Benchmark Results - ScanNet Benchmark

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

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

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

ALS-MinkowskiNet	0.414 1	0.610 2	0.322 3	0.271 1	0.852 1	0.710 2	0.973 1	0.572 3	0.719 3	0.795 1	0.477 5	0.506 2	0.601 1	0.000 12	0.804 5	0.646 2	0.804 1	0.344 2	0.777 1	0.984 1	0.671 1	0.879 2	0.936 1	0.342 4	0.632 7	0.449 3	0.817 3	0.475 9	0.723 2	0.798 1	0.376 8	0.832 2	0.693 1	0.031 9	0.564 1	0.510 13	0.000 1	0.893 2	0.905 1	0.672 16	0.314 1	0.000 7	0.718 1	0.153 2	0.542 2	0.397 2	0.726 3	0.752 8	0.252 7	0.226 1	0.916 2	0.800 1	0.047 15	0.807 3	0.769 1	0.709 3	0.630 2	0.769 1	0.217 9	0.000 3	0.285 1	0.598 3	0.846 9	0.535 2	0.956 3	0.000 6	0.137 11	0.784 2	0.464 6	0.463 13	0.230 11	0.000 1	0.598 3	0.662 9	0.000 4	0.087 2	0.000 1	0.135 2	0.900 1	0.780 11	0.703 2	0.741 1	0.571 2	0.149 9	0.697 5	0.646 1	0.000 3	0.076 1	0.000 1	0.025 9	0.000 3	0.106 4	0.981 1	0.000 1	0.043 6	0.113 3	0.888 1	0.248 15	0.404 4	0.252 5	0.314 1	0.220 5	0.245 1	0.466 6	0.366 2	0.159 2	0.000 4	0.149 7	0.690 2	0.000 3	0.531 5	0.253 2	0.285 5	0.460 1	0.440 4	0.813 1	0.230 2	0.283 5	0.159 10	0.000 1	0.728 1	0.666 5	0.958 1	0.000 1	0.021 4	0.252 6	0.118 4	0.000 7	0.445 3	0.223 10	0.285 1	0.194 3	0.390 2	0.000 1	0.475 3	0.842 7	0.000 1	0.455 3	0.000 1	0.250 7	0.458 7	0.000 1	0.865 1	0.000 1	0.000 1	0.635 1	0.359 4	0.972 1	0.087 3	0.447 2	0.000 1	0.000 7	0.000 1	0.129 2	0.532 6	0.446 7	0.503 3	0.071 11	0.135 12	0.699 3	0.717 1	0.097 2	0.000 1	0.665 1	0.000 2	0.000 2	1.000 1	0.752 5	0.000 2	0.000 1	0.000 1	0.142 8	0.200 1	0.259 1	1.000 1	0.000 1

PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 2	0.851 2	0.687 6	0.971 2	0.586 2	0.755 1	0.752 7	0.505 1	0.404 6	0.575 4	0.000 12	0.848 2	0.616 4	0.761 3	0.349 1	0.738 2	0.978 2	0.546 6	0.860 8	0.926 2	0.346 3	0.654 3	0.384 6	0.828 1	0.523 3	0.699 3	0.583 5	0.387 7	0.822 3	0.688 2	0.118 5	0.474 2	0.603 4	0.000 1	0.832 7	0.903 2	0.753 8	0.140 9	0.000 7	0.650 3	0.109 4	0.520 3	0.457 1	0.497 9	0.871 4	0.281 3	0.192 3	0.887 4	0.748 3	0.168 1	0.727 7	0.733 2	0.740 1	0.644 1	0.714 4	0.190 12	0.000 3	0.256 3	0.449 9	0.914 1	0.514 3	0.759 14	0.337 2	0.172 6	0.692 6	0.617 2	0.636 1	0.325 7	0.000 1	0.641 2	0.782 1	0.000 4	0.065 3	0.000 1	0.000 5	0.842 3	0.903 2	0.661 4	0.662 4	0.612 1	0.405 2	0.731 3	0.566 3	0.000 3	0.000 7	0.000 1	0.017 14	0.301 1	0.088 5	0.941 2	0.000 1	0.077 3	0.000 10	0.717 7	0.790 2	0.310 11	0.026 16	0.264 4	0.349 1	0.220 3	0.397 12	0.366 2	0.115 12	0.000 4	0.337 1	0.463 6	0.000 3	0.531 5	0.218 3	0.593 2	0.455 2	0.469 1	0.708 3	0.210 3	0.592 2	0.108 15	0.000 1	0.728 1	0.682 3	0.671 8	0.000 1	0.000 10	0.407 1	0.136 3	0.022 3	0.575 1	0.436 5	0.259 3	0.428 1	0.048 5	0.000 1	0.000 4	0.879 5	0.000 1	0.480 2	0.000 1	0.133 9	0.597 1	0.000 1	0.690 2	0.000 1	0.000 1	0.009 15	0.000 14	0.921 4	0.000 9	0.151 4	0.000 1	0.000 7	0.000 1	0.109 7	0.494 11	0.622 2	0.394 8	0.073 10	0.141 8	0.798 1	0.528 7	0.026 5	0.000 1	0.551 4	0.000 2	0.000 2	0.134 6	0.717 7	0.000 2	0.000 1	0.000 1	0.188 3	0.000 8	0.000 3	0.791 2	0.000 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
OctFormer ScanNet200	0.326 12	0.539 9	0.265 9	0.131 11	0.806 8	0.670 12	0.943 9	0.535 11	0.662 4	0.705 15	0.423 8	0.407 5	0.505 12	0.003 10	0.765 12	0.582 7	0.686 14	0.227 15	0.680 7	0.943 10	0.601 3	0.854 10	0.892 5	0.335 5	0.417 16	0.357 9	0.724 9	0.453 10	0.632 6	0.596 4	0.432 3	0.783 10	0.512 15	0.021 12	0.244 14	0.637 1	0.000 1	0.787 11	0.873 9	0.743 10	0.000 16	0.000 7	0.534 8	0.110 3	0.499 6	0.289 9	0.626 6	0.620 11	0.168 14	0.204 2	0.849 9	0.679 7	0.117 4	0.633 11	0.684 3	0.650 7	0.552 5	0.684 8	0.312 3	0.000 3	0.175 10	0.429 10	0.865 4	0.413 4	0.837 11	0.000 6	0.145 9	0.626 8	0.451 7	0.487 11	0.513 3	0.000 1	0.529 7	0.613 12	0.000 4	0.033 6	0.000 1	0.000 5	0.828 4	0.871 3	0.622 8	0.587 8	0.411 7	0.137 10	0.645 13	0.343 11	0.000 3	0.000 7	0.000 1	0.022 11	0.000 3	0.026 16	0.829 10	0.000 1	0.022 7	0.089 6	0.842 3	0.253 14	0.318 10	0.296 3	0.178 10	0.291 3	0.224 2	0.584 2	0.200 13	0.132 8	0.000 4	0.128 10	0.227 13	0.000 3	0.230 12	0.047 10	0.149 7	0.331 9	0.412 9	0.618 6	0.164 9	0.102 10	0.522 1	0.000 1	0.655 4	0.378 12	0.469 14	0.000 1	0.000 10	0.000 11	0.105 8	0.000 7	0.000 8	0.483 3	0.000 11	0.000 7	0.028 7	0.000 1	0.000 4	0.906 1	0.000 1	0.339 14	0.000 1	0.000 12	0.457 8	0.000 1	0.612 7	0.000 1	0.000 1	0.408 3	0.000 14	0.900 10	0.000 9	0.000 10	0.000 1	0.029 6	0.000 1	0.074 13	0.455 14	0.479 5	0.427 6	0.079 8	0.140 9	0.496 7	0.414 13	0.022 6	0.000 1	0.471 13	0.000 2	0.000 2	0.000 11	0.722 6	0.000 2	0.000 1	0.000 1	0.138 13	0.000 8	0.000 3	0.000 10	0.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PonderV2 ScanNet200	0.346 5	0.552 7	0.270 7	0.175 8	0.810 7	0.682 9	0.950 4	0.560 6	0.641 9	0.761 3	0.398 12	0.357 9	0.570 7	0.113 2	0.804 5	0.603 6	0.750 6	0.283 4	0.681 6	0.952 5	0.548 5	0.874 4	0.852 12	0.290 11	0.700 2	0.356 10	0.792 4	0.445 11	0.545 12	0.436 11	0.351 12	0.787 9	0.611 7	0.050 8	0.290 13	0.519 12	0.000 1	0.825 9	0.888 4	0.842 3	0.259 3	0.100 2	0.558 6	0.070 11	0.497 7	0.247 13	0.457 10	0.889 3	0.248 8	0.106 9	0.817 12	0.691 6	0.094 6	0.729 6	0.636 6	0.620 11	0.503 10	0.660 12	0.243 6	0.000 3	0.212 6	0.590 4	0.860 7	0.400 5	0.881 8	0.000 6	0.202 2	0.622 9	0.408 10	0.499 8	0.261 10	0.000 1	0.385 9	0.636 10	0.000 4	0.000 9	0.000 1	0.000 5	0.433 15	0.843 6	0.660 6	0.574 11	0.481 3	0.336 4	0.677 8	0.486 5	0.000 3	0.030 3	0.000 1	0.034 5	0.000 3	0.080 6	0.869 9	0.000 1	0.000 9	0.000 10	0.540 9	0.727 3	0.232 16	0.115 10	0.186 9	0.193 7	0.000 13	0.403 11	0.326 6	0.103 13	0.000 4	0.290 3	0.392 8	0.000 3	0.346 9	0.062 9	0.424 4	0.375 6	0.431 5	0.667 4	0.115 13	0.082 11	0.239 6	0.000 1	0.504 13	0.606 8	0.584 11	0.000 1	0.002 8	0.186 8	0.104 9	0.000 7	0.394 4	0.384 7	0.083 8	0.000 7	0.007 8	0.000 1	0.000 4	0.880 4	0.000 1	0.377 9	0.000 1	0.263 5	0.565 2	0.000 1	0.608 8	0.000 1	0.000 1	0.304 7	0.009 9	0.924 3	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.128 3	0.584 3	0.475 6	0.412 7	0.076 9	0.269 3	0.621 5	0.509 8	0.010 7	0.000 1	0.491 10	0.063 1	0.000 2	0.472 4	0.880 2	0.000 2	0.000 1	0.000 1	0.179 4	0.125 2	0.000 3	0.441 8	0.000 1
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
BFANet ScanNet200	0.360 4	0.553 6	0.293 4	0.193 4	0.827 4	0.689 4	0.970 3	0.528 12	0.661 6	0.753 6	0.436 7	0.378 7	0.469 14	0.042 7	0.810 3	0.654 1	0.760 4	0.266 9	0.659 9	0.973 3	0.574 4	0.849 11	0.897 4	0.382 2	0.546 12	0.372 8	0.698 13	0.491 8	0.617 9	0.526 9	0.436 1	0.764 13	0.476 16	0.101 6	0.409 6	0.585 9	0.000 1	0.835 5	0.901 3	0.810 5	0.102 12	0.000 7	0.688 2	0.096 5	0.483 10	0.264 11	0.612 8	0.591 15	0.358 2	0.161 5	0.863 5	0.707 4	0.128 2	0.814 2	0.669 5	0.629 9	0.563 4	0.651 13	0.258 4	0.000 3	0.194 9	0.494 8	0.806 11	0.394 6	0.953 4	0.000 6	0.233 1	0.757 4	0.508 5	0.556 3	0.476 5	0.000 1	0.573 5	0.741 6	0.000 4	0.000 9	0.000 1	0.000 5	0.000 16	0.852 5	0.678 3	0.616 5	0.460 4	0.338 3	0.710 4	0.534 4	0.000 3	0.025 4	0.000 1	0.043 2	0.000 3	0.056 10	0.493 16	0.000 1	0.000 9	0.109 4	0.785 6	0.590 6	0.298 12	0.282 4	0.143 12	0.262 4	0.053 10	0.526 4	0.337 5	0.215 1	0.000 4	0.135 8	0.510 4	0.000 3	0.596 4	0.043 13	0.511 3	0.321 11	0.459 2	0.772 2	0.124 12	0.060 13	0.266 5	0.000 1	0.574 9	0.568 9	0.653 10	0.000 1	0.093 1	0.298 2	0.239 2	0.000 7	0.516 2	0.129 13	0.284 2	0.000 7	0.431 1	0.000 1	0.000 4	0.848 6	0.000 1	0.492 1	0.000 1	0.376 2	0.522 5	0.000 1	0.469 16	0.000 1	0.000 1	0.330 6	0.151 8	0.875 14	0.000 9	0.254 3	0.000 1	0.000 7	0.000 1	0.088 11	0.661 1	0.481 4	0.255 11	0.105 1	0.139 10	0.666 4	0.641 3	0.000 12	0.000 1	0.614 2	0.000 2	0.000 2	0.000 11	0.921 1	0.000 2	0.000 1	0.000 1	0.497 1	0.000 8	0.000 3	0.000 10	0.000 1

