	0.427 83	0.207 80	0.438 97	0.577 88	0.536 99	0.675 91	0.486 101	0.723 104	0.779 84	0.524 96

SD-DETR	0.576 96	0.746 65	0.609 109	0.445 115	0.517 93	0.643 110	0.366 97	0.714 75	0.456 99	0.468 89	0.870 112	0.432 77	0.264 60	0.558 73	0.674 76	0.586 94	0.688 86	0.482 102	0.739 102	0.733 100	0.537 93

SQN_0.1%	0.569 97	0.676 90	0.696 87	0.657 89	0.497 94	0.779 87	0.424 80	0.548 102	0.515 82	0.376 103	0.902 110	0.422 85	0.357 9	0.379 104	0.456 99	0.596 89	0.659 95	0.544 85	0.685 107	0.665 111	0.556 89

TextureNet	0.566 98	0.672 92	0.664 98	0.671 85	0.494 95	0.719 100	0.445 71	0.678 84	0.411 107	0.396 101	0.935 88	0.356 99	0.225 73	0.412 101	0.535 91	0.565 97	0.636 102	0.464 104	0.794 96	0.680 108	0.568 83
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 99	0.648 94	0.700 84	0.770 57	0.586 77	0.687 104	0.333 103	0.650 88	0.514 83	0.475 87	0.906 107	0.359 98	0.223 75	0.340 106	0.442 101	0.422 110	0.668 93	0.501 97	0.708 105	0.779 84	0.534 94

Pointnet++ & Feature	0.557 100	0.735 70	0.661 99	0.686 79	0.491 96	0.744 98	0.392 91	0.539 103	0.451 100	0.375 104	0.946 65	0.376 96	0.205 82	0.403 102	0.356 106	0.553 98	0.643 99	0.497 98	0.824 92	0.756 94	0.515 97

GMLPs	0.538 101	0.495 111	0.693 89	0.647 93	0.471 100	0.793 79	0.300 106	0.477 107	0.505 85	0.358 105	0.903 109	0.327 103	0.081 112	0.472 94	0.529 93	0.448 108	0.710 75	0.509 94	0.746 100	0.737 99	0.554 90

PanopticFusion-label	0.529 102	0.491 112	0.688 93	0.604 101	0.386 107	0.632 111	0.225 117	0.705 79	0.434 104	0.293 111	0.815 115	0.348 101	0.241 69	0.499 89	0.669 77	0.507 101	0.649 96	0.442 110	0.796 95	0.602 115	0.561 86
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	0.516 103	0.676 90	0.591 112	0.609 99	0.442 103	0.774 88	0.335 102	0.597 95	0.422 106	0.357 106	0.932 95	0.341 102	0.094 111	0.298 108	0.528 94	0.473 106	0.676 90	0.495 99	0.602 113	0.721 103	0.349 115

Online SegFusion	0.515 104	0.607 102	0.644 103	0.579 104	0.434 104	0.630 112	0.353 99	0.628 93	0.440 102	0.410 99	0.762 118	0.307 105	0.167 99	0.520 83	0.403 104	0.516 100	0.565 106	0.447 108	0.678 108	0.701 105	0.514 99
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF	0.501 105	0.558 107	0.608 110	0.424 117	0.478 98	0.690 103	0.246 113	0.586 97	0.468 95	0.450 92	0.911 105	0.394 92	0.160 102	0.438 97	0.212 113	0.432 109	0.541 111	0.475 103	0.742 101	0.727 101	0.477 105

PCNN	0.498 106	0.559 106	0.644 103	0.560 106	0.420 106	0.711 102	0.229 115	0.414 108	0.436 103	0.352 107	0.941 80	0.324 104	0.155 103	0.238 113	0.387 105	0.493 102	0.529 112	0.509 94	0.813 94	0.751 96	0.504 100

