Benchmark Results - ScanNet Benchmark

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

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

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

PTv3 ScanNet200	0.393 1	0.592 1	0.330 1	0.216 1	0.520 1	0.109 2	0.108 11	0.000 1	0.337 1	0.000 1	0.310 9	0.394 6	0.494 9	0.753 8	0.848 1	0.256 2	0.717 3	0.000 3	0.842 1	0.192 2	0.065 2	0.449 6	0.346 2	0.546 4	0.190 8	0.000 5	0.384 4	0.000 1	0.000 3	0.218 1	0.505 1	0.791 1	0.000 1	0.136 2	0.000 2	0.903 1	0.073 10	0.687 4	0.000 5	0.168 1	0.551 3	0.387 6	0.941 1	0.000 1	0.000 2	0.397 8	0.654 3	0.000 8	0.714 3	0.759 10	0.752 5	0.118 4	0.264 2	0.926 1	0.000 1	0.048 3	0.575 2	0.000 7	0.597 1	0.366 1	0.755 1	0.469 1	0.474 1	0.798 1	0.140 6	0.617 1	0.692 4	0.000 4	0.592 2	0.971 1	0.188 3	0.000 1	0.133 5	0.593 1	0.349 1	0.650 2	0.717 5	0.699 1	0.455 1	0.790 1	0.523 3	0.636 1	0.301 1	0.000 1	0.622 2	0.000 7	0.017 10	0.259 2	0.000 3	0.921 2	0.337 1	0.733 1	0.210 1	0.514 1	0.860 6	0.407 1	0.000 1	0.688 1	0.109 6	0.000 10	0.000 4	0.000 1	0.151 2	0.671 4	0.782 1	0.115 8	0.641 1	0.903 1	0.349 1	0.616 2	0.088 4	0.832 3	0.000 3	0.480 2	0.000 1	0.428 1	0.000 2	0.497 7	0.000 1	0.000 5	0.000 1	0.662 2	0.690 1	0.612 1	0.828 1	0.575 1	0.000 1	0.404 4	0.644 1	0.325 4	0.887 2	0.728 1	0.009 11	0.134 5	0.026 12	0.000 1	0.761 1	0.731 1	0.172 4	0.077 2	0.528 3	0.727 3	0.000 1	0.603 4	0.220 2	0.022 2	0.000 1	0.740 1	0.000 2	0.000 1	0.661 2	0.586 1	0.566 1	0.436 4	0.531 2	0.978 1	0.457 1	0.708 2	0.583 3	0.141 7	0.748 1	0.000 1	0.026 1	0.822 1	0.871 3	0.879 5	0.000 1	0.851 1	0.405 2	0.914 1	0.000 1	0.682 2	0.000 10	0.281 2	0.738 1	0.463 5
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PPT-SpUNet-F.T.	0.332 7	0.556 3	0.270 4	0.123 9	0.519 2	0.091 4	0.349 2	0.000 1	0.000 3	0.000 1	0.339 6	0.383 7	0.498 8	0.833 4	0.807 3	0.241 3	0.584 4	0.000 3	0.755 4	0.124 5	0.000 6	0.608 2	0.330 5	0.530 7	0.314 1	0.000 5	0.374 5	0.000 1	0.000 3	0.197 2	0.459 4	0.000 6	0.000 1	0.117 3	0.000 2	0.876 5	0.095 2	0.682 5	0.000 5	0.086 6	0.518 5	0.433 2	0.930 2	0.000 1	0.000 2	0.563 3	0.542 9	0.077 5	0.715 2	0.858 6	0.756 3	0.008 12	0.171 7	0.874 5	0.000 1	0.039 4	0.550 6	0.000 7	0.545 4	0.256 6	0.657 6	0.453 3	0.351 5	0.449 8	0.213 3	0.392 7	0.611 8	0.000 4	0.037 10	0.946 4	0.138 9	0.000 1	0.000 8	0.063 6	0.308 2	0.537 5	0.796 3	0.673 2	0.323 8	0.392 7	0.400 9	0.509 6	0.000 3	0.000 1	0.649 1	0.000 7	0.023 7	0.000 7	0.000 3	0.914 5	0.002 11	0.506 11	0.163 6	0.359 6	0.872 4	0.000 7	0.000 1	0.623 4	0.112 4	0.001 9	0.000 4	0.000 1	0.021 4	0.753 1	0.565 11	0.150 2	0.579 2	0.806 8	0.267 4	0.616 2	0.042 11	0.783 8	0.000 3	0.374 8	0.000 1	0.000 4	0.000 2	0.620 5	0.000 1	0.000 5	0.000 1	0.572 10	0.634 3	0.350 7	0.792 3	0.000 7	0.000 1	0.376 6	0.535 4	0.378 3	0.855 4	0.672 2	0.074 8	0.000 7	0.185 5	0.000 1	0.727 7	0.660 7	0.076 12	0.000 7	0.432 7	0.646 6	0.000 1	0.594 6	0.006 10	0.000 5	0.000 1	0.658 4	0.000 2	0.000 1	0.661 2	0.549 5	0.300 9	0.291 8	0.045 9	0.942 7	0.304 4	0.600 6	0.572 4	0.135 11	0.695 3	0.000 1	0.008 5	0.793 4	0.942 1	0.899 2	0.000 1	0.816 4	0.181 7	0.897 2	0.000 1	0.679 3	0.223 3	0.264 3	0.691 3	0.345 10
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 6	0.558 2	0.269 6	0.124 8	0.448 10	0.080 6	0.272 3	0.000 1	0.000 3	0.000 1	0.342 5	0.515 2	0.524 5	0.713 12	0.789 5	0.158 8	0.384 7	0.000 3	0.806 3	0.125 4	0.000 6	0.496 4	0.332 4	0.498 11	0.227 6	0.024 2	0.474 1	0.000 1	0.003 2	0.071 6	0.487 2	0.000 6	0.000 1	0.110 5	0.000 2	0.876 5	0.013 12	0.703 1	0.000 5	0.076 7	0.473 8	0.355 7	0.906 4	0.000 1	0.000 2	0.476 5	0.706 1	0.000 8	0.672 8	0.835 8	0.748 6	0.015 11	0.223 4	0.860 6	0.000 1	0.000 8	0.572 4	0.000 7	0.509 6	0.313 5	0.662 2	0.398 9	0.396 3	0.411 10	0.276 1	0.527 2	0.711 3	0.000 4	0.076 8	0.946 4	0.166 5	0.000 1	0.022 6	0.160 4	0.183 8	0.493 8	0.699 6	0.637 3	0.403 3	0.330 9	0.406 8	0.526 5	0.024 2	0.000 1	0.392 8	0.000 7	0.016 11	0.000 7	0.196 2	0.915 4	0.112 7	0.557 6	0.197 2	0.352 7	0.877 2	0.000 7	0.000 1	0.592 9	0.103 8	0.000 10	0.067 1	0.000 1	0.089 3	0.735 3	0.625 7	0.130 7	0.568 4	0.836 6	0.271 3	0.534 6	0.043 10	0.799 6	0.001 2	0.445 3	0.000 1	0.000 4	0.024 1	0.661 2	0.000 1	0.262 2	0.000 1	0.591 5	0.517 10	0.373 6	0.788 5	0.021 6	0.000 1	0.455 1	0.517 6	0.320 5	0.823 7	0.200 12	0.001 12	0.150 4	0.100 7	0.000 1	0.736 6	0.668 5	0.103 10	0.052 4	0.662 1	0.720 4	0.000 1	0.602 5	0.112 4	0.002 4	0.000 1	0.637 6	0.000 2	0.000 1	0.621 7	0.569 2	0.398 6	0.412 5	0.234 7	0.949 4	0.363 2	0.492 10	0.495 6	0.251 4	0.665 6	0.000 1	0.001 7	0.805 3	0.833 5	0.794 7	0.000 1	0.821 3	0.314 5	0.843 8	0.000 1	0.560 6	0.245 2	0.262 4	0.713 2	0.370 9