CeCo	0.340 6	0.551 8	0.247 12	0.181 5	0.784 12	0.661 13	0.939 12	0.564 5	0.624 12	0.721 11	0.484 4	0.429 4	0.575 4	0.027 8	0.774 11	0.503 13	0.753 5	0.242 12	0.656 10	0.945 9	0.534 7	0.865 6	0.860 10	0.177 16	0.616 8	0.400 4	0.818 2	0.579 1	0.615 10	0.367 13	0.408 6	0.726 14	0.633 4	0.162 1	0.360 8	0.619 2	0.000 1	0.828 8	0.873 9	0.924 2	0.109 11	0.083 3	0.564 5	0.057 14	0.475 12	0.266 10	0.781 1	0.767 7	0.257 6	0.100 10	0.825 10	0.663 9	0.048 14	0.620 13	0.551 11	0.595 12	0.532 7	0.692 7	0.246 5	0.000 3	0.213 5	0.615 1	0.861 6	0.376 7	0.900 7	0.000 6	0.102 15	0.660 7	0.321 14	0.547 4	0.226 12	0.000 1	0.311 12	0.742 5	0.011 3	0.006 8	0.000 1	0.000 5	0.546 14	0.824 8	0.345 13	0.665 3	0.450 5	0.435 1	0.683 7	0.411 7	0.338 1	0.000 7	0.000 1	0.030 8	0.000 3	0.068 7	0.892 7	0.000 1	0.063 4	0.000 10	0.257 12	0.304 13	0.387 5	0.079 13	0.228 5	0.190 10	0.000 13	0.586 1	0.347 4	0.133 7	0.000 4	0.037 12	0.377 9	0.000 3	0.384 8	0.006 15	0.003 12	0.421 3	0.410 10	0.643 5	0.171 8	0.121 8	0.142 11	0.000 1	0.510 12	0.447 11	0.474 13	0.000 1	0.000 10	0.286 3	0.083 10	0.000 7	0.000 8	0.603 1	0.096 7	0.063 4	0.000 10	0.000 1	0.000 4	0.898 3	0.000 1	0.429 6	0.000 1	0.400 1	0.550 3	0.000 1	0.633 6	0.000 1	0.000 1	0.377 4	0.000 14	0.916 5	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.102 10	0.499 9	0.296 13	0.463 5	0.089 5	0.304 1	0.740 2	0.401 15	0.010 7	0.000 1	0.560 3	0.000 2	0.000 2	0.709 2	0.652 9	0.000 2	0.000 1	0.000 1	0.143 7	0.000 8	0.000 3	0.609 3	0.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
PPT-SpUNet-F.T.	0.332 11	0.556 5	0.270 6	0.123 13	0.816 6	0.682 9	0.946 6	0.549 9	0.657 8	0.756 5	0.459 6	0.376 8	0.550 10	0.001 11	0.807 4	0.616 4	0.727 11	0.267 8	0.691 5	0.942 11	0.530 9	0.872 5	0.874 7	0.330 7	0.542 13	0.374 7	0.792 4	0.400 13	0.673 4	0.572 6	0.433 2	0.793 8	0.623 6	0.008 16	0.351 9	0.594 7	0.000 1	0.783 12	0.876 7	0.833 4	0.213 6	0.000 7	0.537 7	0.091 6	0.519 4	0.304 7	0.620 7	0.942 1	0.264 4	0.124 7	0.855 6	0.695 5	0.086 7	0.646 10	0.506 15	0.658 6	0.535 6	0.715 3	0.314 2	0.000 3	0.241 4	0.608 2	0.897 2	0.359 8	0.858 10	0.000 6	0.076 16	0.611 10	0.392 11	0.509 7	0.378 6	0.000 1	0.579 4	0.565 15	0.000 4	0.000 9	0.000 1	0.000 5	0.755 6	0.806 9	0.661 4	0.572 12	0.350 9	0.181 7	0.660 11	0.300 13	0.000 3	0.000 7	0.000 1	0.023 10	0.000 3	0.042 13	0.930 4	0.000 1	0.000 9	0.077 7	0.584 8	0.392 10	0.339 8	0.185 9	0.171 11	0.308 2	0.006 12	0.563 3	0.256 8	0.150 4	0.000 4	0.002 15	0.345 11	0.000 3	0.045 13	0.197 4	0.063 10	0.323 10	0.453 3	0.600 7	0.163 10	0.037 14	0.349 3	0.000 1	0.672 3	0.679 4	0.753 5	0.000 1	0.000 10	0.000 11	0.117 5	0.000 7	0.000 8	0.291 9	0.000 11	0.000 7	0.039 6	0.000 1	0.000 4	0.899 2	0.000 1	0.374 10	0.000 1	0.000 12	0.545 4	0.000 1	0.634 5	0.000 1	0.000 1	0.074 12	0.223 6	0.914 7	0.000 9	0.021 8	0.000 1	0.000 7	0.000 1	0.112 5	0.498 10	0.649 1	0.383 9	0.095 2	0.135 12	0.449 9	0.432 11	0.008 9	0.000 1	0.518 6	0.000 2	0.000 2	0.000 11	0.796 4	0.000 2	0.000 1	0.000 1	0.138 13	0.000 8	0.000 3	0.000 10	0.000 1
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OA-CNN-L_ScanNet200	0.333 10	0.558 4	0.269 8	0.124 12	0.821 5	0.703 3	0.946 6	0.569 4	0.662 4	0.748 8	0.487 3	0.455 3	0.572 6	0.000 12	0.789 9	0.534 8	0.736 8	0.271 7	0.713 4	0.949 6	0.498 13	0.877 3	0.860 10	0.332 6	0.706 1	0.474 2	0.788 6	0.406 12	0.637 5	0.495 10	0.355 11	0.805 7	0.592 11	0.015 13	0.396 7	0.602 5	0.000 1	0.799 10	0.876 7	0.713 12	0.276 2	0.000 7	0.493 12	0.080 8	0.448 14	0.363 4	0.661 4	0.833 6	0.262 5	0.125 6	0.823 11	0.665 8	0.076 8	0.720 8	0.557 10	0.637 8	0.517 8	0.672 9	0.227 7	0.000 3	0.158 11	0.496 7	0.843 10	0.352 9	0.835 12	0.000 6	0.103 14	0.711 5	0.527 3	0.526 5	0.320 8	0.000 1	0.568 6	0.625 11	0.067 1	0.000 9	0.000 1	0.001 4	0.806 5	0.836 7	0.621 9	0.591 7	0.373 8	0.314 5	0.668 9	0.398 8	0.003 2	0.000 7	0.000 1	0.016 15	0.024 2	0.043 12	0.906 6	0.000 1	0.052 5	0.000 10	0.384 11	0.330 12	0.342 7	0.100 11	0.223 6	0.183 12	0.112 6	0.476 5	0.313 7	0.130 9	0.196 3	0.112 11	0.370 10	0.000 3	0.234 11	0.071 8	0.160 6	0.403 4	0.398 13	0.492 14	0.197 5	0.076 12	0.272 4	0.000 1	0.200 16	0.560 10	0.735 7	0.000 1	0.000 10	0.000 11	0.110 7	0.002 6	0.021 7	0.412 6	0.000 11	0.000 7	0.000 10	0.000 1	0.000 4	0.794 10	0.000 1	0.445 5	0.000 1	0.022 10	0.509 6	0.000 1	0.517 12	0.000 1	0.000 1	0.001 16	0.245 5	0.915 6	0.024 6	0.089 6	0.000 1	0.262 2	0.000 1	0.103 9	0.524 7	0.392 10	0.515 2	0.013 16	0.251 4	0.411 11	0.662 2	0.001 11	0.000 1	0.473 12	0.000 2	0.000 2	0.150 5	0.699 8	0.000 2	0.000 1	0.000 1	0.166 5	0.000 8	0.024 2	0.000 10	0.000 1