3DMV	0.484 107	0.484 113	0.538 115	0.643 95	0.424 105	0.606 115	0.310 104	0.574 99	0.433 105	0.378 102	0.796 116	0.301 106	0.214 79	0.537 80	0.208 114	0.472 107	0.507 115	0.413 113	0.693 106	0.602 115	0.539 92
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 108	0.577 105	0.611 108	0.356 119	0.321 115	0.715 101	0.299 108	0.376 112	0.328 115	0.319 109	0.944 74	0.285 108	0.164 100	0.216 116	0.229 111	0.484 104	0.545 110	0.456 106	0.755 99	0.709 104	0.475 106
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 109	0.679 89	0.604 111	0.578 105	0.380 108	0.682 105	0.291 109	0.106 119	0.483 92	0.258 117	0.920 102	0.258 112	0.025 116	0.231 115	0.325 107	0.480 105	0.560 108	0.463 105	0.725 103	0.666 110	0.231 119
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 110	0.474 114	0.623 106	0.463 113	0.366 110	0.651 108	0.310 104	0.389 111	0.349 113	0.330 108	0.937 85	0.271 110	0.126 108	0.285 109	0.224 112	0.350 115	0.577 105	0.445 109	0.625 111	0.723 102	0.394 111
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	0.442 111	0.548 108	0.548 114	0.597 103	0.363 111	0.628 113	0.300 106	0.292 114	0.374 109	0.307 110	0.881 111	0.268 111	0.186 91	0.238 113	0.204 115	0.407 111	0.506 116	0.449 107	0.667 109	0.620 114	0.462 109

SurfaceConvPF	0.442 111	0.505 110	0.622 107	0.380 118	0.342 113	0.654 107	0.227 116	0.397 110	0.367 110	0.276 113	0.924 99	0.240 113	0.198 87	0.359 105	0.262 109	0.366 112	0.581 104	0.435 111	0.640 110	0.668 109	0.398 110
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 113	0.437 116	0.646 102	0.474 112	0.369 109	0.645 109	0.353 99	0.258 116	0.282 118	0.279 112	0.918 104	0.298 107	0.147 107	0.283 110	0.294 108	0.487 103	0.562 107	0.427 112	0.619 112	0.633 113	0.352 114
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 114	0.525 109	0.647 101	0.522 107	0.324 114	0.488 119	0.077 120	0.712 77	0.353 111	0.401 100	0.636 120	0.281 109	0.176 94	0.340 106	0.565 89	0.175 119	0.551 109	0.398 114	0.370 120	0.602 115	0.361 113

SPLAT Net	0.393 115	0.472 115	0.511 116	0.606 100	0.311 116	0.656 106	0.245 114	0.405 109	0.328 115	0.197 118	0.927 98	0.227 115	0.000 120	0.001 121	0.249 110	0.271 118	0.510 113	0.383 116	0.593 114	0.699 106	0.267 117
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	0.383 116	0.297 118	0.491 117	0.432 116	0.358 112	0.612 114	0.274 111	0.116 118	0.411 107	0.265 114	0.904 108	0.229 114	0.079 113	0.250 111	0.185 116	0.320 116	0.510 113	0.385 115	0.548 115	0.597 118	0.394 111

PointNet++	0.339 117	0.584 104	0.478 118	0.458 114	0.256 118	0.360 120	0.250 112	0.247 117	0.278 119	0.261 116	0.677 119	0.183 117	0.117 109	0.212 117	0.145 118	0.364 113	0.346 120	0.232 120	0.548 115	0.523 119	0.252 118
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
GrowSP++	0.323 118	0.114 120	0.589 113	0.499 109	0.147 120	0.555 116	0.290 110	0.336 113	0.290 117	0.262 115	0.865 114	0.102 120	0.000 120	0.037 119	0.000 121	0.000 121	0.462 117	0.381 117	0.389 119	0.664 112	0.473 107

SSC-UNet	0.308 119	0.353 117	0.290 120	0.278 120	0.166 119	0.553 117	0.169 119	0.286 115	0.147 120	0.148 120	0.908 106	0.182 118	0.064 114	0.023 120	0.018 120	0.354 114	0.363 118	0.345 118	0.546 117	0.685 107	0.278 116

ScanNet	0.306 120	0.203 119	0.366 119	0.501 108	0.311 116	0.524 118	0.211 118	0.002 121	0.342 114	0.189 119	0.786 117	0.145 119	0.102 110	0.245 112	0.152 117	0.318 117	0.348 119	0.300 119	0.460 118	0.437 120	0.182 120
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 121	0.000 121	0.041 121	0.172 121	0.030 121	0.062 121	0.001 121	0.035 120	0.004 121	0.051 121	0.143 121	0.019 121	0.003 119	0.041 118	0.050 119	0.003 120	0.054 121	0.018 121	0.005 121	0.264 121	0.082 121

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 3	0.872 10	0.718 5	0.941 1	0.588 3	0.652 38	0.819 2	0.776 3	0.720 5	0.780 4	0.769 12	1.000 1	0.797 10	0.813 27	0.798 7	1.000 1	0.659 3