CeCo	0.340 4	0.551 6	0.247 8	0.181 3	0.475 8	0.057 12	0.142 9	0.000 1	0.000 3	0.000 1	0.387 3	0.463 3	0.499 7	0.924 2	0.774 7	0.213 4	0.257 8	0.000 3	0.546 10	0.100 8	0.006 5	0.615 1	0.177 12	0.534 5	0.246 4	0.000 5	0.400 2	0.000 1	0.338 1	0.006 11	0.484 3	0.609 2	0.000 1	0.083 8	0.000 2	0.873 7	0.089 5	0.661 9	0.000 5	0.048 11	0.560 2	0.408 5	0.892 5	0.000 1	0.000 2	0.586 1	0.616 5	0.000 8	0.692 6	0.900 3	0.721 7	0.162 1	0.228 3	0.860 6	0.000 1	0.000 8	0.575 2	0.083 3	0.550 3	0.347 2	0.624 8	0.410 8	0.360 4	0.740 2	0.109 8	0.321 10	0.660 5	0.000 4	0.121 4	0.939 8	0.143 7	0.000 1	0.400 1	0.003 8	0.190 7	0.564 3	0.652 7	0.615 6	0.421 2	0.304 10	0.579 1	0.547 4	0.000 3	0.000 1	0.296 9	0.000 7	0.030 6	0.096 4	0.000 3	0.916 3	0.037 8	0.551 7	0.171 4	0.376 5	0.865 5	0.286 3	0.000 1	0.633 2	0.102 9	0.027 6	0.011 3	0.000 1	0.000 6	0.474 9	0.742 2	0.133 5	0.311 8	0.824 7	0.242 8	0.503 9	0.068 6	0.828 4	0.000 3	0.429 4	0.000 1	0.063 3	0.000 2	0.781 1	0.000 1	0.000 5	0.000 1	0.665 1	0.633 4	0.450 4	0.818 2	0.000 7	0.000 1	0.429 2	0.532 5	0.226 8	0.825 6	0.510 8	0.377 3	0.709 1	0.079 9	0.000 1	0.753 3	0.683 3	0.102 11	0.063 3	0.401 11	0.620 9	0.000 1	0.619 2	0.000 11	0.000 5	0.000 1	0.595 10	0.000 2	0.000 1	0.345 9	0.564 3	0.411 5	0.603 1	0.384 4	0.945 5	0.266 6	0.643 4	0.367 9	0.304 1	0.663 7	0.000 1	0.010 3	0.726 10	0.767 6	0.898 3	0.000 1	0.784 8	0.435 1	0.861 5	0.000 1	0.447 7	0.000 10	0.257 5	0.656 8	0.377 8
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
PonderV2 ScanNet200	0.346 3	0.552 5	0.270 5	0.175 4	0.497 5	0.070 9	0.239 5	0.000 1	0.000 3	0.000 1	0.232 12	0.412 5	0.584 2	0.842 3	0.804 4	0.212 5	0.540 5	0.000 3	0.433 11	0.106 7	0.000 6	0.590 3	0.290 7	0.548 3	0.243 5	0.000 5	0.356 8	0.000 1	0.000 3	0.062 7	0.398 8	0.441 5	0.000 1	0.104 7	0.000 2	0.888 3	0.076 9	0.682 5	0.030 1	0.094 5	0.491 7	0.351 8	0.869 7	0.000 1	0.063 1	0.403 7	0.700 2	0.000 8	0.660 9	0.881 4	0.761 1	0.050 7	0.186 5	0.852 8	0.000 1	0.007 6	0.570 5	0.100 2	0.565 2	0.326 4	0.641 7	0.431 4	0.290 9	0.621 4	0.259 2	0.408 6	0.622 7	0.125 1	0.082 7	0.950 3	0.179 4	0.000 1	0.263 3	0.424 3	0.193 6	0.558 4	0.880 2	0.545 8	0.375 4	0.727 2	0.445 7	0.499 7	0.000 3	0.000 1	0.475 5	0.002 5	0.034 5	0.083 5	0.000 3	0.924 1	0.290 3	0.636 4	0.115 9	0.400 3	0.874 3	0.186 5	0.000 1	0.611 5	0.128 2	0.113 2	0.000 4	0.000 1	0.000 6	0.584 7	0.636 6	0.103 9	0.385 6	0.843 5	0.283 2	0.603 4	0.080 5	0.825 5	0.000 3	0.377 7	0.000 1	0.000 4	0.000 2	0.457 8	0.000 1	0.000 5	0.000 1	0.574 9	0.608 6	0.481 2	0.792 3	0.394 3	0.000 1	0.357 7	0.503 8	0.261 7	0.817 8	0.504 9	0.304 5	0.472 3	0.115 6	0.000 1	0.750 4	0.677 4	0.202 2	0.000 7	0.509 4	0.729 2	0.000 1	0.519 9	0.000 11	0.000 5	0.000 1	0.620 9	0.000 2	0.000 1	0.660 4	0.560 4	0.486 3	0.384 6	0.346 5	0.952 3	0.247 9	0.667 3	0.436 7	0.269 3	0.691 4	0.000 1	0.010 3	0.787 5	0.889 2	0.880 4	0.000 1	0.810 5	0.336 4	0.860 6	0.000 1	0.606 4	0.009 6	0.248 6	0.681 4	0.392 7
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
LGround	0.272 10	0.485 10	0.184 10	0.106 10	0.476 7	0.077 7	0.218 6	0.000 1	0.000 3	0.000 1	0.547 1	0.295 8	0.540 4	0.746 9	0.745 10	0.058 11	0.112 11	0.005 1	0.658 6	0.077 12	0.000 6	0.322 10	0.178 11	0.512 9	0.190 8	0.199 1	0.277 10	0.000 1	0.000 3	0.173 4	0.399 7	0.000 6	0.000 1	0.039 11	0.000 2	0.858 10	0.085 6	0.676 7	0.002 3	0.103 4	0.498 6	0.323 9	0.703 9	0.000 1	0.000 2	0.296 10	0.549 7	0.216 1	0.702 4	0.768 9	0.718 9	0.028 8	0.092 11	0.786 11	0.000 1	0.000 8	0.453 11	0.022 5	0.251 12	0.252 7	0.572 10	0.348 10	0.321 6	0.514 5	0.063 10	0.279 11	0.552 10	0.000 4	0.019 11	0.932 10	0.132 11	0.000 1	0.000 8	0.000 10	0.156 12	0.457 10	0.623 8	0.518 9	0.265 11	0.358 8	0.381 10	0.395 10	0.000 3	0.000 1	0.127 12	0.012 4	0.051 1	0.000 7	0.000 3	0.886 9	0.014 9	0.437 12	0.179 3	0.244 10	0.826 10	0.000 7	0.000 1	0.599 7	0.136 1	0.085 3	0.000 4	0.000 1	0.000 6	0.565 8	0.612 9	0.143 3	0.207 10	0.566 10	0.232 10	0.446 10	0.127 2	0.708 10	0.000 3	0.384 6	0.000 1	0.000 4	0.000 2	0.402 9	0.000 1	0.059 3	0.000 1	0.525 12	0.566 8	0.229 9	0.659 10	0.000 7	0.000 1	0.265 10	0.446 9	0.147 11	0.720 12	0.597 5	0.066 9	0.000 7	0.187 4	0.000 1	0.726 8	0.467 12	0.134 8	0.000 7	0.413 10	0.629 8	0.000 1	0.363 11	0.055 7	0.022 2	0.000 1	0.626 8	0.000 2	0.000 1	0.323 10	0.479 12	0.154 11	0.117 10	0.028 11	0.901 10	0.243 10	0.415 11	0.295 12	0.143 6	0.610 11	0.000 1	0.000 8	0.777 7	0.397 12	0.324 11	0.000 1	0.778 10	0.179 8	0.702 11	0.000 1	0.274 12	0.404 1	0.233 7	0.622 10	0.398 6
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
AWCS	0.305 9	0.508 9	0.225 9	0.142 6	0.463 9	0.063 10	0.195 7	0.000 1	0.000 3	0.000 1	0.467 2	0.551 1	0.504 6	0.773 6	0.764 9	0.142 9	0.029 12	0.000 3	0.626 8	0.100 8	0.000 6	0.360 9	0.179 10	0.507 10	0.137 10	0.006 4	0.300 9	0.000 1	0.000 3	0.172 5	0.364 10	0.512 4	0.000 1	0.056 9	0.000 2	0.865 9	0.093 4	0.634 12	0.000 5	0.071 9	0.396 10	0.296 11	0.876 6	0.000 1	0.000 2	0.373 9	0.436 11	0.063 7	0.749 1	0.877 5	0.721 7	0.131 3	0.124 9	0.804 10	0.000 1	0.000 8	0.515 7	0.010 6	0.452 8	0.252 7	0.578 9	0.417 6	0.179 12	0.484 7	0.171 4	0.337 9	0.606 9	0.000 4	0.115 5	0.937 9	0.142 8	0.000 1	0.008 7	0.000 10	0.157 11	0.484 9	0.402 12	0.501 10	0.339 6	0.553 4	0.529 2	0.478 9	0.000 3	0.000 1	0.404 7	0.001 6	0.022 8	0.077 6	0.000 3	0.894 8	0.219 4	0.628 5	0.093 10	0.305 9	0.886 1	0.233 4	0.000 1	0.603 6	0.112 4	0.023 7	0.000 4	0.000 1	0.000 6	0.741 2	0.664 5	0.097 10	0.253 9	0.782 9	0.264 6	0.523 8	0.154 1	0.707 11	0.000 3	0.411 5	0.000 1	0.000 4	0.000 2	0.332 11	0.000 1	0.000 5	0.000 1	0.602 4	0.595 7	0.185 10	0.656 11	0.159 4	0.000 1	0.355 8	0.424 10	0.154 10	0.729 10	0.516 7	0.220 7	0.620 2	0.084 8	0.000 1	0.707 9	0.651 8	0.173 3	0.014 6	0.381 12	0.582 10	0.000 1	0.619 2	0.049 9	0.000 5	0.000 1	0.702 2	0.000 2	0.000 1	0.302 11	0.489 10	0.317 8	0.334 7	0.392 3	0.922 9	0.254 8	0.533 9	0.394 8	0.129 12	0.613 10	0.000 1	0.000 8	0.820 2	0.649 8	0.749 9	0.000 1	0.782 9	0.282 6	0.863 4	0.000 1	0.288 11	0.006 7	0.220 8	0.633 9	0.542 2