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

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

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

LGround	0.272 14	0.485 14	0.184 14	0.106 14	0.778 14	0.676 11	0.932 14	0.479 16	0.572 14	0.718 13	0.399 11	0.265 14	0.453 15	0.085 3	0.745 14	0.446 14	0.726 12	0.232 14	0.622 14	0.901 14	0.512 11	0.826 14	0.786 15	0.178 15	0.549 11	0.277 14	0.659 14	0.381 14	0.518 13	0.295 16	0.323 13	0.777 11	0.599 9	0.028 10	0.321 10	0.363 15	0.000 1	0.708 14	0.858 14	0.746 9	0.063 13	0.022 5	0.457 14	0.077 9	0.476 11	0.243 14	0.402 13	0.397 16	0.233 9	0.077 14	0.720 16	0.610 15	0.103 5	0.629 12	0.437 16	0.626 10	0.446 13	0.702 5	0.190 12	0.005 1	0.058 15	0.322 13	0.702 15	0.244 14	0.768 13	0.000 6	0.134 12	0.552 14	0.279 15	0.395 14	0.147 15	0.000 1	0.207 14	0.612 13	0.000 4	0.000 9	0.000 1	0.000 5	0.658 10	0.566 14	0.323 14	0.525 14	0.229 11	0.179 8	0.467 16	0.154 15	0.000 3	0.002 5	0.000 1	0.051 1	0.000 3	0.127 2	0.703 11	0.000 1	0.000 9	0.216 1	0.112 15	0.358 11	0.547 1	0.187 8	0.092 15	0.156 16	0.055 9	0.296 14	0.252 9	0.143 5	0.000 4	0.014 13	0.398 7	0.000 3	0.028 15	0.173 6	0.000 14	0.265 15	0.348 14	0.415 15	0.179 7	0.019 15	0.218 7	0.000 1	0.597 8	0.274 16	0.565 12	0.000 1	0.012 7	0.000 11	0.039 15	0.022 3	0.000 8	0.117 14	0.000 11	0.000 7	0.000 10	0.000 1	0.000 4	0.324 15	0.000 1	0.384 8	0.000 1	0.000 12	0.251 16	0.000 1	0.566 10	0.000 1	0.000 1	0.066 13	0.404 3	0.886 13	0.199 2	0.000 10	0.000 1	0.059 5	0.000 1	0.136 1	0.540 5	0.127 16	0.295 10	0.085 6	0.143 7	0.514 6	0.413 14	0.000 12	0.000 1	0.498 7	0.000 2	0.000 2	0.000 11	0.623 11	0.000 2	0.000 1	0.000 1	0.132 15	0.000 8	0.000 3	0.000 10	0.000 1
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	0.766 16	0.659 14	0.930 16	0.494 13	0.542 16	0.700 16	0.314 16	0.215 16	0.430 16	0.121 1	0.697 16	0.441 15	0.683 15	0.235 13	0.609 16	0.895 15	0.476 14	0.816 15	0.770 16	0.186 13	0.634 6	0.216 16	0.734 8	0.340 15	0.471 15	0.307 15	0.293 16	0.591 16	0.542 14	0.076 7	0.205 15	0.464 14	0.000 1	0.484 16	0.832 16	0.766 7	0.052 14	0.000 7	0.413 15	0.059 13	0.418 15	0.222 15	0.318 16	0.609 13	0.206 12	0.112 8	0.743 13	0.625 13	0.076 8	0.579 15	0.548 12	0.590 13	0.371 15	0.552 16	0.081 15	0.003 2	0.142 13	0.201 15	0.638 16	0.233 15	0.686 16	0.000 6	0.142 10	0.444 16	0.375 12	0.247 16	0.198 13	0.000 1	0.128 16	0.454 16	0.019 2	0.097 1	0.000 1	0.000 5	0.553 13	0.557 15	0.373 12	0.545 13	0.164 13	0.014 15	0.547 15	0.174 14	0.000 3	0.002 5	0.000 1	0.037 3	0.000 3	0.063 9	0.664 13	0.000 1	0.000 9	0.130 2	0.170 13	0.152 16	0.335 9	0.079 13	0.110 14	0.175 13	0.098 8	0.175 16	0.166 14	0.045 16	0.207 2	0.014 13	0.465 5	0.000 3	0.001 16	0.001 16	0.046 11	0.299 14	0.327 15	0.537 12	0.033 15	0.012 16	0.186 9	0.000 1	0.205 15	0.377 13	0.463 15	0.000 1	0.058 3	0.000 11	0.055 14	0.041 1	0.000 8	0.105 15	0.000 11	0.000 7	0.000 10	0.000 1	0.000 4	0.398 14	0.000 1	0.308 16	0.000 1	0.000 12	0.319 14	0.000 1	0.543 11	0.000 1	0.000 1	0.062 14	0.004 12	0.862 15	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.123 4	0.316 15	0.225 14	0.250 12	0.094 3	0.180 6	0.332 13	0.441 10	0.000 12	0.000 1	0.310 16	0.000 2	0.000 2	0.000 11	0.592 13	0.000 2	0.000 1	0.000 1	0.203 2	0.000 8	0.000 3	0.000 10	0.000 1
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	0.771 15	0.650 15	0.932 14	0.483 15	0.571 15	0.710 14	0.331 15	0.250 15	0.492 13	0.044 6	0.703 15	0.419 16	0.606 16	0.227 15	0.621 15	0.865 16	0.531 8	0.771 16	0.813 13	0.291 10	0.484 14	0.242 15	0.612 16	0.282 16	0.440 16	0.351 14	0.299 14	0.622 15	0.593 10	0.027 11	0.293 12	0.310 16	0.000 1	0.757 13	0.858 14	0.737 11	0.150 8	0.164 1	0.368 16	0.084 7	0.381 16	0.142 16	0.357 14	0.720 9	0.214 11	0.092 13	0.724 15	0.596 16	0.056 13	0.655 9	0.525 13	0.581 14	0.352 16	0.594 15	0.056 16	0.000 3	0.014 16	0.224 14	0.772 14	0.205 16	0.720 15	0.000 6	0.159 7	0.531 15	0.163 16	0.294 15	0.136 16	0.000 1	0.169 15	0.589 14	0.000 4	0.000 9	0.000 1	0.002 3	0.663 9	0.466 16	0.265 16	0.582 9	0.337 10	0.016 14	0.559 14	0.084 16	0.000 3	0.000 7	0.000 1	0.036 4	0.000 3	0.125 3	0.670 12	0.000 1	0.102 2	0.071 8	0.164 14	0.406 9	0.386 6	0.046 15	0.068 16	0.159 14	0.117 5	0.284 15	0.111 15	0.094 15	0.000 4	0.000 16	0.197 16	0.000 3	0.044 14	0.013 14	0.002 13	0.228 16	0.307 16	0.588 11	0.025 16	0.545 4	0.134 14	0.000 1	0.655 4	0.302 14	0.282 16	0.000 1	0.060 2	0.000 11	0.035 16	0.000 7	0.000 8	0.097 16	0.000 11	0.000 7	0.005 9	0.000 1	0.000 4	0.096 16	0.000 1	0.334 15	0.000 1	0.000 12	0.274 15	0.000 1	0.513 13	0.000 1	0.000 1	0.280 8	0.194 7	0.897 11	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.108 8	0.279 16	0.189 15	0.141 16	0.059 12	0.272 2	0.307 16	0.445 9	0.003 10	0.000 1	0.353 15	0.000 2	0.026 1	0.000 11	0.581 14	0.001 1	0.000 1	0.000 1	0.093 16	0.002 7	0.000 3	0.000 10	0.000 1
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	head ap	common ap	tail ap	chair	table	door	couch	cabinet	shelf	desk	office chair	bed	pillow	sink	picture	window	toilet	bookshelf	monitor	curtain	book	coffee table	box	refrigerator	lamp	kitchen cabinet	towel	clothes	tv	nightstand	counter	dresser	stool	cushion	plant	ceiling	bathtub	end table	dining table	keyboard	bag	backpack	toilet paper	printer	tv stand	whiteboard	blanket	shower curtain	trash can	closet	stairs	microwave	stove	shoe	computer tower	bottle	bin	bench	board	washing machine	mirror	copier	basket	sofa chair	file cabinet	fan	laptop	shower	paper	person	paper towel dispenser	oven	blinds	rack	plate	blackboard	piano	suitcase	radiator	recycling bin	wardrobe	soap dispenser	telephone	bucket	clock	stand	light	laundry basket	pipe	clothes dryer	seat	speaker	column	bicycle	ladder	bathroom stall	shower wall	cup	jacket	storage bin	coffee maker	dishwasher	paper towel roll	machine	mat	windowsill	bar	toaster	bulletin board	ironing board	kitchen counter	doorframe	toilet paper dispenser	mini fridge	fire extinguisher	ball	hat	shower curtain rod	water cooler	paper cutter	tray	ledge	mouse	cart	storage container	scale	tissue box	light switch	power outlet	decoration	sign	projector	vacuum cleaner	candle	plunger	stuffed animal	headphones	dish rack	broom	range hood	water bottle	vent	shower floor	water pitcher	mailbox	bowl	paper bag	music stand	projector screen	laundry detergent	object	bathroom vanity	laundry hamper	bathroom stall door	ceiling light	trash bin	dumbbell	stair rail	tube	bathroom cabinet	closet rod	coffee kettle	structure	shower head	keyboard piano	case of water bottles	coat rack	storage organizer	folded chair	fire alarm	power strip	calendar	poster

Mask3D Scannet200	0.278 1	0.383 1	0.263 1	0.168 1	0.661 2	0.465 1	0.572 1	0.665 3	0.391 1	0.121 4	0.304 1	0.015 2	0.647 1	0.349 1	0.474 1	0.489 1	0.321 1	0.816 5	0.351 3	0.722 1	0.402 4	0.195 1	0.515 3	0.082 1	0.795 1	0.215 2	0.396 1	0.377 1	0.082 4	0.724 1	0.586 1	0.015 2	0.277 1	0.377 5	0.201 1	0.475 2	0.572 1	0.778 3	0.089 1	0.759 1	0.556 1	0.068 1	0.506 1	0.467 1	0.323 3	0.778 2	0.427 1	0.027 2	0.789 1	0.744 1	0.003 1	0.570 2	0.561 1	0.337 1	0.265 1	0.711 1	0.258 1	0.031 1	0.569 1	0.311 1	0.441 1	0.179 1	1.000 1	0.000 1	0.233 2	0.411 2	0.283 2	0.380 1	0.667 1	0.016 1	0.048 3	0.418 2	0.139 1	0.173 1	0.000 1	0.086 1	0.014 2	0.500 1	0.384 1	0.497 1	0.044 3	0.032 2	0.752 1	0.287 1	0.003 1	0.000 1	0.007 1	0.208 1	0.000 1	0.001 2	0.349 1	0.008 2	0.014 2	0.509 1	0.500 1	0.323 1	0.023 2	0.176 1	0.107 1	0.105 3	0.000 1	0.605 1	0.378 1	0.016 1	0.000 1	0.400 1	0.192 1	0.000 1	0.048 2	0.037 2	0.000 1	0.275 1	0.119 1	0.810 1	0.258 1	0.006 3	0.083 5	0.000 1	0.568 2	0.377 2	0.708 1	0.000 1	0.005 2	0.147 1	0.014 2	0.000 2	0.556 1	0.085 1	0.325 1	0.500 1	0.083 1	0.004 2	0.000 1	0.590 1	0.000 1	0.365 1	0.000 1	0.116 1	0.491 1	0.000 1	0.626 1	0.000 1	0.000 1	0.579 1	0.391 1	0.050 4	0.000 1	0.028 1	0.000 1	0.222 1	0.000 1	0.063 1	0.302 1	0.356 1	0.149 4	0.573 1	0.415 1	0.013 5	0.002 4	0.004 1	0.000 1	0.005 4	0.000 1	0.000 1	0.444 1	0.514 1	0.000 1	0.028 1	0.000 2	0.156 2	0.267 1	0.000 2	1.000 1	0.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
LGround Inst.	0.154 3	0.275 3	0.108 3	0.060 3	0.573 3	0.381 3	0.434 3	0.654 4	0.190 4	0.141 2	0.097 3	0.000 3	0.503 3	0.180 3	0.252 3	0.242 4	0.242 3	0.881 3	0.448 1	0.494 3	0.429 3	0.078 2	0.364 5	0.024 3	0.654 2	0.213 4	0.222 3	0.239 3	0.099 3	0.616 2	0.363 3	0.000 3	0.092 3	0.444 3	0.000 3	0.383 4	0.209 5	0.815 2	0.030 3	0.000 3	0.166 3	0.002 4	0.295 5	0.099 4	0.364 2	0.778 2	0.177 3	0.001 4	0.427 5	0.585 4	0.000 2	0.470 3	0.268 5	0.205 3	0.045 3	0.642 2	0.007 3	0.000 3	0.333 5	0.148 3	0.407 3	0.130 2	1.000 1	0.000 1	0.156 4	0.189 3	0.097 4	0.169 2	0.000 5	0.000 2	0.056 2	0.400 3	0.000 3	0.000 2	0.000 1	0.000 2	0.556 1	0.278 3	0.203 3	0.323 4	0.019 4	0.000 3	0.402 4	0.026 3	0.000 2	0.000 1	0.000 3	0.044 3	0.000 1	0.000 3	0.037 4	0.000 3	0.000 3	0.181 2	0.000 2	0.127 3	0.006 4	0.028 4	0.023 3	0.115 2	0.000 1	0.327 2	0.267 2	0.000 2	0.000 1	0.000 4	0.028 3	0.000 1	0.000 3	0.000 3	0.000 1	0.003 3	0.048 2	0.135 4	0.222 2	0.089 2	0.278 1	0.000 1	0.514 3	0.333 4	0.611 2	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.037 3	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.322 2	0.000 1	0.209 2	0.000 1	0.000 3	0.278 2	0.000 1	0.302 3	0.000 1	0.000 1	0.143 3	0.148 3	0.000 5	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.015 3	0.064 5	0.000 3	0.272 2	0.031 5	0.000 4	0.257 2	0.028 2	0.000 2	0.000 1	0.041 2	0.000 1	0.000 1	0.000 2	0.222 5	0.000 1	0.000 2	0.000 2	0.000 5	0.000 2	0.000 2	0.000 2	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.	0.130 4	0.246 4	0.083 4	0.043 5	0.547 5	0.236 4	0.415 4	0.672 2	0.141 5	0.133 3	0.067 4	0.000 3	0.521 2	0.114 5	0.238 4	0.289 2	0.232 4	0.883 2	0.182 5	0.373 5	0.486 1	0.076 3	0.488 4	0.022 4	0.529 4	0.199 5	0.110 4	0.217 4	0.100 2	0.460 4	0.319 4	0.000 3	0.025 5	0.472 1	0.000 3	0.394 3	0.210 4	0.537 4	0.004 4	0.000 3	0.083 5	0.000 5	0.299 4	0.061 5	0.201 5	0.761 4	0.084 4	0.008 3	0.720 3	0.557 5	0.000 2	0.317 5	0.280 3	0.094 5	0.020 5	0.564 5	0.000 4	0.000 3	0.400 3	0.048 4	0.259 4	0.101 3	1.000 1	0.000 1	0.190 3	0.142 5	0.094 5	0.137 3	0.089 3	0.000 2	0.101 1	0.355 5	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.444 2	0.082 5	0.384 2	0.000 5	0.000 3	0.334 5	0.004 5	0.000 2	0.000 1	0.000 3	0.041 4	0.000 1	0.000 3	0.026 5	0.000 3	0.000 3	0.000 4	0.000 2	0.082 5	0.022 3	0.000 5	0.021 4	0.088 4	0.000 1	0.241 4	0.033 4	0.000 2	0.000 1	0.067 3	0.000 5	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.026 4	0.262 2	0.016 4	0.000 4	0.278 1	0.000 1	0.500 4	0.394 1	0.028 5	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.019 4	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.156 5	0.000 1	0.032 5	0.000 1	0.000 3	0.194 5	0.000 1	0.248 4	0.000 1	0.000 1	0.099 4	0.019 4	0.308 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.007 4	0.122 2	0.000 3	0.175 3	0.063 2	0.000 4	0.271 1	0.000 5	0.000 2	0.000 1	0.000 5	0.000 1	0.000 1	0.000 2	0.278 2	0.000 1	0.000 2	0.000 2	0.111 3	0.000 2	0.000 2	0.000 2	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.	0.123 5	0.223 5	0.082 5	0.046 4	0.564 4	0.152 5	0.394 5	0.578 5	0.235 2	0.116 5	0.034 5	0.000 3	0.348 5	0.119 4	0.297 2	0.285 3	0.202 5	0.838 4	0.323 4	0.407 4	0.184 5	0.037 5	0.516 2	0.013 5	0.424 5	0.214 3	0.093 5	0.105 5	0.078 5	0.542 3	0.250 5	0.000 3	0.064 4	0.444 3	0.000 3	0.224 5	0.231 3	0.537 4	0.001 5	0.000 3	0.126 4	0.004 3	0.308 3	0.193 3	0.244 4	0.343 5	0.228 2	0.000 5	0.441 4	0.588 3	0.000 2	0.338 4	0.275 4	0.189 4	0.030 4	0.600 4	0.000 4	0.000 3	0.378 4	0.000 5	0.108 5	0.098 4	1.000 1	0.000 1	0.096 5	0.172 4	0.144 3	0.011 5	0.125 2	0.000 2	0.000 5	0.376 4	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.042 5	0.141 4	0.377 3	0.051 2	0.000 3	0.483 3	0.017 4	0.000 2	0.000 1	0.000 3	0.022 5	0.000 1	0.000 3	0.065 3	0.000 3	0.000 3	0.000 4	0.000 2	0.094 4	0.000 5	0.042 3	0.000 5	0.064 5	0.000 1	0.259 3	0.089 3	0.000 2	0.000 1	0.000 4	0.022 4	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.018 5	0.111 5	0.000 5	0.000 4	0.278 1	0.000 1	0.444 5	0.333 4	0.333 4	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.000 5	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.267 3	0.000 1	0.184 3	0.000 1	0.000 3	0.211 4	0.000 1	0.378 2	0.000 1	0.000 1	0.063 5	0.000 5	0.275 3	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.007 5	0.105 3	0.000 3	0.032 5	0.045 3	0.198 3	0.171 4	0.028 2	0.000 2	0.000 1	0.006 3	0.000 1	0.000 1	0.000 2	0.278 2	0.000 1	0.000 2	0.000 2	0.044 4	0.000 2	0.000 2	0.000 2	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
TD3D Scannet200	0.211 2	0.332 2	0.177 2	0.103 2	0.662 1	0.413 2	0.463 2	0.705 1	0.192 3	0.145 1	0.266 2	0.215 1	0.452 4	0.209 2	0.222 5	0.219 5	0.315 2	0.893 1	0.380 2	0.617 2	0.439 2	0.047 4	0.646 1	0.080 2	0.610 3	0.253 1	0.237 2	0.293 2	0.135 1	0.379 5	0.494 2	0.048 1	0.252 2	0.451 2	0.184 2	0.483 1	0.395 2	0.852 1	0.083 2	0.551 2	0.278 2	0.036 2	0.337 2	0.266 2	0.544 1	0.963 1	0.079 5	0.039 1	0.740 2	0.604 2	0.000 2	0.586 1	0.283 2	0.282 2	0.059 2	0.633 3	0.028 2	0.004 2	0.559 2	0.309 2	0.420 2	0.028 5	1.000 1	0.000 1	0.456 1	0.411 1	0.372 1	0.060 4	0.046 4	0.000 2	0.040 4	0.694 1	0.083 2	0.000 2	0.000 1	0.000 2	0.000 3	0.083 4	0.252 2	0.260 5	0.200 1	0.160 1	0.669 2	0.111 2	0.000 2	0.000 1	0.006 2	0.169 2	0.000 1	0.007 1	0.296 2	0.032 1	0.074 1	0.139 3	0.000 2	0.321 2	0.031 1	0.108 2	0.088 2	0.157 1	0.000 1	0.231 5	0.026 5	0.000 2	0.000 1	0.356 2	0.052 2	0.000 1	0.240 1	0.147 1	0.000 1	0.015 2	0.046 3	0.144 3	0.073 3	0.414 1	0.222 4	0.000 1	0.806 1	0.343 3	0.486 3	0.000 1	0.008 1	0.038 2	0.083 1	0.002 1	0.028 2	0.074 2	0.032 2	0.150 2	0.039 2	0.008 1	0.000 1	0.250 4	0.000 1	0.125 4	0.000 1	0.052 2	0.260 3	0.000 1	0.143 5	0.000 1	0.000 1	0.543 2	0.207 2	0.404 1	0.000 1	0.003 2	0.000 1	0.000 2	0.000 1	0.037 2	0.093 4	0.272 2	0.342 1	0.039 4	0.281 2	0.249 3	0.224 1	0.000 2	0.000 1	0.074 1	0.000 1	0.000 1	0.000 2	0.278 2	0.000 1	0.000 2	0.889 1	0.323 1	0.000 2	0.014 1	0.000 2	0.000 1
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024