PointRel	0.816 1	1.000 1	0.971 8	0.908 6	0.743 2	0.923 7	0.573 7	0.714 22	0.695 18	0.734 9	0.747 2	0.725 11	0.809 1	1.000 1	0.814 8	0.899 3	0.820 3	1.000 1	0.610 17
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
Spherical Mask(CtoF)	0.812 3	1.000 1	0.973 7	0.852 14	0.718 6	0.917 9	0.574 5	0.677 29	0.748 11	0.729 13	0.715 7	0.795 2	0.809 1	1.000 1	0.831 4	0.854 9	0.787 11	1.000 1	0.638 6

EV3D	0.811 4	1.000 1	0.968 9	0.852 14	0.717 7	0.921 8	0.574 6	0.677 29	0.748 11	0.730 12	0.703 12	0.795 2	0.809 1	1.000 1	0.831 4	0.854 9	0.778 15	1.000 1	0.638 7

SIM3D	0.803 5	1.000 1	0.967 10	0.863 13	0.692 18	0.924 6	0.552 11	0.732 21	0.667 22	0.732 11	0.662 16	0.796 1	0.789 9	1.000 1	0.803 9	0.864 6	0.766 20	1.000 1	0.643 5

OneFormer3D	0.801 6	1.000 1	0.973 6	0.909 5	0.698 15	0.928 4	0.582 4	0.668 34	0.685 19	0.780 2	0.687 14	0.698 19	0.702 15	1.000 1	0.794 12	0.900 2	0.784 13	0.986 52	0.635 8
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
Competitor-SPFormer	0.800 7	1.000 1	0.986 2	0.845 16	0.705 13	0.915 10	0.532 13	0.733 20	0.757 10	0.733 10	0.708 9	0.698 18	0.648 35	0.981 38	0.890 1	0.830 18	0.796 8	0.997 39	0.644 4

UniPerception	0.800 7	1.000 1	0.930 12	0.872 10	0.727 4	0.862 24	0.454 19	0.764 13	0.820 1	0.746 7	0.706 10	0.750 6	0.772 10	0.926 45	0.764 18	0.818 26	0.826 1	0.997 39	0.660 2

InsSSM	0.799 9	1.000 1	0.915 14	0.710 41	0.729 3	0.925 5	0.664 1	0.670 32	0.770 7	0.766 4	0.739 3	0.737 7	0.700 16	1.000 1	0.792 13	0.829 20	0.815 4	0.997 39	0.625 10
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
TST3D	0.795 10	1.000 1	0.929 13	0.918 4	0.709 10	0.884 19	0.596 2	0.704 25	0.769 8	0.734 8	0.644 21	0.699 17	0.751 13	1.000 1	0.794 11	0.876 5	0.757 23	0.997 39	0.550 32
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
MG-Former	0.791 11	1.000 1	0.980 5	0.837 19	0.626 26	0.897 12	0.543 12	0.759 15	0.800 6	0.766 5	0.659 17	0.769 5	0.697 19	1.000 1	0.791 14	0.707 48	0.791 10	1.000 1	0.610 16

ExtMask3D	0.789 12	1.000 1	0.988 1	0.756 34	0.706 12	0.912 11	0.429 20	0.647 40	0.806 5	0.755 6	0.673 15	0.689 20	0.772 11	1.000 1	0.789 15	0.852 11	0.811 5	1.000 1	0.617 13

Queryformer	0.787 13	1.000 1	0.933 11	0.601 50	0.754 1	0.886 17	0.558 10	0.661 36	0.767 9	0.665 19	0.716 6	0.639 25	0.808 5	1.000 1	0.844 3	0.897 4	0.804 6	1.000 1	0.624 11

MAFT	0.786 14	1.000 1	0.894 19	0.807 23	0.694 17	0.893 15	0.486 15	0.674 31	0.740 13	0.786 1	0.704 11	0.727 10	0.739 14	1.000 1	0.707 24	0.849 13	0.756 24	1.000 1	0.685 1

KmaxOneFormerNet	0.783 15	0.903 55	0.981 4	0.794 27	0.706 11	0.931 3	0.561 9	0.701 26	0.706 16	0.727 14	0.697 13	0.731 9	0.689 22	1.000 1	0.856 2	0.750 39	0.761 22	1.000 1	0.599 21