Minkowski 34D	0.253 11	0.463 11	0.154 12	0.102 11	0.381 12	0.084 5	0.134 10	0.000 1	0.000 3	0.000 1	0.386 4	0.141 12	0.279 12	0.737 11	0.703 11	0.014 12	0.164 10	0.000 3	0.663 5	0.092 11	0.000 6	0.224 11	0.291 6	0.531 6	0.056 12	0.000 5	0.242 11	0.000 1	0.000 3	0.013 10	0.331 11	0.000 6	0.000 1	0.035 12	0.001 1	0.858 10	0.059 11	0.650 11	0.000 5	0.056 10	0.353 11	0.299 10	0.670 10	0.000 1	0.000 2	0.284 11	0.484 10	0.071 6	0.594 11	0.720 11	0.710 10	0.027 9	0.068 12	0.813 9	0.000 1	0.005 7	0.492 9	0.164 1	0.274 11	0.111 11	0.571 11	0.307 12	0.293 8	0.307 12	0.150 5	0.163 12	0.531 11	0.002 3	0.545 3	0.932 10	0.093 12	0.000 1	0.000 8	0.002 9	0.159 10	0.368 12	0.581 10	0.440 12	0.228 12	0.406 6	0.282 12	0.294 11	0.000 3	0.000 1	0.189 11	0.060 2	0.036 4	0.000 7	0.000 3	0.897 7	0.000 12	0.525 9	0.025 12	0.205 12	0.771 12	0.000 7	0.000 1	0.593 8	0.108 7	0.044 4	0.000 4	0.000 1	0.000 6	0.282 12	0.589 10	0.094 11	0.169 11	0.466 12	0.227 11	0.419 12	0.125 3	0.757 9	0.002 1	0.334 11	0.000 1	0.000 4	0.000 2	0.357 10	0.000 1	0.000 5	0.000 1	0.582 7	0.513 11	0.337 8	0.612 12	0.000 7	0.000 1	0.250 11	0.352 12	0.136 12	0.724 11	0.655 3	0.280 6	0.000 7	0.046 11	0.000 1	0.606 12	0.559 10	0.159 5	0.102 1	0.445 5	0.655 5	0.000 1	0.310 12	0.117 3	0.000 5	0.000 1	0.581 12	0.026 1	0.000 1	0.265 12	0.483 11	0.084 12	0.097 12	0.044 10	0.865 12	0.142 12	0.588 7	0.351 10	0.272 2	0.596 12	0.000 1	0.003 6	0.622 11	0.720 7	0.096 12	0.000 1	0.771 11	0.016 11	0.772 10	0.000 1	0.302 10	0.194 4	0.214 9	0.621 11	0.197 12
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain	0.249 12	0.455 12	0.171 11	0.079 12	0.418 11	0.059 11	0.186 8	0.000 1	0.000 3	0.000 1	0.335 7	0.250 10	0.316 11	0.766 7	0.697 12	0.142 9	0.170 9	0.003 2	0.553 9	0.112 6	0.097 1	0.201 12	0.186 9	0.476 12	0.081 11	0.000 5	0.216 12	0.000 1	0.000 3	0.001 12	0.314 12	0.000 6	0.000 1	0.055 10	0.000 2	0.832 12	0.094 3	0.659 10	0.002 3	0.076 7	0.310 12	0.293 12	0.664 11	0.000 1	0.000 2	0.175 12	0.634 4	0.130 2	0.552 12	0.686 12	0.700 12	0.076 6	0.110 10	0.770 12	0.000 1	0.000 8	0.430 12	0.000 7	0.319 10	0.166 10	0.542 12	0.327 11	0.205 11	0.332 11	0.052 11	0.375 8	0.444 12	0.000 4	0.012 12	0.930 12	0.203 2	0.000 1	0.000 8	0.046 7	0.175 9	0.413 11	0.592 9	0.471 11	0.299 10	0.152 12	0.340 11	0.247 12	0.000 3	0.000 1	0.225 10	0.058 3	0.037 3	0.000 7	0.207 1	0.862 11	0.014 9	0.548 8	0.033 11	0.233 11	0.816 11	0.000 7	0.000 1	0.542 10	0.123 3	0.121 1	0.019 2	0.000 1	0.000 6	0.463 11	0.454 12	0.045 12	0.128 12	0.557 11	0.235 9	0.441 11	0.063 8	0.484 12	0.000 3	0.308 12	0.000 1	0.000 4	0.000 2	0.318 12	0.000 1	0.000 5	0.000 1	0.545 11	0.543 9	0.164 11	0.734 6	0.000 7	0.000 1	0.215 12	0.371 11	0.198 9	0.743 9	0.205 11	0.062 10	0.000 7	0.079 9	0.000 1	0.683 11	0.547 11	0.142 7	0.000 7	0.441 6	0.579 11	0.000 1	0.464 10	0.098 6	0.041 1	0.000 1	0.590 11	0.000 2	0.000 1	0.373 8	0.494 9	0.174 10	0.105 11	0.001 12	0.895 11	0.222 11	0.537 8	0.307 11	0.180 5	0.625 9	0.000 1	0.000 8	0.591 12	0.609 10	0.398 10	0.000 1	0.766 12	0.014 12	0.638 12	0.000 1	0.377 9	0.004 8	0.206 10	0.609 12	0.465 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