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 14	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 9	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 33	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 26	0.788 4	0.853 19	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 32	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 18	0.781 7	0.858 13	0.575 7	0.831 36	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 23	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 49	0.805 16	0.708 9	0.916 36	0.898 4	0.801 3

TTT-KD	0.773 7	0.646 95	0.818 15	0.809 38	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 34	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

PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 36	0.829 28	0.751 26	0.854 17	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 29	0.802 17	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
OctFormer	0.766 9	0.925 7	0.808 26	0.849 11	0.786 5	0.846 29	0.566 10	0.876 19	0.690 11	0.674 16	0.960 19	0.576 21	0.226 70	0.753 28	0.904 11	0.777 16	0.815 8	0.722 6	0.923 31	0.877 16	0.776 9
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
OccuSeg+Semantic	0.764 11	0.758 61	0.796 34	0.839 21	0.746 29	0.907 1	0.562 12	0.850 28	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 20	0.757 21	0.853 19	0.580 4	0.848 29	0.709 4	0.643 28	0.958 23	0.587 15	0.295 37	0.753 28	0.884 22	0.758 23	0.815 8	0.725 4	0.927 27	0.867 25	0.743 19

O-CNN	0.762 13	0.924 8	0.823 8	0.844 17	0.770 13	0.852 21	0.577 5	0.847 31	0.711 3	0.640 32	0.958 23	0.592 11	0.217 76	0.762 21	0.888 19	0.758 23	0.813 12	0.726 3	0.932 25	0.868 24	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 18	0.762 17	0.856 14	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 39	0.678 22	0.926 29	0.880 13	0.799 4

DTC	0.757 15	0.843 28	0.820 11	0.847 14	0.791 2	0.862 11	0.511 37	0.870 21	0.707 5	0.652 24	0.954 38	0.604 8	0.279 47	0.760 22	0.942 2	0.734 30	0.766 48	0.701 13	0.884 58	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 37	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 52	0.770 19	0.787 35	0.691 17	0.936 19	0.880 13	0.761 13

PNE	0.755 17	0.786 45	0.835 5	0.834 25	0.758 19	0.849 24	0.570 9	0.836 35	0.648 31	0.668 20	0.978 5	0.581 20	0.367 7	0.683 38	0.856 32	0.804 7	0.801 21	0.678 22	0.961 5	0.889 6	0.716 33
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer	0.755 17	0.927 6	0.822 9	0.836 23	0.801 1	0.849 24	0.516 34	0.864 25	0.651 29	0.680 13	0.958 23	0.584 18	0.282 44	0.759 24	0.855 34	0.728 32	0.802 17	0.678 22	0.880 63	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 44	0.689 43	0.825 50	0.556 14	0.867 22	0.681 16	0.602 48	0.960 19	0.555 31	0.365 8	0.779 9	0.859 29	0.747 26	0.795 29	0.717 7	0.917 35	0.856 34	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 25	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 36	0.752 24	0.839 36	0.485 51	0.842 32	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 36	0.856 34	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 40	0.750 28	0.856 14	0.524 30	0.881 18	0.588 57	0.642 31	0.977 9	0.591 12	0.274 50	0.781 8	0.929 4	0.804 7	0.796 26	0.642 38	0.947 10	0.885 10	0.715 34
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP	0.748 23	0.623 98	0.804 28	0.859 4	0.745 30	0.824 52	0.501 41	0.912 7	0.690 11	0.685 10	0.956 29	0.567 24	0.320 27	0.768 18	0.918 7	0.720 37	0.802 17	0.676 26	0.921 33	0.881 12	0.779 8

StratifiedFormer	0.747 24	0.901 14	0.803 29	0.845 16	0.757 21	0.846 29	0.512 36	0.825 39	0.696 9	0.645 26	0.956 29	0.576 21	0.262 61	0.744 33	0.861 28	0.742 27	0.770 46	0.705 11	0.899 48	0.860 31	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
VMNet	0.746 25	0.870 20	0.838 3	0.858 5	0.729 35	0.850 23	0.501 41	0.874 20	0.587 58	0.658 22	0.956 29	0.564 26	0.299 35	0.765 20	0.900 14	0.716 40	0.812 13	0.631 43	0.939 16	0.858 32	0.709 35
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion	0.746 25	0.771 55	0.819 13	0.848 13	0.702 41	0.865 10	0.397 88	0.899 12	0.699 7	0.664 21	0.948 59	0.588 14	0.330 22	0.746 32	0.851 38	0.764 21	0.796 26	0.704 12	0.935 20	0.866 26	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
DiffSeg3D2	0.745 27	0.725 78	0.814 19	0.837 22	0.751 26	0.831 44	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 43	0.798 24	0.690 19	0.923 31	0.875 20	0.757 14

Retro-FPN	0.744 28	0.842 29	0.800 30	0.767 58	0.740 31	0.836 39	0.541 21	0.914 6	0.672 21	0.626 36	0.958 23	0.552 32	0.272 52	0.777 10	0.886 21	0.696 50	0.801 21	0.674 29	0.941 14	0.858 32	0.717 31
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 99	0.799 33	0.849 11	0.730 34	0.822 54	0.493 48	0.897 13	0.664 22	0.681 12	0.955 32	0.562 28	0.378 4	0.760 22	0.903 12	0.738 28	0.801 21	0.673 30	0.907 40	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 65	0.775 9	0.843 32	0.528 29	0.897 13	0.679 18	0.674 16	0.954 38	0.583 19	0.322 26	0.782 6	0.720 67	0.802 9	0.785 36	0.707 10	0.935 20	0.863 28	0.745 16

SAT	0.742 31	0.860 23	0.765 53	0.819 31	0.769 15	0.848 26	0.533 25	0.829 37	0.663 23	0.631 35	0.955 32	0.586 16	0.274 50	0.753 28	0.896 16	0.729 31	0.760 54	0.666 32	0.921 33	0.855 36	0.733 22

LRPNet	0.742 31	0.816 37	0.806 27	0.807 40	0.752 24	0.828 48	0.575 7	0.839 34	0.699 7	0.637 33	0.954 38	0.520 43	0.320 27	0.755 27	0.834 42	0.760 22	0.772 43	0.676 26	0.915 38	0.862 29	0.717 31

LargeKernel3D	0.739 33	0.909 11	0.820 11	0.806 42	0.740 31	0.852 21	0.545 19	0.826 38	0.594 56	0.643 28	0.955 32	0.541 34	0.263 60	0.723 36	0.858 31	0.775 18	0.767 47	0.678 22	0.933 23	0.848 41	0.694 40
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 9	0.754 23	0.854 17	0.491 50	0.866 23	0.596 55	0.686 9	0.955 32	0.536 36	0.342 15	0.624 53	0.869 25	0.787 12	0.802 17	0.628 44	0.927 27	0.875 20	0.704 37

MinkowskiNet	0.736 34	0.859 24	0.818 15	0.832 27	0.709 39	0.840 34	0.521 32	0.853 27	0.660 25	0.643 28	0.951 49	0.544 33	0.286 42	0.731 34	0.893 17	0.675 58	0.772 43	0.683 21	0.874 69	0.852 39	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 36	0.873 6	0.530 28	0.824 40	0.489 90	0.647 25	0.978 5	0.609 5	0.336 18	0.624 53	0.733 61	0.758 23	0.776 41	0.570 68	0.949 8	0.877 16	0.728 24

SparseConvNet	0.725 37	0.647 94	0.821 10	0.846 15	0.721 37	0.869 7	0.533 25	0.754 61	0.603 51	0.614 40	0.955 32	0.572 23	0.325 24	0.710 37	0.870 24	0.724 35	0.823 3	0.628 44	0.934 22	0.865 27	0.683 43