Mask3D	0.780 16	1.000 1	0.786 43	0.716 39	0.696 16	0.885 18	0.500 14	0.714 22	0.810 4	0.672 18	0.715 7	0.679 21	0.809 1	1.000 1	0.831 4	0.833 17	0.787 11	1.000 1	0.602 19
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormer	0.770 17	0.903 55	0.903 16	0.806 24	0.609 32	0.886 16	0.568 8	0.815 6	0.705 17	0.711 15	0.655 18	0.652 24	0.685 23	1.000 1	0.789 16	0.809 28	0.776 17	1.000 1	0.583 25
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++	0.769 18	1.000 1	0.803 36	0.937 1	0.684 19	0.865 21	0.213 35	0.870 2	0.664 23	0.571 25	0.758 1	0.702 15	0.807 6	1.000 1	0.653 31	0.902 1	0.792 9	1.000 1	0.626 9

SoftGroup	0.761 19	1.000 1	0.808 32	0.845 16	0.716 8	0.862 23	0.243 32	0.824 4	0.655 25	0.620 20	0.734 4	0.699 16	0.791 8	0.981 38	0.716 22	0.844 14	0.769 18	1.000 1	0.594 23
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 20	1.000 1	0.904 15	0.731 37	0.678 20	0.895 13	0.458 17	0.644 42	0.670 21	0.710 16	0.620 26	0.732 8	0.650 25	1.000 1	0.756 19	0.778 31	0.779 14	1.000 1	0.614 14
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 21	1.000 1	0.774 44	0.867 12	0.621 28	0.934 2	0.404 21	0.706 24	0.812 3	0.605 23	0.633 24	0.626 26	0.690 21	1.000 1	0.640 33	0.820 23	0.777 16	1.000 1	0.612 15
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNet	0.747 22	1.000 1	0.818 28	0.837 20	0.713 9	0.844 26	0.457 18	0.647 40	0.711 15	0.614 21	0.617 28	0.657 23	0.650 25	1.000 1	0.692 25	0.822 22	0.765 21	1.000 1	0.595 22
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut	0.732 23	1.000 1	0.788 41	0.724 38	0.642 25	0.859 25	0.248 31	0.787 11	0.618 28	0.596 24	0.653 20	0.722 13	0.583 47	1.000 1	0.766 17	0.861 7	0.825 2	1.000 1	0.504 38

IPCA-Inst	0.731 24	1.000 1	0.788 42	0.884 9	0.698 14	0.788 42	0.252 30	0.760 14	0.646 26	0.511 33	0.637 23	0.665 22	0.804 7	1.000 1	0.644 32	0.778 32	0.747 26	1.000 1	0.561 29

TopoSeg	0.725 25	1.000 1	0.806 35	0.933 2	0.668 22	0.758 46	0.272 29	0.734 19	0.630 27	0.549 29	0.654 19	0.606 27	0.697 20	0.966 42	0.612 37	0.839 15	0.754 25	1.000 1	0.573 26

DKNet	0.718 26	1.000 1	0.814 29	0.782 28	0.619 29	0.872 20	0.224 33	0.751 17	0.569 32	0.677 17	0.585 32	0.724 12	0.633 37	0.981 38	0.515 47	0.819 24	0.736 27	1.000 1	0.617 12
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC	0.707 27	1.000 1	0.850 21	0.924 3	0.648 23	0.747 49	0.162 37	0.862 3	0.572 31	0.520 31	0.624 25	0.549 30	0.649 34	1.000 1	0.560 42	0.706 49	0.768 19	1.000 1	0.591 24

HAIS	0.699 28	1.000 1	0.849 22	0.820 21	0.675 21	0.808 36	0.279 27	0.757 16	0.465 38	0.517 32	0.596 30	0.559 29	0.600 41	1.000 1	0.654 30	0.767 34	0.676 31	0.994 48	0.560 30
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet	0.698 29	1.000 1	0.697 60	0.888 8	0.556 39	0.803 37	0.387 22	0.626 44	0.417 43	0.556 28	0.585 33	0.702 14	0.600 41	1.000 1	0.824 7	0.720 47	0.692 29	1.000 1	0.509 37
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup	0.694 30	1.000 1	0.799 38	0.811 22	0.622 27	0.817 31	0.376 23	0.805 9	0.590 30	0.487 37	0.568 36	0.525 34	0.650 25	0.835 55	0.600 38	0.829 19	0.655 34	1.000 1	0.526 34