L3DETR-ScanNet_200	0.336 5	0.533 8	0.279 3	0.155 5	0.508 3	0.073 8	0.101 12	0.000 1	0.058 2	0.000 1	0.294 11	0.233 11	0.548 3	0.927 1	0.788 6	0.264 1	0.463 6	0.000 3	0.638 7	0.098 10	0.014 4	0.411 8	0.226 8	0.525 8	0.225 7	0.010 3	0.397 3	0.000 1	0.000 3	0.192 3	0.380 9	0.598 3	0.000 1	0.117 3	0.000 2	0.883 4	0.082 7	0.689 2	0.000 5	0.032 12	0.549 4	0.417 4	0.910 3	0.000 1	0.000 2	0.448 6	0.613 6	0.000 8	0.697 5	0.960 1	0.759 2	0.158 2	0.293 1	0.883 4	0.000 1	0.312 2	0.583 1	0.079 4	0.422 9	0.068 12	0.660 5	0.418 5	0.298 7	0.430 9	0.114 7	0.526 3	0.776 1	0.051 2	0.679 1	0.946 4	0.152 6	0.000 1	0.183 4	0.000 10	0.211 5	0.511 7	0.409 11	0.565 7	0.355 5	0.448 5	0.512 4	0.557 2	0.000 3	0.000 1	0.420 6	0.000 7	0.007 12	0.104 3	0.000 3	0.125 12	0.330 2	0.514 10	0.146 7	0.321 8	0.860 6	0.174 6	0.000 1	0.629 3	0.075 11	0.000 10	0.000 4	0.000 1	0.002 5	0.671 4	0.712 4	0.141 4	0.339 7	0.856 3	0.261 7	0.529 7	0.067 7	0.835 1	0.000 3	0.369 9	0.000 1	0.259 2	0.000 2	0.629 3	0.000 1	0.487 1	0.000 1	0.579 8	0.646 2	0.107 12	0.720 8	0.122 5	0.000 1	0.333 9	0.505 7	0.303 6	0.908 1	0.503 10	0.565 1	0.074 6	0.324 1	0.000 1	0.740 5	0.661 6	0.109 9	0.000 7	0.427 8	0.563 12	0.000 1	0.579 8	0.108 5	0.000 5	0.000 1	0.664 3	0.000 2	0.000 1	0.641 5	0.539 6	0.416 4	0.515 2	0.256 6	0.940 8	0.312 3	0.209 12	0.620 1	0.138 10	0.636 8	0.000 1	0.000 8	0.775 8	0.861 4	0.765 8	0.000 1	0.801 7	0.119 10	0.860 6	0.000 1	0.687 1	0.001 9	0.192 11	0.679 6	0.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OctFormer ScanNet200	0.326 8	0.539 7	0.265 7	0.131 7	0.499 4	0.110 1	0.522 1	0.000 1	0.000 3	0.000 1	0.318 8	0.427 4	0.455 10	0.743 10	0.765 8	0.175 7	0.842 1	0.000 3	0.828 2	0.204 1	0.033 3	0.429 7	0.335 3	0.601 1	0.312 2	0.000 5	0.357 7	0.000 1	0.000 3	0.047 8	0.423 6	0.000 6	0.000 1	0.105 6	0.000 2	0.873 7	0.079 8	0.670 8	0.000 5	0.117 3	0.471 9	0.432 3	0.829 8	0.000 1	0.000 2	0.584 2	0.417 12	0.089 4	0.684 7	0.837 7	0.705 11	0.021 10	0.178 6	0.892 3	0.000 1	0.028 5	0.505 8	0.000 7	0.457 7	0.200 9	0.662 2	0.412 7	0.244 10	0.496 6	0.000 12	0.451 5	0.626 6	0.000 4	0.102 6	0.943 7	0.138 9	0.000 1	0.000 8	0.149 5	0.291 3	0.534 6	0.722 4	0.632 4	0.331 7	0.253 11	0.453 6	0.487 8	0.000 3	0.000 1	0.479 4	0.000 7	0.022 8	0.000 7	0.000 3	0.900 6	0.128 6	0.684 2	0.164 5	0.413 2	0.854 8	0.000 7	0.000 1	0.512 11	0.074 12	0.003 8	0.000 4	0.000 1	0.000 6	0.469 10	0.613 8	0.132 6	0.529 5	0.871 2	0.227 11	0.582 5	0.026 12	0.787 7	0.000 3	0.339 10	0.000 1	0.000 4	0.000 2	0.626 4	0.000 1	0.029 4	0.000 1	0.587 6	0.612 5	0.411 5	0.724 7	0.000 7	0.000 1	0.407 3	0.552 3	0.513 1	0.849 5	0.655 3	0.408 2	0.000 7	0.296 2	0.000 1	0.686 10	0.645 9	0.145 6	0.022 5	0.414 9	0.633 7	0.000 1	0.637 1	0.224 1	0.000 5	0.000 1	0.650 5	0.000 2	0.000 1	0.622 6	0.535 7	0.343 7	0.483 3	0.230 8	0.943 6	0.289 5	0.618 5	0.596 2	0.140 8	0.679 5	0.000 1	0.022 2	0.783 6	0.620 9	0.906 1	0.000 1	0.806 6	0.137 9	0.865 3	0.000 1	0.378 8	0.000 10	0.168 12	0.680 5	0.227 11
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023