PointTransformer++	0.725 37	0.727 77	0.811 23	0.819 31	0.765 16	0.841 33	0.502 40	0.814 45	0.621 41	0.623 38	0.955 32	0.556 30	0.284 43	0.620 55	0.866 26	0.781 15	0.757 58	0.648 35	0.932 25	0.862 29	0.709 35

MatchingNet	0.724 39	0.812 39	0.812 21	0.810 37	0.735 33	0.834 41	0.495 47	0.860 26	0.572 64	0.602 48	0.954 38	0.512 45	0.280 46	0.757 25	0.845 40	0.725 34	0.780 38	0.606 54	0.937 18	0.851 40	0.700 39

INS-Conv-semantic	0.717 40	0.751 64	0.759 57	0.812 35	0.704 40	0.868 8	0.537 24	0.842 32	0.609 47	0.608 44	0.953 43	0.534 38	0.293 38	0.616 56	0.864 27	0.719 39	0.793 30	0.640 39	0.933 23	0.845 45	0.663 48

PointMetaBase	0.714 41	0.835 30	0.785 42	0.821 29	0.684 45	0.846 29	0.531 27	0.865 24	0.614 42	0.596 52	0.953 43	0.500 48	0.246 66	0.674 39	0.888 19	0.692 51	0.764 50	0.624 46	0.849 85	0.844 46	0.675 45

contrastBoundary	0.705 42	0.769 58	0.775 47	0.809 38	0.687 44	0.820 57	0.439 76	0.812 46	0.661 24	0.591 54	0.945 67	0.515 44	0.171 95	0.633 50	0.856 32	0.720 37	0.796 26	0.668 31	0.889 55	0.847 42	0.689 41
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 43	0.774 53	0.800 30	0.793 49	0.760 18	0.847 28	0.471 55	0.802 49	0.463 97	0.634 34	0.968 13	0.491 51	0.271 54	0.726 35	0.910 9	0.706 45	0.815 8	0.551 80	0.878 64	0.833 47	0.570 80

RFCR	0.702 44	0.889 17	0.745 67	0.813 34	0.672 48	0.818 62	0.493 48	0.815 44	0.623 39	0.610 42	0.947 61	0.470 60	0.249 65	0.594 59	0.848 39	0.705 46	0.779 39	0.646 36	0.892 53	0.823 53	0.611 63
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 45	0.825 34	0.796 34	0.723 66	0.716 38	0.832 43	0.433 78	0.816 42	0.634 36	0.609 43	0.969 11	0.418 86	0.344 14	0.559 71	0.833 43	0.715 41	0.808 15	0.560 74	0.902 45	0.847 42	0.680 44

JSENet	0.699 46	0.881 19	0.762 54	0.821 29	0.667 49	0.800 74	0.522 31	0.792 52	0.613 43	0.607 45	0.935 87	0.492 50	0.205 81	0.576 64	0.853 36	0.691 52	0.758 56	0.652 34	0.872 72	0.828 50	0.649 52
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 47	0.743 67	0.794 36	0.655 89	0.684 45	0.822 54	0.497 46	0.719 71	0.622 40	0.617 39	0.977 9	0.447 73	0.339 16	0.750 31	0.664 79	0.703 48	0.790 33	0.596 58	0.946 12	0.855 36	0.647 53
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 48	0.732 72	0.772 48	0.786 50	0.677 47	0.866 9	0.517 33	0.848 29	0.509 83	0.626 36	0.952 47	0.536 36	0.225 72	0.545 77	0.704 70	0.689 55	0.810 14	0.564 73	0.903 44	0.854 38	0.729 23
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 49	0.884 18	0.754 61	0.795 47	0.647 56	0.818 62	0.422 80	0.802 49	0.612 44	0.604 46	0.945 67	0.462 63	0.189 89	0.563 70	0.853 36	0.726 33	0.765 49	0.632 42	0.904 42	0.821 56	0.606 67
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 50	0.704 84	0.741 71	0.754 62	0.656 51	0.829 46	0.501 41	0.741 66	0.609 47	0.548 61	0.950 53	0.522 42	0.371 5	0.633 50	0.756 56	0.715 41	0.771 45	0.623 47	0.861 80	0.814 59	0.658 49
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 51	0.866 21	0.748 64	0.819 31	0.645 58	0.794 77	0.450 66	0.802 49	0.587 58	0.604 46	0.945 67	0.464 62	0.201 84	0.554 73	0.840 41	0.723 36	0.732 69	0.602 56	0.907 40	0.822 55	0.603 70

DGNet	0.684 52	0.712 83	0.784 43	0.782 54	0.658 50	0.835 40	0.499 45	0.823 41	0.641 33	0.597 51	0.950 53	0.487 53	0.281 45	0.575 65	0.619 83	0.647 71	0.764 50	0.620 49	0.871 75	0.846 44	0.688 42

VACNN++	0.684 52	0.728 76	0.757 60	0.776 55	0.690 42	0.804 72	0.464 60	0.816 42	0.577 63	0.587 55	0.945 67	0.508 47	0.276 49	0.671 40	0.710 68	0.663 63	0.750 62	0.589 63	0.881 61	0.832 49	0.653 51

KP-FCNN	0.684 52	0.847 27	0.758 59	0.784 52	0.647 56	0.814 65	0.473 54	0.772 55	0.605 49	0.594 53	0.935 87	0.450 71	0.181 92	0.587 60	0.805 51	0.690 53	0.785 36	0.614 50	0.882 60	0.819 57	0.632 59
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
PointContrast_LA_SEM	0.683 55	0.757 62	0.784 43	0.786 50	0.639 60	0.824 52	0.408 83	0.775 54	0.604 50	0.541 63	0.934 91	0.532 39	0.269 56	0.552 74	0.777 54	0.645 74	0.793 30	0.640 39	0.913 39	0.824 52	0.671 46

Superpoint Network	0.683 55	0.851 26	0.728 75	0.800 46	0.653 53	0.806 70	0.468 57	0.804 47	0.572 64	0.602 48	0.946 64	0.453 70	0.239 69	0.519 83	0.822 45	0.689 55	0.762 53	0.595 60	0.895 51	0.827 51	0.630 60

VI-PointConv	0.676 57	0.770 57	0.754 61	0.783 53	0.621 64	0.814 65	0.552 16	0.758 59	0.571 66	0.557 59	0.954 38	0.529 40	0.268 58	0.530 80	0.682 74	0.675 58	0.719 72	0.603 55	0.888 56	0.833 47	0.665 47
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 58	0.789 44	0.748 64	0.763 60	0.635 62	0.814 65	0.407 85	0.747 63	0.581 62	0.573 56	0.950 53	0.484 54	0.271 54	0.607 57	0.754 57	0.649 68	0.774 42	0.596 58	0.883 59	0.823 53	0.606 67

SALANet	0.670 59	0.816 37	0.770 51	0.768 57	0.652 54	0.807 69	0.451 63	0.747 63	0.659 27	0.545 62	0.924 98	0.473 59	0.149 105	0.571 67	0.811 50	0.635 77	0.746 63	0.623 47	0.892 53	0.794 71	0.570 80

O3DSeg	0.668 60	0.822 35	0.771 50	0.496 109	0.651 55	0.833 42	0.541 21	0.761 58	0.555 72	0.611 41	0.966 14	0.489 52	0.370 6	0.388 102	0.580 86	0.776 17	0.751 60	0.570 68	0.956 6	0.817 58	0.646 54

PointConv	0.666 61	0.781 48	0.759 57	0.699 74	0.644 59	0.822 54	0.475 53	0.779 53	0.564 69	0.504 80	0.953 43	0.428 80	0.203 83	0.586 62	0.754 57	0.661 64	0.753 59	0.588 64	0.902 45	0.813 61	0.642 55
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 61	0.703 85	0.781 45	0.751 64	0.655 52	0.830 45	0.471 55	0.769 56	0.474 93	0.537 65	0.951 49	0.475 58	0.279 47	0.635 48	0.698 73	0.675 58	0.751 60	0.553 79	0.816 92	0.806 63	0.703 38
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 63	0.746 65	0.708 78	0.722 67	0.638 61	0.820 57	0.451 63	0.566 99	0.599 53	0.541 63	0.950 53	0.510 46	0.313 29	0.648 45	0.819 48	0.616 82	0.682 87	0.590 62	0.869 76	0.810 62	0.656 50
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 64	0.778 49	0.702 81	0.806 42	0.619 65	0.813 68	0.468 57	0.693 79	0.494 86	0.524 72	0.941 79	0.449 72	0.298 36	0.510 85	0.821 46	0.675 58	0.727 71	0.568 71	0.826 90	0.803 65	0.637 57
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 65	0.698 87	0.743 69	0.650 90	0.564 82	0.820 57	0.505 39	0.758 59	0.631 37	0.479 84	0.945 67	0.480 56	0.226 70	0.572 66	0.774 55	0.690 53	0.735 67	0.614 50	0.853 84	0.776 86	0.597 73
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 66	0.752 63	0.734 73	0.664 87	0.583 77	0.815 64	0.399 87	0.754 61	0.639 34	0.535 67	0.942 77	0.470 60	0.309 31	0.665 41	0.539 89	0.650 67	0.708 77	0.635 41	0.857 83	0.793 73	0.642 55
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 67	0.778 49	0.731 74	0.699 74	0.577 78	0.829 46	0.446 68	0.736 67	0.477 92	0.523 74	0.945 67	0.454 67	0.269 56	0.484 92	0.749 60	0.618 80	0.738 64	0.599 57	0.827 89	0.792 76	0.621 62

PointConv-SFPN	0.641 68	0.776 51	0.703 80	0.721 68	0.557 85	0.826 49	0.451 63	0.672 85	0.563 70	0.483 83	0.943 76	0.425 83	0.162 100	0.644 46	0.726 62	0.659 65	0.709 76	0.572 67	0.875 67	0.786 81	0.559 86

MVPNet	0.641 68	0.831 31	0.715 76	0.671 84	0.590 73	0.781 83	0.394 89	0.679 82	0.642 32	0.553 60	0.937 84	0.462 63	0.256 62	0.649 44	0.406 102	0.626 78	0.691 84	0.666 32	0.877 65	0.792 76	0.608 66
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 70	0.717 82	0.701 82	0.692 77	0.576 79	0.801 73	0.467 59	0.716 72	0.563 70	0.459 90	0.953 43	0.429 79	0.169 97	0.581 63	0.854 35	0.605 83	0.710 74	0.550 81	0.894 52	0.793 73	0.575 78

FPConv	0.639 71	0.785 46	0.760 56	0.713 72	0.603 68	0.798 75	0.392 90	0.534 104	0.603 51	0.524 72	0.948 59	0.457 65	0.250 64	0.538 78	0.723 65	0.598 87	0.696 82	0.614 50	0.872 72	0.799 66	0.567 83
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 72	0.797 42	0.769 52	0.641 95	0.590 73	0.820 57	0.461 61	0.537 103	0.637 35	0.536 66	0.947 61	0.388 93	0.206 80	0.656 42	0.668 77	0.647 71	0.732 69	0.585 65	0.868 77	0.793 73	0.473 106

PointSPNet	0.637 73	0.734 71	0.692 89	0.714 71	0.576 79	0.797 76	0.446 68	0.743 65	0.598 54	0.437 95	0.942 77	0.403 89	0.150 104	0.626 52	0.800 53	0.649 68	0.697 81	0.557 77	0.846 86	0.777 85	0.563 84

SConv	0.636 74	0.830 32	0.697 85	0.752 63	0.572 81	0.780 85	0.445 70	0.716 72	0.529 76	0.530 68	0.951 49	0.446 74	0.170 96	0.507 87	0.666 78	0.636 76	0.682 87	0.541 87	0.886 57	0.799 66	0.594 74

Supervoxel-CNN	0.635 75	0.656 92	0.711 77	0.719 69	0.613 66	0.757 94	0.444 73	0.765 57	0.534 75	0.566 57	0.928 96	0.478 57	0.272 52	0.636 47	0.531 91	0.664 62	0.645 97	0.508 95	0.864 79	0.792 76	0.611 63

joint point-based	0.634 76	0.614 100	0.778 46	0.667 86	0.633 63	0.825 50	0.420 81	0.804 47	0.467 95	0.561 58	0.951 49	0.494 49	0.291 39	0.566 68	0.458 97	0.579 94	0.764 50	0.559 76	0.838 87	0.814 59	0.598 72
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 77	0.731 73	0.688 92	0.675 81	0.591 72	0.784 82	0.444 73	0.565 100	0.610 45	0.492 81	0.949 57	0.456 66	0.254 63	0.587 60	0.706 69	0.599 86	0.665 93	0.612 53	0.868 77	0.791 79	0.579 77