SphereSeg	0.680 31	1.000 1	0.856 20	0.744 35	0.618 30	0.893 14	0.151 38	0.651 39	0.713 14	0.537 30	0.579 35	0.430 44	0.651 24	1.000 1	0.389 58	0.744 42	0.697 28	0.991 50	0.601 20

DANCENET	0.680 31	1.000 1	0.807 33	0.733 36	0.600 33	0.768 45	0.375 24	0.543 52	0.538 33	0.610 22	0.599 29	0.498 35	0.632 39	0.981 38	0.739 21	0.856 8	0.633 40	0.882 63	0.454 47

Box2Mask	0.677 33	1.000 1	0.847 23	0.771 30	0.509 48	0.816 32	0.277 28	0.558 51	0.482 35	0.562 27	0.640 22	0.448 40	0.700 16	1.000 1	0.666 26	0.852 12	0.578 47	0.997 39	0.488 42
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 34	1.000 1	0.758 52	0.682 43	0.576 37	0.842 27	0.477 16	0.504 58	0.524 34	0.567 26	0.585 34	0.451 39	0.557 49	1.000 1	0.751 20	0.797 29	0.563 50	1.000 1	0.467 46
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group	0.664 35	1.000 1	0.822 27	0.764 33	0.616 31	0.815 33	0.139 42	0.694 28	0.597 29	0.459 41	0.566 37	0.599 28	0.600 41	0.516 65	0.715 23	0.819 25	0.635 38	1.000 1	0.603 18
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 36	1.000 1	0.760 50	0.667 45	0.581 35	0.863 22	0.323 25	0.655 37	0.477 36	0.473 39	0.549 39	0.432 43	0.650 25	1.000 1	0.655 29	0.738 43	0.585 46	0.944 55	0.472 45

CSC-Pretrained	0.648 37	1.000 1	0.810 30	0.768 31	0.523 46	0.813 34	0.143 41	0.819 5	0.389 46	0.422 50	0.511 43	0.443 41	0.650 25	1.000 1	0.624 35	0.732 44	0.634 39	1.000 1	0.375 54

PE	0.645 38	1.000 1	0.773 46	0.798 26	0.538 41	0.786 43	0.088 50	0.799 10	0.350 50	0.435 48	0.547 40	0.545 31	0.646 36	0.933 44	0.562 41	0.761 37	0.556 55	0.997 39	0.501 40
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 39	1.000 1	0.758 51	0.582 56	0.539 40	0.826 30	0.046 55	0.765 12	0.372 48	0.436 47	0.588 31	0.539 33	0.650 25	1.000 1	0.577 39	0.750 40	0.653 36	0.997 39	0.495 41
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3D	0.641 40	1.000 1	0.841 24	0.893 7	0.531 43	0.802 38	0.115 47	0.588 49	0.448 40	0.438 45	0.537 42	0.430 45	0.550 50	0.857 47	0.534 45	0.764 36	0.657 33	0.987 51	0.568 27
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN	0.638 41	1.000 1	0.895 18	0.800 25	0.480 52	0.676 54	0.144 40	0.737 18	0.354 49	0.447 42	0.400 56	0.365 51	0.700 16	1.000 1	0.569 40	0.836 16	0.599 42	1.000 1	0.473 44

PointGroup	0.636 42	1.000 1	0.765 47	0.624 47	0.505 50	0.797 39	0.116 46	0.696 27	0.384 47	0.441 43	0.559 38	0.476 37	0.596 44	1.000 1	0.666 26	0.756 38	0.556 54	0.997 39	0.513 36
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 43	0.667 58	0.797 40	0.714 40	0.562 38	0.774 44	0.146 39	0.810 8	0.429 42	0.476 38	0.546 41	0.399 47	0.633 37	1.000 1	0.632 34	0.722 46	0.609 41	1.000 1	0.514 35
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 44	1.000 1	0.829 26	0.606 49	0.646 24	0.836 28	0.068 51	0.511 56	0.462 39	0.507 34	0.619 27	0.389 49	0.610 40	1.000 1	0.432 53	0.828 21	0.673 32	0.788 67	0.552 31

DENet	0.629 45	1.000 1	0.797 39	0.608 48	0.589 34	0.627 58	0.219 34	0.882 1	0.310 52	0.402 55	0.383 58	0.396 48	0.650 25	1.000 1	0.663 28	0.543 66	0.691 30	1.000 1	0.568 28