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

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

Mask3D Scannet200	0.388 1	0.542 1	0.357 1	0.237 1	0.610 1	0.091 1	0.125 5	0.000 1	0.000 1	0.000 1	0.065 3	0.668 1	0.451 1	1.000 1	0.955 1	0.640 1	0.500 1	0.039 1	0.125 2	0.063 2	0.409 1	0.311 2	0.291 1	0.609 3	0.266 1	0.000 1	0.163 1	0.000 1	0.008 1	0.044 2	0.496 1	1.000 1	0.000 1	0.018 2	0.000 1	0.756 1	0.573 1	0.808 2	0.000 1	0.010 1	0.042 3	0.130 3	0.552 1	0.042 1	0.000 1	1.000 1	0.725 4	0.750 1	0.883 1	1.000 1	0.832 4	0.024 2	0.107 1	0.614 3	0.226 1	0.250 1	0.628 2	0.792 1	0.677 2	0.400 1	0.741 1	0.278 1	0.511 1	0.077 5	0.111 1	0.313 2	0.715 2	0.302 1	0.017 3	0.200 2	0.000 1	0.188 1	0.000 1	0.178 2	0.736 1	1.000 1	0.615 1	0.514 1	0.409 2	0.380 5	0.600 1	0.000 1	0.000 1	0.400 1	0.013 2	0.254 1	0.381 1	0.000 1	0.123 4	0.400 1	0.839 1	0.258 1	0.463 1	0.926 1	0.265 1	0.000 1	0.857 2	0.099 1	0.021 2	0.500 1	0.027 1	0.028 1	1.000 1	0.502 5	0.016 1	0.076 4	0.500 1	0.612 1	0.578 1	0.005 2	0.597 2	0.194 1	0.497 1	0.000 1	0.500 1	0.000 2	0.323 4	0.000 1	1.000 1	0.000 1	0.748 1	0.708 2	0.050 4	0.890 2	1.000 1	0.008 2	0.151 3	0.301 1	1.000 1	1.000 1	0.792 3	0.945 1	1.000 1	0.511 1	0.004 2	0.753 1	0.776 2	0.287 2	0.020 2	0.003 4	0.974 3	0.033 1	0.412 5	0.000 1	0.000 2	0.000 2	0.667 1	0.000 1	0.000 1	0.491 1	0.676 2	0.352 1	0.335 1	0.060 2	0.822 5	0.527 2	1.000 1	0.517 1	0.606 1	0.853 1	0.000 1	0.004 1	0.806 1	1.000 1	0.727 1	0.000 1	0.042 2	0.739 2	0.000 1	0.399 3	0.391 1	0.504 1	0.591 1	0.571 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
CSC-Pretrain Inst.	0.209 4	0.361 5	0.157 4	0.085 4	0.506 2	0.007 3	0.500 1	0.000 1	0.000 1	0.000 1	0.000 5	0.093 5	0.221 4	0.667 4	0.524 5	0.400 3	0.000 2	0.000 3	0.000 3	0.004 4	0.000 2	0.000 5	0.109 5	0.589 4	0.000 4	0.000 1	0.059 5	0.000 1	0.000 2	0.000 3	0.322 2	0.000 2	0.000 1	0.000 3	0.000 1	0.405 3	0.055 4	0.700 5	0.000 1	0.000 2	0.028 4	0.091 5	0.083 3	0.000 2	0.000 1	0.667 2	0.768 2	0.000 4	0.807 3	1.000 1	0.776 5	0.000 3	0.000 5	0.340 5	0.000 3	0.000 3	0.103 5	0.000 3	0.750 1	0.200 3	0.634 5	0.053 5	0.246 3	0.677 2	0.006 5	0.198 3	0.432 4	0.000 2	0.000 4	0.050 4	0.000 1	0.000 3	0.000 1	0.111 5	0.356 4	0.500 2	0.188 5	0.000 4	0.220 4	0.448 2	0.050 5	0.000 1	0.000 1	0.000 3	0.000 3	0.032 5	0.000 3	0.000 1	0.396 2	0.000 4	0.573 4	0.000 5	0.228 3	0.747 4	0.000 3	0.000 1	0.573 5	0.021 5	0.000 3	0.000 3	0.000 2	0.000 3	0.500 4	0.573 3	0.000 2	0.000 5	0.125 4	0.592 3	0.364 5	0.000 3	0.450 5	0.000 2	0.364 2	0.000 1	0.000 3	0.000 2	0.340 3	0.000 1	0.000 2	0.000 1	0.610 3	0.833 1	0.221 1	0.702 5	0.000 3	0.000 3	0.135 5	0.094 4	0.125 2	0.571 4	0.500 4	0.143 5	0.000 2	0.125 3	0.000 3	0.618 2	0.667 4	0.115 5	0.000 3	0.125 2	1.000 1	0.000 3	0.500 2	0.000 1	0.000 2	0.000 2	0.502 4	0.000 1	0.000 1	0.312 4	0.248 5	0.050 4	0.000 5	0.000 3	0.997 3	0.420 3	0.500 4	0.149 5	0.451 2	0.748 2	0.000 1	0.000 2	0.636 3	0.667 5	0.600 2	0.000 1	0.000 3	0.278 5	0.000 1	0.333 4	0.000 5	0.294 2	0.381 5	0.110 3
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.	0.246 3	0.413 3	0.170 3	0.130 3	0.455 5	0.003 5	0.500 1	0.000 1	0.000 1	0.000 1	0.017 4	0.333 4	0.111 5	1.000 1	0.681 4	0.400 3	0.000 2	0.000 3	1.000 1	0.003 5	0.000 2	0.167 3	0.190 2	0.637 2	0.067 3	0.000 1	0.081 3	0.000 1	0.000 2	0.000 3	0.264 4	0.000 2	0.000 1	0.000 3	0.000 1	0.387 4	0.031 5	0.754 3	0.000 1	0.000 2	0.151 2	0.135 2	0.056 4	0.000 2	0.000 1	0.582 4	0.589 5	0.500 2	0.815 2	1.000 1	0.903 1	0.000 3	0.097 2	0.588 4	0.000 3	0.000 3	0.234 3	0.000 3	0.500 3	0.400 1	0.682 4	0.156 3	0.159 4	0.750 1	0.046 3	0.125 4	0.660 3	0.000 2	0.200 2	0.000 5	0.000 1	0.000 3	0.000 1	0.164 3	0.402 3	0.500 2	0.373 3	0.025 3	0.143 5	0.426 3	0.317 2	0.000 1	0.000 1	0.000 