PointNet2-SFPN	0.631 78	0.771 55	0.692 89	0.672 82	0.524 90	0.837 37	0.440 75	0.706 77	0.538 74	0.446 92	0.944 73	0.421 85	0.219 75	0.552 74	0.751 59	0.591 90	0.737 65	0.543 86	0.901 47	0.768 89	0.557 87

APCF-Net	0.631 78	0.742 68	0.687 94	0.672 82	0.557 85	0.792 80	0.408 83	0.665 86	0.545 73	0.508 77	0.952 47	0.428 80	0.186 90	0.634 49	0.702 71	0.620 79	0.706 78	0.555 78	0.873 70	0.798 68	0.581 76
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF	0.631 78	0.626 97	0.745 67	0.801 45	0.607 67	0.751 95	0.506 38	0.729 70	0.565 68	0.491 82	0.866 112	0.434 75	0.197 87	0.595 58	0.630 82	0.709 44	0.705 79	0.560 74	0.875 67	0.740 97	0.491 101

FusionAwareConv	0.630 81	0.604 102	0.741 71	0.766 59	0.590 73	0.747 96	0.501 41	0.734 68	0.503 85	0.527 70	0.919 102	0.454 67	0.323 25	0.550 76	0.420 101	0.678 57	0.688 85	0.544 84	0.896 50	0.795 70	0.627 61
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 82	0.800 41	0.625 104	0.719 69	0.545 87	0.806 70	0.445 70	0.597 94	0.448 100	0.519 75	0.938 83	0.481 55	0.328 23	0.489 91	0.499 96	0.657 66	0.759 55	0.592 61	0.881 61	0.797 69	0.634 58

SegGroup_sem	0.627 83	0.818 36	0.747 66	0.701 73	0.602 69	0.764 91	0.385 94	0.629 91	0.490 88	0.508 77	0.931 95	0.409 88	0.201 84	0.564 69	0.725 63	0.618 80	0.692 83	0.539 88	0.873 70	0.794 71	0.548 90
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 84	0.830 32	0.694 87	0.757 61	0.563 83	0.772 89	0.448 67	0.647 89	0.520 79	0.509 76	0.949 57	0.431 78	0.191 88	0.496 89	0.614 84	0.647 71	0.672 91	0.535 90	0.876 66	0.783 82	0.571 79

dtc_net	0.625 84	0.703 85	0.751 63	0.794 48	0.535 88	0.848 26	0.480 52	0.676 84	0.528 77	0.469 87	0.944 73	0.454 67	0.004 117	0.464 94	0.636 81	0.704 47	0.758 56	0.548 83	0.924 30	0.787 80	0.492 100

HPEIN	0.618 86	0.729 75	0.668 95	0.647 92	0.597 71	0.766 90	0.414 82	0.680 81	0.520 79	0.525 71	0.946 64	0.432 76	0.215 77	0.493 90	0.599 85	0.638 75	0.617 102	0.570 68	0.897 49	0.806 63	0.605 69
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 87	0.858 25	0.772 48	0.489 110	0.532 89	0.792 80	0.404 86	0.643 90	0.570 67	0.507 79	0.935 87	0.414 87	0.046 114	0.510 85	0.702 71	0.602 85	0.705 79	0.549 82	0.859 81	0.773 87	0.534 93
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 88	0.760 60	0.667 96	0.649 91	0.521 91	0.793 78	0.457 62	0.648 88	0.528 77	0.434 97	0.947 61	0.401 90	0.153 103	0.454 95	0.721 66	0.648 70	0.717 73	0.536 89	0.904 42	0.765 90	0.485 102
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 89	0.901 14	0.762 54	0.627 97	0.478 97	0.820 57	0.346 100	0.689 80	0.353 110	0.528 69	0.933 92	0.217 115	0.172 94	0.530 80	0.725 63	0.593 89	0.737 65	0.515 92	0.858 82	0.772 88	0.515 96

wsss-transformer	0.600 90	0.634 96	0.743 69	0.697 76	0.601 70	0.781 83	0.437 77	0.585 97	0.493 87	0.446 92	0.933 92	0.394 91	0.011 116	0.654 43	0.661 80	0.603 84	0.733 68	0.526 91	0.832 88	0.761 92	0.480 103

LAP-D	0.594 91	0.720 80	0.692 89	0.637 96	0.456 101	0.773 88	0.391 92	0.730 69	0.587 58	0.445 94	0.940 81	0.381 94	0.288 40	0.434 98	0.453 99	0.591 90	0.649 95	0.581 66	0.777 96	0.749 96	0.610 65

DPC	0.592 92	0.720 80	0.700 83	0.602 101	0.480 96	0.762 93	0.380 95	0.713 75	0.585 61	0.437 95	0.940 81	0.369 96	0.288 40	0.434 98	0.509 95	0.590 92	0.639 100	0.567 72	0.772 97	0.755 94	0.592 75
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 93	0.766 59	0.659 99	0.683 79	0.470 100	0.740 98	0.387 93	0.620 93	0.490 88	0.476 85	0.922 100	0.355 99	0.245 67	0.511 84	0.511 94	0.571 95	0.643 98	0.493 99	0.872 72	0.762 91	0.600 71

ROSMRF	0.580 94	0.772 54	0.707 79	0.681 80	0.563 83	0.764 91	0.362 97	0.515 105	0.465 96	0.465 89	0.936 86	0.427 82	0.207 79	0.438 96	0.577 87	0.536 98	0.675 90	0.486 100	0.723 103	0.779 83	0.524 95

SD-DETR	0.576 95	0.746 65	0.609 108	0.445 114	0.517 92	0.643 109	0.366 96	0.714 74	0.456 98	0.468 88	0.870 111	0.432 76	0.264 59	0.558 72	0.674 75	0.586 93	0.688 85	0.482 101	0.739 101	0.733 99	0.537 92

SQN_0.1%	0.569 96	0.676 89	0.696 86	0.657 88	0.497 93	0.779 86	0.424 79	0.548 101	0.515 81	0.376 102	0.902 109	0.422 84	0.357 9	0.379 103	0.456 98	0.596 88	0.659 94	0.544 84	0.685 106	0.665 110	0.556 88

TextureNet	0.566 97	0.672 91	0.664 97	0.671 84	0.494 94	0.719 99	0.445 70	0.678 83	0.411 106	0.396 100	0.935 87	0.356 98	0.225 72	0.412 100	0.535 90	0.565 96	0.636 101	0.464 103	0.794 95	0.680 107	0.568 82
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 98	0.648 93	0.700 83	0.770 56	0.586 76	0.687 103	0.333 102	0.650 87	0.514 82	0.475 86	0.906 106	0.359 97	0.223 74	0.340 105	0.442 100	0.422 109	0.668 92	0.501 96	0.708 104	0.779 83	0.534 93

Pointnet++ & Feature	0.557 99	0.735 70	0.661 98	0.686 78	0.491 95	0.744 97	0.392 90	0.539 102	0.451 99	0.375 103	0.946 64	0.376 95	0.205 81	0.403 101	0.356 105	0.553 97	0.643 98	0.497 97	0.824 91	0.756 93	0.515 96

GMLPs	0.538 100	0.495 110	0.693 88	0.647 92	0.471 99	0.793 78	0.300 105	0.477 106	0.505 84	0.358 104	0.903 108	0.327 102	0.081 111	0.472 93	0.529 92	0.448 107	0.710 74	0.509 93	0.746 99	0.737 98	0.554 89

PanopticFusion-label	0.529 101	0.491 111	0.688 92	0.604 100	0.386 106	0.632 110	0.225 116	0.705 78	0.434 103	0.293 110	0.815 114	0.348 100	0.241 68	0.499 88	0.669 76	0.507 100	0.649 95	0.442 109	0.796 94	0.602 114	0.561 85
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	0.516 102	0.676 89	0.591 111	0.609 98	0.442 102	0.774 87	0.335 101	0.597 94	0.422 105	0.357 105	0.932 94	0.341 101	0.094 110	0.298 107	0.528 93	0.473 105	0.676 89	0.495 98	0.602 112	0.721 102	0.349 114

Online SegFusion	0.515 103	0.607 101	0.644 102	0.579 103	0.434 103	0.630 111	0.353 98	0.628 92	0.440 101	0.410 98	0.762 117	0.307 104	0.167 98	0.520 82	0.403 103	0.516 99	0.565 105	0.447 107	0.678 107	0.701 104	0.514 98
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	0.501 104	0.558 106	0.608 109	0.424 116	0.478 97	0.690 102	0.246 112	0.586 96	0.468 94	0.450 91	0.911 104	0.394 91	0.160 101	0.438 96	0.212 112	0.432 108	0.541 110	0.475 102	0.742 100	0.727 100	0.477 104

PCNN	0.498 105	0.559 105	0.644 102	0.560 105	0.420 105	0.711 101	0.229 114	0.414 107	0.436 102	0.352 106	0.941 79	0.324 103	0.155 102	0.238 112	0.387 104	0.493 101	0.529 111	0.509 93	0.813 93	0.751 95	0.504 99

3DMV	0.484 106	0.484 112	0.538 114	0.643 94	0.424 104	0.606 114	0.310 103	0.574 98	0.433 104	0.378 101	0.796 115	0.301 105	0.214 78	0.537 79	0.208 113	0.472 106	0.507 114	0.413 112	0.693 105	0.602 114	0.539 91
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 107	0.577 104	0.611 107	0.356 118	0.321 114	0.715 100	0.299 107	0.376 111	0.328 114	0.319 108	0.944 73	0.285 107	0.164 99	0.216 115	0.229 110	0.484 103	0.545 109	0.456 105	0.755 98	0.709 103	0.475 105
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 108	0.679 88	0.604 110	0.578 104	0.380 107	0.682 104	0.291 108	0.106 118	0.483 91	0.258 116	0.920 101	0.258 111	0.025 115	0.231 114	0.325 106	0.480 104	0.560 107	0.463 104	0.725 102	0.666 109	0.231 118
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 109	0.474 113	0.623 105	0.463 112	0.366 109	0.651 107	0.310 103	0.389 110	0.349 112	0.330 107	0.937 84	0.271 109	0.126 107	0.285 108	0.224 111	0.350 114	0.577 104	0.445 108	0.625 110	0.723 101	0.394 110
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	0.442 110	0.548 107	0.548 113	0.597 102	0.363 110	0.628 112	0.300 105	0.292 113	0.374 108	0.307 109	0.881 110	0.268 110	0.186 90	0.238 112	0.204 114	0.407 110	0.506 115	0.449 106	0.667 108	0.620 113	0.462 108

SurfaceConvPF	0.442 110	0.505 109	0.622 106	0.380 117	0.342 112	0.654 106	0.227 115	0.397 109	0.367 109	0.276 112	0.924 98	0.240 112	0.198 86	0.359 104	0.262 108	0.366 111	0.581 103	0.435 110	0.640 109	0.668 108	0.398 109
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 112	0.437 115	0.646 101	0.474 111	0.369 108	0.645 108	0.353 98	0.258 115	0.282 117	0.279 111	0.918 103	0.298 106	0.147 106	0.283 109	0.294 107	0.487 102	0.562 106	0.427 111	0.619 111	0.633 112	0.352 113
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 113	0.525 108	0.647 100	0.522 106	0.324 113	0.488 118	0.077 119	0.712 76	0.353 110	0.401 99	0.636 119	0.281 108	0.176 93	0.340 105	0.565 88	0.175 118	0.551 108	0.398 113	0.370 119	0.602 114	0.361 112

SPLAT Net	0.393 114	0.472 114	0.511 115	0.606 99	0.311 115	0.656 105	0.245 113	0.405 108	0.328 114	0.197 117	0.927 97	0.227 114	0.000 119	0.001 120	0.249 109	0.271 117	0.510 112	0.383 115	0.593 113	0.699 105	0.267 116
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	0.383 115	0.297 117	0.491 116	0.432 115	0.358 111	0.612 113	0.274 110	0.116 117	0.411 106	0.265 113	0.904 107	0.229 113	0.079 112	0.250 110	0.185 115	0.320 115	0.510 112	0.385 114	0.548 114	0.597 117	0.394 110