3D-MPA	0.611 46	1.000 1	0.833 25	0.765 32	0.526 45	0.756 47	0.136 44	0.588 49	0.470 37	0.438 46	0.432 52	0.358 53	0.650 25	0.857 47	0.429 54	0.765 35	0.557 53	1.000 1	0.430 49
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 47	1.000 1	0.801 37	0.599 51	0.535 42	0.728 51	0.286 26	0.436 62	0.679 20	0.491 35	0.433 50	0.256 55	0.404 62	0.857 47	0.620 36	0.724 45	0.510 60	1.000 1	0.539 33

AOIA	0.601 48	1.000 1	0.761 49	0.687 42	0.485 51	0.828 29	0.008 62	0.663 35	0.405 45	0.405 54	0.425 53	0.490 36	0.596 44	0.714 58	0.553 44	0.779 30	0.597 43	0.992 49	0.424 51

PCJC	0.578 49	1.000 1	0.810 31	0.583 55	0.449 55	0.813 35	0.042 56	0.603 47	0.341 51	0.490 36	0.465 47	0.410 46	0.650 25	0.835 55	0.264 64	0.694 53	0.561 51	0.889 60	0.504 39

SSEN	0.575 50	1.000 1	0.761 48	0.473 58	0.477 53	0.795 40	0.066 52	0.529 54	0.658 24	0.460 40	0.461 48	0.380 50	0.331 64	0.859 46	0.401 57	0.692 55	0.653 35	1.000 1	0.348 56
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 51	0.528 68	0.708 59	0.626 46	0.580 36	0.745 50	0.063 53	0.627 43	0.240 56	0.400 56	0.497 44	0.464 38	0.515 51	1.000 1	0.475 49	0.745 41	0.571 48	1.000 1	0.429 50

NeuralBF	0.555 52	0.667 58	0.896 17	0.843 18	0.517 47	0.751 48	0.029 57	0.519 55	0.414 44	0.439 44	0.465 46	0.000 74	0.484 53	0.857 47	0.287 62	0.693 54	0.651 37	1.000 1	0.485 43
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 53	1.000 1	0.807 34	0.588 54	0.327 60	0.647 56	0.004 64	0.815 7	0.180 59	0.418 51	0.364 60	0.182 58	0.445 56	1.000 1	0.442 52	0.688 56	0.571 49	1.000 1	0.396 52
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance	0.539 54	1.000 1	0.621 63	0.300 61	0.530 44	0.698 52	0.127 45	0.533 53	0.222 57	0.430 49	0.400 55	0.365 51	0.574 48	0.938 43	0.472 50	0.659 58	0.543 56	0.944 55	0.347 57

One_Thing_One_Click	0.529 55	0.667 58	0.718 55	0.777 29	0.399 56	0.683 53	0.000 67	0.669 33	0.138 62	0.391 57	0.374 59	0.539 32	0.360 63	0.641 62	0.556 43	0.774 33	0.593 44	0.997 39	0.251 62
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 56	1.000 1	0.538 68	0.282 62	0.468 54	0.790 41	0.173 36	0.345 64	0.429 41	0.413 53	0.484 45	0.176 59	0.595 46	0.591 63	0.522 46	0.668 57	0.476 61	0.986 53	0.327 58

Occipital-SCS	0.512 57	1.000 1	0.716 56	0.509 57	0.506 49	0.611 59	0.092 49	0.602 48	0.177 60	0.346 60	0.383 57	0.165 60	0.442 57	0.850 54	0.386 59	0.618 62	0.543 57	0.889 60	0.389 53

3D-BoNet	0.488 58	1.000 1	0.672 62	0.590 53	0.301 62	0.484 69	0.098 48	0.620 45	0.306 53	0.341 61	0.259 64	0.125 62	0.434 59	0.796 57	0.402 56	0.499 68	0.513 59	0.909 59	0.439 48
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 59	0.667 58	0.712 58	0.595 52	0.259 65	0.550 65	0.000 67	0.613 46	0.175 61	0.250 66	0.434 49	0.437 42	0.411 61	0.857 47	0.485 48	0.591 65	0.267 71	0.944 55	0.359 55
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 60	0.667 58	0.685 61	0.677 44	0.372 58	0.562 63	0.000 67	0.482 59	0.244 55	0.316 63	0.298 61	0.052 69	0.442 58	0.857 47	0.267 63	0.702 50	0.559 52	1.000 1	0.287 60