3	0.000 3	0.063 3	0.000 3	0.000 1	0.000 5	0.000 4	0.575 3	0.250 2	0.241 2	0.772 3	0.000 3	0.000 1	0.653 4	0.034 3	0.000 3	0.000 3	0.000 2	0.000 3	1.000 1	0.561 4	0.000 2	0.100 2	0.500 1	0.541 4	0.452 3	0.000 3	0.581 3	0.000 2	0.364 2	0.000 1	0.000 3	0.000 2	0.571 2	0.000 1	0.000 2	0.000 1	0.568 4	0.511 4	0.167 3	0.857 3	0.000 3	0.000 3	0.164 2	0.112 3	0.000 5	0.530 5	1.000 1	0.286 3	0.000 2	0.125 3	0.000 3	0.464 5	0.706 3	0.208 4	0.000 3	0.125 2	0.744 4	0.000 3	0.500 2	0.000 1	0.000 2	0.000 2	0.511 3	0.000 1	0.000 1	0.344 2	0.541 3	0.068 3	0.333 2	0.000 3	1.000 1	0.196 4	0.533 3	0.318 3	0.000 4	0.748 3	0.000 1	0.000 2	0.690 2	1.000 1	0.400 3	0.000 1	0.000 3	0.667 3	0.000 1	0.333 4	0.333 2	0.270 3	0.399 3	0.083 4
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
TD3D Scannet200	0.320 2	0.501 2	0.264 2	0.164 2	0.506 3	0.062 2	0.500 1	0.000 1	0.000 1	0.000 1	0.208 1	0.431 2	0.252 3	1.000 1	0.733 3	0.587 2	0.000 2	0.008 2	0.000 3	0.106 1	0.000 2	0.356 1	0.123 4	0.686 1	0.101 2	0.000 1	0.152 2	0.000 1	0.000 2	0.226 1	0.280 3	0.000 2	0.000 1	0.250 1	0.000 1	0.619 2	0.061 3	0.841 1	0.000 1	0.000 2	0.167 1	0.194 1	0.333 2	0.000 2	0.000 1	0.667 2	0.820 1	0.250 3	0.790 4	1.000 1	0.879 2	0.077 1	0.094 3	0.708 1	0.217 2	0.049 2	0.634 1	0.792 1	0.331 4	0.033 5	0.716 2	0.159 2	0.396 2	0.331 4	0.099 2	0.415 1	0.842 1	0.000 2	0.458 1	0.542 1	0.000 1	0.101 2	0.000 1	0.218 1	0.513 2	0.500 2	0.458 2	0.104 2	0.516 1	0.456 1	0.268 4	0.000 1	0.000 1	0.400 1	0.022 1	0.233 2	0.143 2	0.000 1	0.677 1	0.400 1	0.504 5	0.095 3	0.083 5	0.890 2	0.061 2	0.000 1	0.906 1	0.076 2	0.231 1	0.125 2	0.000 2	0.003 2	0.792 3	0.881 1	0.000 2	0.098 3	0.125 4	0.498 5	0.459 2	0.063 1	0.715 1	0.000 2	0.241 4	0.000 1	0.396 2	0.063 1	0.605 1	0.000 1	0.000 2	0.000 1	0.448 5	0.629 3	0.202 2	0.967 1	0.250 2	0.038 1	0.192 1	0.185 2	0.083 4	1.000 1	1.000 1	0.857 2	0.000 2	0.470 2	0.012 1	0.565 3	0.798 1	0.621 1	0.111 1	0.500 1	1.000 1	0.017 2	0.509 1	0.000 1	0.008 1	1.000 1	0.525 2	0.000 1	0.000 1	0.332 3	0.679 1	0.264 2	0.333 2	0.267 1	1.000 1	0.549 1	0.299 5	0.387 2	0.328 3	0.744 4	0.000 1	0.000 2	0.435 5	1.000 1	0.283 4	0.000 1	0.196 1	0.817 1	0.000 1	0.472 1	0.222 3	0.123 4	0.560 2	0.156 2
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Minkowski 34D Inst.	0.203 5	0.369 4	0.134 5	0.078 5	0.479 4	0.003 4	0.500 1	0.000 1	0.000 1	0.000 1	0.100 2	0.371 3	0.300 2	0.667 4	0.746 2	0.400 3	0.000 2	0.000 3	0.000 3	0.031 3	0.000 2	0.074 4	0.165 3	0.413 5	0.000 4	0.000 1	0.070 4	0.000 1	0.000 2	0.000 3	0.221 5	0.000 2	0.000 1	0.000 3	0.000 1	0.372 5	0.070 2	0.706 4	0.000 1	0.000 2	0.000 5	0.123 4	0.033 5	0.000 2	0.000 1	0.422 5	0.732 3	0.000 4	0.778 5	1.000 1	0.845 3	0.000 3	0.090 4	0.636 2	0.000 3	0.000 3	0.158 4	0.000 3	0.250 5	0.050 4	0.693 3	0.123 4	0.051 5	0.385 3	0.009 4	0.118 5	0.406 5	0.000 2	0.000 4	0.200 2	0.000 1	0.000 3	0.000 1	0.133 4	0.307 5	0.500 2	0.251 4	0.000 4	0.281 3	0.402 4	0.317 2	0.000 1	0.000 1	0.000 3	0.000 3	0.060 4	0.000 3	0.000 1	0.396 2	0.200 3	0.669 2	0.021 4	0.218 4	0.720 5	0.000 3	0.000 1	0.696 3	0.025 4	0.000 3	0.000 3	0.000 2	0.000 3	0.125 5	0.596 2	0.000 2	0.191 1	0.500 1	0.595 2	0.369 4	0.000 3	0.500 4	0.000 2	0.143 5	0.000 1	0.000 3	0.000 2	0.226 5	0.000 1	0.000 2	0.000 1	0.701 2	0.511 4	0.000 5	0.851 4	0.000 3	0.000 3	0.150 4	0.052 5	0.100 3	0.981 3	0.500 4	0.286 3	0.000 2	0.000 5	0.000 3	0.545 4	0.522 5	0.250 3	0.000 3	0.000 5	0.522 5	0.000 3	0.500 2	0.000 1	0.000 2	0.000 2	0.282 5	0.000 1	0.000 1	0.178 5	0.382 4	0.018 5	0.056 4	0.000 3	0.997 3	0.107 5	0.677 2	0.313 4	0.000 4	0.726 5	0.000 1	0.000 2	0.583 4	0.903 4	0.200 5	0.000 1	0.000 3	0.333 4	0.000 1	0.442 2	0.083 4	0.109 5	0.387 4	0.000 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019