PointNet++	0.339 116	0.584 103	0.478 117	0.458 113	0.256 117	0.360 119	0.250 111	0.247 116	0.278 118	0.261 115	0.677 118	0.183 116	0.117 108	0.212 116	0.145 117	0.364 112	0.346 119	0.232 119	0.548 114	0.523 118	0.252 117
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
GrowSP++	0.323 117	0.114 119	0.589 112	0.499 108	0.147 119	0.555 115	0.290 109	0.336 112	0.290 116	0.262 114	0.865 113	0.102 119	0.000 119	0.037 118	0.000 120	0.000 120	0.462 116	0.381 116	0.389 118	0.664 111	0.473 106

SSC-UNet	0.308 118	0.353 116	0.290 119	0.278 119	0.166 118	0.553 116	0.169 118	0.286 114	0.147 119	0.148 119	0.908 105	0.182 117	0.064 113	0.023 119	0.018 119	0.354 113	0.363 117	0.345 117	0.546 116	0.685 106	0.278 115

ScanNet	0.306 119	0.203 118	0.366 118	0.501 107	0.311 115	0.524 117	0.211 117	0.002 120	0.342 113	0.189 118	0.786 116	0.145 118	0.102 109	0.245 111	0.152 116	0.318 116	0.348 118	0.300 118	0.460 117	0.437 119	0.182 119
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 120	0.000 120	0.041 120	0.172 120	0.030 120	0.062 120	0.001 120	0.035 119	0.004 120	0.051 120	0.143 120	0.019 120	0.003 118	0.041 117	0.050 118	0.003 119	0.054 120	0.018 120	0.005 120	0.264 120	0.082 120

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

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

PointRel	0.622 1	0.926 8	0.710 3	0.541 10	0.502 2	0.772 6	0.314 4	0.598 11	0.425 8	0.504 9	0.565 1	0.650 6	0.716 2	0.809 7	0.476 11	0.747 4	0.618 1	0.963 3	0.364 19
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
Competitor-MAFT	0.618 2	0.866 15	0.724 1	0.628 1	0.484 3	0.803 1	0.300 7	0.509 32	0.496 1	0.539 1	0.547 5	0.703 1	0.668 8	0.708 30	0.463 16	0.708 16	0.595 3	0.959 5	0.418 7

SIM3D	0.617 3	0.952 4	0.629 16	0.539 11	0.426 15	0.768 10	0.302 6	0.681 2	0.425 9	0.473 15	0.511 15	0.701 2	0.717 1	0.821 6	0.467 14	0.774 1	0.559 14	0.914 17	0.448 2

Spherical Mask(CtoF)	0.616 4	0.946 5	0.654 12	0.555 6	0.434 12	0.769 9	0.271 11	0.604 8	0.447 5	0.505 7	0.549 2	0.698 3	0.716 2	0.775 15	0.480 8	0.747 5	0.575 10	0.925 13	0.436 4

EV3D	0.615 5	0.946 5	0.652 13	0.555 6	0.433 13	0.773 5	0.271 12	0.604 8	0.447 5	0.506 6	0.544 6	0.698 3	0.716 2	0.775 15	0.480 8	0.747 5	0.572 12	0.925 13	0.435 5

ExtMask3D	0.598 6	0.852 16	0.692 7	0.433 30	0.461 7	0.791 3	0.264 13	0.488 35	0.493 2	0.508 5	0.528 14	0.594 12	0.706 6	0.791 9	0.483 6	0.734 9	0.595 4	0.911 19	0.437 3

MAFT	0.596 7	0.889 13	0.721 2	0.448 23	0.460 8	0.768 11	0.251 15	0.558 21	0.408 10	0.504 8	0.539 8	0.616 10	0.618 11	0.858 3	0.482 7	0.684 19	0.551 17	0.931 12	0.450 1

UniPerception	0.588 8	0.963 3	0.667 10	0.493 15	0.472 6	0.750 14	0.229 18	0.528 27	0.468 4	0.498 12	0.542 7	0.643 7	0.530 20	0.661 37	0.463 15	0.695 18	0.599 2	0.972 1	0.420 6

MG-Former	0.587 9	0.852 16	0.639 15	0.454 22	0.393 20	0.758 13	0.338 2	0.572 16	0.480 3	0.527 3	0.491 21	0.671 5	0.527 21	0.867 1	0.485 5	0.601 30	0.590 7	0.938 11	0.390 11

InsSSM	0.586 10	1.000 1	0.593 20	0.440 26	0.480 4	0.771 7	0.345 1	0.437 39	0.444 7	0.495 13	0.548 4	0.579 15	0.621 10	0.720 27	0.409 22	0.712 11	0.593 5	0.960 4	0.395 9
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
Queryformer	0.583 11	0.926 8	0.702 5	0.393 36	0.504 1	0.733 20	0.276 10	0.527 28	0.373 16	0.479 14	0.534 10	0.533 22	0.697 7	0.720 28	0.436 20	0.745 7	0.592 6	0.958 6	0.363 20

KmaxOneFormerNet	0.581 12	0.745 27	0.692 8	0.551 8	0.458 9	0.798 2	0.264 14	0.531 26	0.369 18	0.513 4	0.531 13	0.632 8	0.494 24	0.798 8	0.567 2	0.648 23	0.558 16	0.950 8	0.362 21

Competitor-SPFormer	0.580 13	0.721 33	0.705 4	0.593 4	0.444 11	0.786 4	0.286 8	0.564 19	0.376 15	0.498 11	0.534 11	0.546 20	0.390 43	0.785 11	0.577 1	0.708 15	0.579 9	0.954 7	0.388 12

PBNet	0.573 14	0.926 8	0.575 25	0.619 2	0.472 5	0.736 18	0.239 17	0.487 36	0.383 14	0.459 18	0.506 18	0.533 21	0.585 13	0.767 17	0.404 23	0.717 10	0.559 15	0.969 2	0.381 15
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
TST3D	0.569 15	0.778 24	0.675 9	0.598 3	0.451 10	0.727 21	0.280 9	0.476 38	0.395 11	0.472 16	0.457 27	0.583 13	0.580 15	0.777 12	0.462 18	0.735 8	0.547 19	0.919 16	0.333 27
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
Mask3D	0.566 16	0.926 8	0.597 19	0.408 33	0.420 17	0.737 17	0.239 16	0.598 11	0.386 13	0.458 19	0.549 2	0.568 18	0.716 2	0.601 43	0.480 8	0.646 24	0.575 10	0.922 15	0.364 18
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
OneFormer3D	0.566 16	0.781 23	0.697 6	0.562 5	0.431 14	0.770 8	0.331 3	0.400 45	0.373 17	0.529 2	0.504 19	0.568 17	0.475 27	0.732 25	0.470 12	0.762 2	0.550 18	0.871 34	0.379 16
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
ISBNet	0.559 18	0.939 7	0.655 11	0.383 39	0.426 16	0.763 12	0.180 20	0.534 25	0.386 12	0.499 10	0.509 17	0.621 9	0.427 37	0.704 32	0.467 13	0.649 22	0.571 13	0.948 9	0.401 8
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
GraphCut	0.552 19	1.000 1	0.611 18	0.438 27	0.392 21	0.714 22	0.139 23	0.598 13	0.327 21	0.389 22	0.510 16	0.598 11	0.427 38	0.754 20	0.463 17	0.761 3	0.588 8	0.903 22	0.329 28

SPFormer	0.549 20	0.745 27	0.640 14	0.484 16	0.395 19	0.739 16	0.311 5	0.566 18	0.335 20	0.468 17	0.492 20	0.555 19	0.478 26	0.747 22	0.436 19	0.712 12	0.540 20	0.893 26	0.343 26
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DKNet	0.532 21	0.815 20	0.624 17	0.517 12	0.377 23	0.749 15	0.107 25	0.509 31	0.304 23	0.437 20	0.475 22	0.581 14	0.539 18	0.775 14	0.339 28	0.640 26	0.506 23	0.901 23	0.385 14
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst	0.520 22	0.889 13	0.551 29	0.548 9	0.418 18	0.665 32	0.064 34	0.585 14	0.260 31	0.277 36	0.471 24	0.500 23	0.644 9	0.785 10	0.369 24	0.591 33	0.511 21	0.878 31	0.362 22

SoftGroup++	0.513 23	0.704 35	0.578 24	0.398 35	0.363 29	0.704 23	0.061 35	0.647 5	0.297 28	0.378 25	0.537 9	0.343 26	0.614 12	0.828 5	0.295 33	0.710 14	0.505 25	0.875 33	0.394 10

SSTNet	0.506 24	0.738 31	0.549 30	0.497 14	0.316 34	0.693 26	0.178 21	0.377 48	0.198 37	0.330 27	0.463 26	0.576 16	0.515 22	0.857 4	0.494 3	0.637 27	0.457 29	0.943 10	0.290 37
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SoftGroup	0.504 25	0.667 42	0.579 22	0.372 41	0.381 22	0.694 25	0.072 31	0.677 3	0.303 24	0.387 23	0.531 12	0.319 30	0.582 14	0.754 19	0.318 29	0.643 25	0.492 26	0.907 21	0.388 13
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
DANCENET	0.504 25	0.926 8	0.579 21	0.472 18	0.367 26	0.626 42	0.165 22	0.432 40	0.221 33	0.408 21	0.449 29	0.411 24	0.564 16	0.746 23	0.421 21	0.707 17	0.438 32	0.846 42	0.288 38

TD3D	0.489 27	0.852 16	0.511 39	0.434 28	0.322 33	0.735 19	0.101 28	0.512 30	0.355 19	0.349 26	0.468 25	0.283 34	0.514 23	0.676 36	0.268 38	0.671 20	0.510 22	0.908 20	0.329 29
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
OccuSeg+instance	0.486 28	0.802 22	0.536 32	0.428 31	0.369 25	0.702 24	0.205 19	0.331 53	0.301 25	0.379 24	0.474 23	0.327 27	0.437 32	0.862 2	0.485 4	0.601 31	0.394 40	0.846 44	0.273 41
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg	0.479 29	0.704 35	0.564 26	0.467 20	0.366 27	0.633 40	0.068 32	0.554 22	0.262 30	0.328 28	0.447 30	0.323 28	0.534 19	0.722 26	0.288 35	0.614 28	0.482 27	0.912 18	0.358 24

DualGroup	0.469 30	0.815 20	0.552 28	0.398 34	0.374 24	0.683 28	0.130 24	0.539 24	0.310 22	0.327 29	0.407 33	0.276 35	0.447 31	0.535 47	0.342 27	0.659 21	0.455 30	0.900 25	0.301 33

SSEC	0.465 31	0.667 42	0.578 23	0.502 13	0.362 30	0.641 39	0.035 44	0.605 7	0.291 29	0.323 30	0.451 28	0.296 32	0.417 41	0.677 35	0.245 42	0.501 51	0.506 24	0.900 24	0.366 17

HAIS	0.457 32	0.704 35	0.561 27	0.457 21	0.364 28	0.673 29	0.046 43	0.547 23	0.194 38	0.308 31	0.426 31	0.288 33	0.454 30	0.711 29	0.262 39	0.563 41	0.434 34	0.889 28	0.344 25
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group	0.436 33	0.630 50	0.508 42	0.480 17	0.310 36	0.624 44	0.065 33	0.638 6	0.174 39	0.256 40	0.384 37	0.194 47	0.428 35	0.759 18	0.289 34	0.574 38	0.400 38	0.849 41	0.291 36
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
INS-Conv-instance	0.435 34	0.716 34	0.495 44	0.355 43	0.331 31	0.689 27	0.102 27	0.394 47	0.208 36	0.280 34	0.395 35	0.250 38	0.544 17	0.741 24	0.309 31	0.536 47	0.391 41	0.842 47	0.258 45

Mask-Group	0.434 35	0.778 24	0.516 37	0.471 19	0.330 32	0.658 33	0.029 46	0.526 29	0.249 32	0.256 39	0.400 34	0.309 31	0.384 46	0.296 63	0.368 25	0.575 37	0.425 35	0.877 32	0.362 23
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Box2Mask	0.433 36	0.741 29	0.463 49	0.433 29	0.283 39	0.625 43	0.103 26	0.298 58	0.125 48	0.260 38	0.424 32	0.322 29	0.472 28	0.701 33	0.363 26	0.711 13	0.309 57	0.882 29	0.272 43
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
RPGN	0.428 37	0.630 50	0.508 41	0.367 42	0.249 46	0.658 34	0.016 54	0.673 4	0.131 46	0.234 43	0.383 38	0.270 36	0.434 33	0.748 21	0.274 37	0.609 29	0.406 37	0.842 46	0.267 44
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet	0.413 38	0.741 29	0.520 34	0.237 54	0.284 38	0.523 53	0.097 29	0.691 1	0.138 43	0.209 53	0.229 55	0.238 41	0.390 44	0.707 31	0.310 30	0.448 58	0.470 28	0.892 27	0.310 31