SALoss-ResNet	0.459 61	1.000 1	0.737 54	0.159 72	0.259 64	0.587 61	0.138 43	0.475 60	0.217 58	0.416 52	0.408 54	0.128 61	0.315 65	0.714 58	0.411 55	0.536 67	0.590 45	0.873 64	0.304 59
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 62	0.528 68	0.555 66	0.381 59	0.382 57	0.633 57	0.002 65	0.509 57	0.260 54	0.361 59	0.432 51	0.327 54	0.451 55	0.571 64	0.367 60	0.639 60	0.386 62	0.980 54	0.276 61
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_ins	0.445 63	0.667 58	0.773 45	0.185 69	0.317 61	0.656 55	0.000 67	0.407 63	0.134 63	0.381 58	0.267 63	0.217 57	0.476 54	0.714 58	0.452 51	0.629 61	0.514 58	1.000 1	0.222 65
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 64	1.000 1	0.432 71	0.245 64	0.190 66	0.577 62	0.013 61	0.263 66	0.033 69	0.320 62	0.240 65	0.075 65	0.422 60	0.857 47	0.117 69	0.699 51	0.271 70	0.883 62	0.235 64
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.323 65	0.667 58	0.542 67	0.264 63	0.157 69	0.550 64	0.000 67	0.205 69	0.009 71	0.270 65	0.218 66	0.075 65	0.500 52	0.688 61	0.007 75	0.698 52	0.301 67	0.459 72	0.200 66
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone	0.319 66	0.667 58	0.715 57	0.233 65	0.189 67	0.479 70	0.008 62	0.218 67	0.067 68	0.201 68	0.173 67	0.107 63	0.123 70	0.438 66	0.150 66	0.615 63	0.355 63	0.916 58	0.093 74

R-PointNet	0.306 67	0.500 70	0.405 72	0.311 60	0.348 59	0.589 60	0.054 54	0.068 72	0.126 64	0.283 64	0.290 62	0.028 70	0.219 68	0.214 69	0.331 61	0.396 72	0.275 68	0.821 66	0.245 63

Region-18class	0.284 68	0.250 74	0.751 53	0.228 67	0.270 63	0.521 66	0.000 67	0.468 61	0.008 73	0.205 67	0.127 68	0.000 74	0.068 72	0.070 73	0.262 65	0.652 59	0.323 65	0.740 68	0.173 67

SemRegionNet-20cls	0.250 69	0.333 71	0.613 64	0.229 66	0.163 68	0.493 67	0.000 67	0.304 65	0.107 65	0.147 71	0.100 70	0.052 68	0.231 66	0.119 71	0.039 71	0.445 70	0.325 64	0.654 69	0.141 70

tmp	0.248 70	0.667 58	0.437 70	0.188 68	0.153 70	0.491 68	0.000 67	0.208 68	0.094 67	0.153 70	0.099 71	0.057 67	0.217 69	0.119 71	0.039 71	0.466 69	0.302 66	0.640 70	0.140 71

3D-BEVIS	0.248 70	0.667 58	0.566 65	0.076 73	0.035 75	0.394 73	0.027 59	0.035 74	0.098 66	0.099 73	0.030 74	0.025 71	0.098 71	0.375 68	0.126 68	0.604 64	0.181 73	0.854 65	0.171 68
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sem_Recon_ins	0.227 72	0.764 57	0.486 69	0.069 74	0.098 72	0.426 72	0.017 60	0.067 73	0.015 70	0.172 69	0.100 69	0.096 64	0.054 74	0.183 70	0.135 67	0.366 73	0.260 72	0.614 71	0.168 69

ASIS	0.199 73	0.333 71	0.253 74	0.167 71	0.140 71	0.438 71	0.000 67	0.177 70	0.008 72	0.121 72	0.069 72	0.004 73	0.231 67	0.429 67	0.036 73	0.445 71	0.273 69	0.333 74	0.119 73

Sgpn_scannet	0.143 74	0.208 75	0.390 73	0.169 70	0.065 73	0.275 74	0.029 58	0.069 71	0.000 74	0.087 74	0.043 73	0.014 72	0.027 75	0.000 74	0.112 70	0.351 74	0.168 74	0.438 73	0.138 72

MaskRCNN 2d->3d Proj	0.058 75	0.333 71	0.002 75	0.000 75	0.053 74	0.002 75	0.002 66	0.021 75	0.000 74	0.045 75	0.024 75	0.238 56	0.065 73	0.000 74	0.014 74	0.107 75	0.020 75	0.110 75	0.006 75