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 ScanNet	0.794 1	0.941 3	0.813 19	0.851 7	0.782 6	0.890 2	0.597 1	0.916 2	0.696 8	0.713 3	0.979 1	0.635 1	0.384 2	0.793 2	0.907 8	0.821 4	0.790 31	0.696 11	0.967 3	0.903 1	0.805 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)
PonderV2	0.785 2	0.978 1	0.800 27	0.833 23	0.788 4	0.853 16	0.545 16	0.910 5	0.713 1	0.705 4	0.979 1	0.596 7	0.390 1	0.769 12	0.832 41	0.821 4	0.792 30	0.730 1	0.975 1	0.897 4	0.785 4
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 3	0.964 2	0.855 1	0.843 16	0.781 7	0.858 12	0.575 6	0.831 33	0.685 14	0.714 2	0.979 1	0.594 8	0.310 26	0.801 1	0.892 16	0.841 2	0.819 4	0.723 4	0.940 13	0.887 6	0.725 24
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 4	0.861 20	0.818 14	0.836 20	0.790 3	0.875 4	0.576 5	0.905 6	0.704 5	0.739 1	0.969 10	0.611 2	0.349 10	0.756 22	0.958 1	0.702 45	0.805 14	0.708 7	0.916 32	0.898 3	0.801 2

TTT-KD	0.773 5	0.646 91	0.818 14	0.809 35	0.774 9	0.878 3	0.581 2	0.943 1	0.687 12	0.704 5	0.978 4	0.607 5	0.336 15	0.775 8	0.912 6	0.838 3	0.823 2	0.694 12	0.967 3	0.899 2	0.794 3
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 6	0.939 4	0.824 6	0.854 6	0.771 10	0.840 30	0.564 10	0.900 8	0.686 13	0.677 12	0.961 16	0.537 31	0.348 11	0.769 12	0.903 10	0.785 10	0.815 6	0.676 22	0.939 14	0.880 11	0.772 8

OctFormer	0.766 7	0.925 7	0.808 23	0.849 9	0.786 5	0.846 26	0.566 9	0.876 15	0.690 10	0.674 14	0.960 17	0.576 17	0.226 67	0.753 24	0.904 9	0.777 12	0.815 6	0.722 5	0.923 27	0.877 13	0.776 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint	0.766 7	0.932 5	0.794 33	0.829 25	0.751 22	0.854 14	0.540 20	0.903 7	0.630 34	0.672 15	0.963 14	0.565 21	0.357 8	0.788 3	0.900 12	0.737 25	0.802 15	0.685 17	0.950 7	0.887 6	0.780 5
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OccuSeg+Semantic	0.764 9	0.758 58	0.796 31	0.839 18	0.746 25	0.907 1	0.562 11	0.850 25	0.680 16	0.672 15	0.978 4	0.610 3	0.335 17	0.777 6	0.819 45	0.847 1	0.830 1	0.691 14	0.972 2	0.885 8	0.727 22

CU-Hybrid Net	0.764 9	0.924 8	0.819 12	0.840 17	0.757 17	0.853 16	0.580 3	0.848 26	0.709 3	0.643 24	0.958 21	0.587 12	0.295 33	0.753 24	0.884 20	0.758 19	0.815 6	0.725 3	0.927 24	0.867 22	0.743 15

O-CNN	0.762 11	0.924 8	0.823 7	0.844 15	0.770 11	0.852 18	0.577 4	0.847 28	0.711 2	0.640 28	0.958 21	0.592 9	0.217 73	0.762 17	0.888 17	0.758 19	0.813 10	0.726 2	0.932 22	0.868 21	0.744 14
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
DTC	0.757 12	0.843 26	0.820 10	0.847 12	0.791 2	0.862 10	0.511 33	0.870 17	0.707 4	0.652 20	0.954 35	0.604 6	0.279 44	0.760 18	0.942 2	0.734 26	0.766 44	0.701 10	0.884 54	0.874 19	0.736 16

OA-CNN-L_ScanNet20	0.756 13	0.783 44	0.826 5	0.858 4	0.776 8	0.837 33	0.548 15	0.896 11	0.649 26	0.675 13	0.962 15	0.586 13	0.335 17	0.771 11	0.802 49	0.770 15	0.787 33	0.691 14	0.936 17	0.880 11	0.761 10

PNE	0.755 14	0.786 42	0.835 4	0.834 22	0.758 15	0.849 21	0.570 8	0.836 32	0.648 27	0.668 17	0.978 4	0.581 16	0.367 6	0.683 35	0.856 29	0.804 6	0.801 19	0.678 19	0.961 5	0.889 5	0.716 29
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer	0.755 14	0.927 6	0.822 8	0.836 20	0.801 1	0.849 21	0.516 30	0.864 22	0.651 25	0.680 11	0.958 21	0.584 15	0.282 41	0.759 20	0.855 31	0.728 28	0.802 15	0.678 19	0.880 59	0.873 20	0.756 12
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 16	0.906 12	0.793 35	0.802 41	0.689 40	0.825 46	0.556 12	0.867 18	0.681 15	0.602 44	0.960 17	0.555 27	0.365 7	0.779 5	0.859 26	0.747 22	0.795 27	0.717 6	0.917 31	0.856 30	0.764 9
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 16	0.742 66	0.809 22	0.872 1	0.758 15	0.860 11	0.552 13	0.891 13	0.610 41	0.687 6	0.960 17	0.559 25	0.304 29	0.766 15	0.926 4	0.767 16	0.797 23	0.644 33	0.942 11	0.876 16	0.722 26
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer	0.749 18	0.793 40	0.790 36	0.807 37	0.750 24	0.856 13	0.524 26	0.881 14	0.588 53	0.642 27	0.977 8	0.591 10	0.274 47	0.781 4	0.929 3	0.804 6	0.796 24	0.642 34	0.947 9	0.885 8	0.715 30
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNet	0.749 18	0.909 10	0.818 14	0.811 33	0.752 20	0.839 32	0.485 47	0.842 29	0.673 18	0.644 23	0.957 25	0.528 37	0.305 28	0.773 9	0.859 26	0.788 8	0.818 5	0.693 13	0.916 32	0.856 30	0.723 25
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP	0.748 20	0.623 94	0.804 25	0.859 3	0.745 26	0.824 48	0.501 37	0.912 4	0.690 10	0.685 8	0.956 26	0.567 20	0.320 23	0.768 14	0.918 5	0.720 33	0.802 15	0.676 22	0.921 29	0.881 10	0.779 6