PointGroup	0.407 39	0.639 49	0.496 43	0.415 32	0.243 48	0.645 38	0.021 51	0.570 17	0.114 49	0.211 51	0.359 40	0.217 45	0.428 36	0.660 38	0.256 40	0.562 42	0.341 49	0.860 37	0.291 35
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]
CSC-Pretrained	0.405 40	0.738 31	0.465 48	0.331 47	0.205 52	0.655 35	0.051 39	0.601 10	0.092 53	0.211 52	0.329 43	0.198 46	0.459 29	0.775 13	0.195 49	0.524 49	0.400 39	0.878 30	0.184 54

PE	0.396 41	0.667 42	0.467 47	0.446 25	0.243 47	0.624 45	0.022 50	0.577 15	0.106 50	0.219 46	0.340 41	0.239 40	0.487 25	0.475 54	0.225 44	0.541 46	0.350 47	0.818 49	0.273 42
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3D	0.395 42	0.642 48	0.518 36	0.447 24	0.259 45	0.666 31	0.050 40	0.251 63	0.166 40	0.231 44	0.362 39	0.232 42	0.331 49	0.535 46	0.229 43	0.587 34	0.438 33	0.850 39	0.317 30
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS	0.392 43	0.778 24	0.530 33	0.220 56	0.278 40	0.567 50	0.083 30	0.330 54	0.299 26	0.270 37	0.310 46	0.143 53	0.260 53	0.624 41	0.277 36	0.568 40	0.361 45	0.865 36	0.301 32

AOIA	0.387 44	0.704 35	0.515 38	0.385 38	0.225 51	0.669 30	0.005 61	0.482 37	0.126 47	0.181 56	0.269 52	0.221 44	0.426 39	0.478 53	0.218 45	0.592 32	0.371 43	0.851 38	0.242 47

SSEN	0.384 45	0.852 16	0.494 45	0.192 57	0.226 50	0.648 37	0.022 49	0.398 46	0.299 27	0.277 35	0.317 45	0.231 43	0.194 60	0.514 50	0.196 47	0.586 35	0.444 31	0.843 45	0.184 53
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Mask3D_evaluation	0.382 46	0.593 52	0.520 35	0.390 37	0.314 35	0.600 46	0.018 53	0.287 61	0.151 42	0.281 33	0.387 36	0.169 51	0.429 34	0.654 39	0.172 53	0.578 36	0.384 42	0.670 60	0.278 40

PCJC	0.375 47	0.704 35	0.542 31	0.284 51	0.197 54	0.649 36	0.006 58	0.426 41	0.138 44	0.242 41	0.304 47	0.183 50	0.388 45	0.629 40	0.141 60	0.546 45	0.344 48	0.738 55	0.283 39

ClickSeg_Instance	0.366 48	0.654 46	0.375 53	0.184 58	0.302 37	0.592 48	0.050 41	0.300 57	0.093 52	0.283 32	0.277 49	0.249 39	0.426 40	0.615 42	0.299 32	0.504 50	0.367 44	0.832 48	0.191 52

SphereSeg	0.357 49	0.651 47	0.411 51	0.345 44	0.264 44	0.630 41	0.059 36	0.289 60	0.212 34	0.240 42	0.336 42	0.158 52	0.305 50	0.557 44	0.159 56	0.455 57	0.341 50	0.726 57	0.294 34

3D-MPA	0.355 50	0.457 62	0.484 46	0.299 49	0.277 41	0.591 49	0.047 42	0.332 51	0.212 35	0.217 47	0.278 48	0.193 48	0.413 42	0.410 57	0.195 48	0.574 39	0.352 46	0.849 40	0.213 50
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF	0.353 51	0.593 52	0.511 40	0.375 40	0.264 43	0.597 47	0.008 56	0.332 52	0.160 41	0.229 45	0.274 51	0.000 74	0.206 57	0.678 34	0.155 57	0.485 53	0.422 36	0.816 50	0.254 46
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
RWSeg	0.348 52	0.475 59	0.456 50	0.320 48	0.275 42	0.476 55	0.020 52	0.491 34	0.056 60	0.212 50	0.320 44	0.261 37	0.302 51	0.520 48	0.182 51	0.557 43	0.285 59	0.867 35	0.197 51

GICN	0.341 53	0.580 54	0.371 54	0.344 45	0.198 53	0.469 56	0.052 38	0.564 20	0.093 51	0.212 49	0.212 57	0.127 55	0.347 48	0.537 45	0.206 46	0.525 48	0.329 52	0.729 56	0.241 48

One_Thing_One_Click	0.326 54	0.472 60	0.361 55	0.232 55	0.183 55	0.555 51	0.000 67	0.498 33	0.038 62	0.195 54	0.226 56	0.362 25	0.168 61	0.469 55	0.251 41	0.553 44	0.335 51	0.846 43	0.117 62
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Occipital-SCS	0.320 55	0.679 41	0.352 56	0.334 46	0.229 49	0.436 57	0.025 47	0.412 44	0.058 58	0.161 61	0.240 54	0.085 57	0.262 52	0.496 52	0.187 50	0.467 55	0.328 53	0.775 51	0.231 49

Sparse R-CNN	0.292 56	0.704 35	0.213 66	0.153 60	0.154 57	0.551 52	0.053 37	0.212 64	0.132 45	0.174 58	0.274 50	0.070 59	0.363 47	0.441 56	0.176 52	0.424 60	0.234 61	0.758 53	0.161 58

MTML	0.282 57	0.577 55	0.380 52	0.182 59	0.107 63	0.430 58	0.001 64	0.422 42	0.057 59	0.179 57	0.162 60	0.070 60	0.229 55	0.511 51	0.161 54	0.491 52	0.313 54	0.650 63	0.162 56
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet	0.262 58	0.667 42	0.335 57	0.067 67	0.123 61	0.427 59	0.022 48	0.280 62	0.058 57	0.216 48	0.211 58	0.039 63	0.142 63	0.519 49	0.106 64	0.338 64	0.310 56	0.721 58	0.138 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	0.254 59	0.463 61	0.249 65	0.113 61	0.167 56	0.412 61	0.000 66	0.374 49	0.073 54	0.173 59	0.243 53	0.130 54	0.228 56	0.368 59	0.160 55	0.356 62	0.208 62	0.711 59	0.136 60
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet	0.253 60	0.519 57	0.324 60	0.251 53	0.137 60	0.345 66	0.031 45	0.419 43	0.069 55	0.162 60	0.131 62	0.052 61	0.202 59	0.338 61	0.147 59	0.301 67	0.303 58	0.651 62	0.178 55
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
SPG_WSIS	0.251 61	0.380 64	0.274 63	0.289 50	0.144 58	0.413 60	0.000 67	0.311 55	0.065 56	0.113 63	0.130 63	0.029 66	0.204 58	0.388 58	0.108 63	0.459 56	0.311 55	0.769 52	0.127 61

SegGroup_ins	0.246 62	0.556 56	0.335 58	0.062 69	0.115 62	0.490 54	0.000 67	0.297 59	0.018 66	0.186 55	0.142 61	0.083 58	0.233 54	0.216 65	0.153 58	0.469 54	0.251 60	0.744 54	0.083 65
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PanopticFusion-inst	0.214 63	0.250 69	0.330 59	0.275 52	0.103 64	0.228 72	0.000 67	0.345 50	0.024 64	0.088 65	0.203 59	0.186 49	0.167 62	0.367 60	0.125 61	0.221 70	0.112 72	0.666 61	0.162 57
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
UNet-backbone	0.161 64	0.519 57	0.259 64	0.084 63	0.059 66	0.325 68	0.002 62	0.093 69	0.009 68	0.077 67	0.064 66	0.045 62	0.044 70	0.161 67	0.045 66	0.331 65	0.180 64	0.566 64	0.033 74

3D-SIS	0.161 64	0.407 63	0.155 71	0.068 66	0.043 70	0.346 65	0.001 63	0.134 66	0.005 69	0.088 64	0.106 65	0.037 64	0.135 65	0.321 62	0.028 70	0.339 63	0.116 71	0.466 67	0.093 64
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet	0.158 66	0.356 65	0.173 69	0.113 62	0.140 59	0.359 62	0.012 55	0.023 72	0.039 61	0.134 62	0.123 64	0.008 70	0.089 66	0.149 68	0.117 62	0.221 69	0.128 69	0.563 65	0.094 63

Region-18class	0.146 67	0.175 73	0.321 61	0.080 64	0.062 65	0.357 63	0.000 67	0.307 56	0.002 71	0.066 68	0.044 68	0.000 74	0.018 72	0.036 73	0.054 65	0.447 59	0.133 67	0.472 66	0.060 69

SemRegionNet-20cls	0.121 68	0.296 67	0.203 67	0.071 65	0.058 67	0.349 64	0.000 67	0.150 65	0.019 65	0.054 70	0.034 71	0.017 69	0.052 68	0.042 72	0.013 73	0.209 71	0.183 63	0.371 68	0.057 70

3D-BEVIS	0.117 69	0.250 69	0.308 62	0.020 73	0.009 75	0.269 71	0.006 59	0.008 73	0.029 63	0.037 73	0.014 74	0.003 72	0.036 71	0.147 69	0.042 68	0.381 61	0.118 70	0.362 69	0.069 68
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Hier3D	0.117 69	0.222 71	0.161 70	0.054 71	0.027 72	0.289 69	0.000 67	0.124 67	0.001 73	0.079 66	0.061 67	0.027 67	0.141 64	0.240 64	0.005 74	0.310 66	0.129 68	0.153 74	0.081 66
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp	0.113 71	0.333 66	0.151 72	0.056 70	0.053 68	0.344 67	0.000 67	0.105 68	0.016 67	0.049 71	0.035 70	0.020 68	0.053 67	0.048 71	0.013 72	0.183 73	0.173 65	0.344 71	0.054 71

Sem_Recon_ins	0.098 72	0.295 68	0.187 68	0.015 74	0.036 71	0.213 73	0.005 60	0.038 71	0.003 70	0.056 69	0.037 69	0.036 65	0.015 73	0.051 70	0.044 67	0.209 72	0.098 73	0.354 70	0.071 67

ASIS	0.085 73	0.037 74	0.080 74	0.066 68	0.047 69	0.282 70	0.000 67	0.052 70	0.002 72	0.047 72	0.026 72	0.001 73	0.046 69	0.194 66	0.031 69	0.264 68	0.140 66	0.167 73	0.047 73

Sgpn_scannet	0.049 74	0.023 75	0.134 73	0.031 72	0.013 74	0.144 74	0.006 57	0.008 74	0.000 74	0.028 74	0.017 73	0.003 71	0.009 75	0.000 74	0.021 71	0.122 74	0.095 74	0.175 72	0.054 72

MaskRCNN 2d->3d Proj	0.022 75	0.185 72	0.000 75	0.000 75	0.015 73	0.000 75	0.000 65	0.006 75	0.000 74	0.010 75	0.006 75	0.107 56	0.012 74	0.000 74	0.002 75	0.027 75	0.004 75	0.022 75	0.001 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

LAST-PCL-type	0.780 1	0.250 3	1.000 1	1.000 1	1.000 1	1.000 1	1.000 1	0.500 2	1.000 1	0.500 2	0.889 1	0.000 2	1.000 1	1.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, and Jian Zhang: Language-Assisted 3D Scene Understanding. arxiv23.12
multi-task	0.700 2	0.500 1	1.000 1	0.882 3	0.500 3	1.000 1	1.000 1	0.500 2	1.000 1	1.000 1	0.778 2	0.000 2	0.938 2	0.000 3
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE	0.691 3	0.500 1	0.938 3	0.824 4	1.000 1	1.000 1	0.500 3	1.000 1	0.857 3	0.500 2	0.556 4	0.000 2	0.812 3	0.500 2

SE-ResNeXt-SSMA	0.498 4	0.000 5	0.812 4	0.941 2	0.500 3	0.500 4	0.500 3	0.500 2	0.429 5	0.500 2	0.667 3	0.500 1	0.625 4	0.000 3
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet	0.353 5	0.250 3	0.812 4	0.529 5	0.500 3	0.500 4	0.000 5	0.500 2	0.571 4	0.000 5	0.556 4	0.000 2	0.375 5	0.000 3