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

Virtual MVFusion (R)	0.745 1	0.861 1	0.839 1	0.881 1	0.672 2	0.512 1	0.422 17	0.898 1	0.723 1	0.714 1	0.954 2	0.454 1	0.509 1	0.773 1	0.895 1	0.756 1	0.820 1	0.653 1	0.935 1	0.891 1	0.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2D	0.670 2	0.822 3	0.795 3	0.836 2	0.659 3	0.481 2	0.451 13	0.769 4	0.656 3	0.567 4	0.931 3	0.395 6	0.390 5	0.700 4	0.534 4	0.689 10	0.770 2	0.574 3	0.865 9	0.831 3	0.675 5
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MVF-GNN（2D）	0.636 3	0.606 14	0.794 4	0.434 16	0.688 1	0.337 8	0.464 12	0.798 3	0.632 5	0.589 3	0.908 8	0.420 2	0.329 12	0.743 2	0.594 2	0.738 2	0.676 5	0.527 4	0.906 2	0.818 6	0.715 3

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

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

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

EMSANet	0.600 7	0.716 4	0.746 9	0.395 18	0.614 9	0.382 5	0.523 4	0.713 11	0.571 11	0.503 10	0.922 6	0.404 5	0.397 4	0.655 9	0.400 16	0.626 21	0.663 6	0.469 13	0.900 4	0.827 4	0.577 14
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
MCA-Net	0.595 8	0.533 20	0.756 8	0.746 4	0.590 10	0.334 10	0.506 7	0.670 15	0.587 8	0.500 12	0.905 10	0.366 10	0.352 9	0.601 13	0.506 8	0.669 16	0.648 9	0.501 7	0.839 15	0.769 15	0.516 21

RFBNet	0.592 9	0.616 11	0.758 7	0.659 5	0.581 11	0.330 11	0.469 11	0.655 18	0.543 14	0.524 8	0.924 4	0.355 13	0.336 11	0.572 17	0.479 10	0.671 14	0.648 9	0.480 10	0.814 19	0.814 7	0.614 10

FAN_NV_RVC	0.586 10	0.510 21	0.764 6	0.079 26	0.620 8	0.330 11	0.494 8	0.753 5	0.573 9	0.556 5	0.884 16	0.405 4	0.303 16	0.718 3	0.452 13	0.672 13	0.658 7	0.509 5	0.898 5	0.813 8	0.727 2

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

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

SSMA	0.577 13	0.695 5	0.716 15	0.439 14	0.563 14	0.314 14	0.444 15	0.719 9	0.551 12	0.503 10	0.887 15	0.346 16	0.348 10	0.603 12	0.353 20	0.709 6	0.600 15	0.457 14	0.901 3	0.786 11	0.599 13
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
DMMF	0.567 14	0.623 10	0.767 5	0.238 20	0.571 13	0.347 6	0.413 19	0.719 9	0.472 20	0.418 22	0.895 13	0.357 12	0.260 22	0.696 5	0.523 7	0.666 17	0.642 11	0.437 18	0.895 6	0.793 10	0.603 12

UNIV_CNP_RVC_UE	0.566 15	0.569 19	0.686 19	0.435 15	0.524 17	0.294 19	0.421 18	0.712 12	0.543 14	0.463 17	0.872 17	0.320 17	0.363 8	0.611 11	0.477 11	0.686 11	0.627 12	0.443 17	0.862 10	0.775 14	0.639 6

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

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

UDSSEG_RVC	0.545 18	0.610 13	0.661 22	0.588 8	0.556 15	0.268 21	0.482 9	0.642 20	0.572 10	0.475 16	0.836 23	0.312 18	0.367 7	0.630 10	0.189 23	0.639 19	0.495 23	0.452 15	0.826 17	0.756 20	0.541 17

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

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

Enet (reimpl)	0.376 25	0.264 26	0.452 26	0.452 13	0.365 24	0.181 24	0.143 26	0.456 25	0.409 25	0.346 25	0.769 26	0.164 24	0.218 24	0.359 25	0.123 26	0.403 26	0.381 26	0.313 26	0.571 25	0.685 24	0.472 23
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
ScanNet (2d proj)	0.330 26	0.293 25	0.521 25	0.657 6	0.361 25	0.161 25	0.250 25	0.004 26	0.440 23	0.183 26	0.836 23	0.125 25	0.060 26	0.319 26	0.132 25	0.417 25	0.412 25	0.344 25	0.541 26	0.427 26	0.109 26
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17

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

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

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

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