StratifiedFormer	0.747 21	0.901 13	0.803 26	0.845 14	0.757 17	0.846 26	0.512 32	0.825 36	0.696 8	0.645 22	0.956 26	0.576 17	0.262 58	0.744 29	0.861 25	0.742 23	0.770 42	0.705 8	0.899 44	0.860 27	0.734 17
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 22	0.870 18	0.838 2	0.858 4	0.729 31	0.850 20	0.501 37	0.874 16	0.587 54	0.658 19	0.956 26	0.564 22	0.299 31	0.765 16	0.900 12	0.716 36	0.812 11	0.631 39	0.939 14	0.858 28	0.709 31
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 22	0.771 52	0.819 12	0.848 11	0.702 37	0.865 9	0.397 85	0.899 9	0.699 6	0.664 18	0.948 55	0.588 11	0.330 19	0.746 28	0.851 35	0.764 17	0.796 24	0.704 9	0.935 18	0.866 23	0.728 20
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 24	0.725 75	0.814 18	0.837 19	0.751 22	0.831 40	0.514 31	0.896 11	0.674 17	0.684 9	0.960 17	0.564 22	0.303 30	0.773 9	0.820 44	0.713 39	0.798 22	0.690 16	0.923 27	0.875 17	0.757 11

Retro-FPN	0.744 25	0.842 27	0.800 27	0.767 55	0.740 27	0.836 35	0.541 18	0.914 3	0.672 19	0.626 32	0.958 21	0.552 28	0.272 49	0.777 6	0.886 19	0.696 46	0.801 19	0.674 25	0.941 12	0.858 28	0.717 27
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 26	0.620 95	0.799 30	0.849 9	0.730 30	0.822 50	0.493 44	0.897 10	0.664 20	0.681 10	0.955 29	0.562 24	0.378 3	0.760 18	0.903 10	0.738 24	0.801 19	0.673 26	0.907 36	0.877 13	0.745 13
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet	0.742 27	0.816 35	0.806 24	0.807 37	0.752 20	0.828 44	0.575 6	0.839 31	0.699 6	0.637 29	0.954 35	0.520 40	0.320 23	0.755 23	0.834 39	0.760 18	0.772 39	0.676 22	0.915 34	0.862 25	0.717 27

SAT	0.742 27	0.860 21	0.765 49	0.819 28	0.769 12	0.848 23	0.533 22	0.829 34	0.663 21	0.631 31	0.955 29	0.586 13	0.274 47	0.753 24	0.896 14	0.729 27	0.760 50	0.666 28	0.921 29	0.855 32	0.733 18

LargeKernel3D	0.739 29	0.909 10	0.820 10	0.806 39	0.740 27	0.852 18	0.545 16	0.826 35	0.594 52	0.643 24	0.955 29	0.541 30	0.263 57	0.723 33	0.858 28	0.775 14	0.767 43	0.678 19	0.933 20	0.848 37	0.694 36
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 30	0.776 48	0.790 36	0.851 7	0.754 19	0.854 14	0.491 46	0.866 20	0.596 51	0.686 7	0.955 29	0.536 32	0.342 13	0.624 50	0.869 22	0.787 9	0.802 15	0.628 40	0.927 24	0.875 17	0.704 33

MinkowskiNet	0.736 30	0.859 22	0.818 14	0.832 24	0.709 35	0.840 30	0.521 28	0.853 24	0.660 23	0.643 24	0.951 45	0.544 29	0.286 39	0.731 31	0.893 15	0.675 55	0.772 39	0.683 18	0.874 66	0.852 35	0.727 22
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 32	0.890 14	0.837 3	0.864 2	0.726 32	0.873 5	0.530 25	0.824 37	0.489 87	0.647 21	0.978 4	0.609 4	0.336 15	0.624 50	0.733 58	0.758 19	0.776 37	0.570 65	0.949 8	0.877 13	0.728 20

PointTransformer++	0.725 33	0.727 74	0.811 21	0.819 28	0.765 13	0.841 29	0.502 36	0.814 42	0.621 37	0.623 34	0.955 29	0.556 26	0.284 40	0.620 52	0.866 23	0.781 11	0.757 54	0.648 31	0.932 22	0.862 25	0.709 31

SparseConvNet	0.725 33	0.647 90	0.821 9	0.846 13	0.721 33	0.869 6	0.533 22	0.754 58	0.603 47	0.614 36	0.955 29	0.572 19	0.325 21	0.710 34	0.870 21	0.724 31	0.823 2	0.628 40	0.934 19	0.865 24	0.683 39

MatchingNet	0.724 35	0.812 37	0.812 20	0.810 34	0.735 29	0.834 37	0.495 43	0.860 23	0.572 61	0.602 44	0.954 35	0.512 42	0.280 43	0.757 21	0.845 37	0.725 30	0.780 35	0.606 50	0.937 16	0.851 36	0.700 35

INS-Conv-semantic	0.717 36	0.751 61	0.759 52	0.812 32	0.704 36	0.868 7	0.537 21	0.842 29	0.609 43	0.608 40	0.953 39	0.534 34	0.293 34	0.616 53	0.864 24	0.719 35	0.793 28	0.640 35	0.933 20	0.845 41	0.663 45

PointMetaBase	0.714 37	0.835 28	0.785 38	0.821 26	0.684 42	0.846 26	0.531 24	0.865 21	0.614 38	0.596 48	0.953 39	0.500 45	0.246 63	0.674 36	0.888 17	0.692 47	0.764 46	0.624 42	0.849 81	0.844 42	0.675 41

contrastBoundary	0.705 38	0.769 55	0.775 43	0.809 35	0.687 41	0.820 53	0.439 73	0.812 43	0.661 22	0.591 50	0.945 63	0.515 41	0.171 91	0.633 47	0.856 29	0.720 33	0.796 24	0.668 27	0.889 51	0.847 38	0.689 37
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 39	0.774 50	0.800 27	0.793 46	0.760 14	0.847 25	0.471 51	0.802 46	0.463 94	0.634 30	0.968 12	0.491 48	0.271 51	0.726 32	0.910 7	0.706 41	0.815 6	0.551 77	0.878 60	0.833 43	0.570 77

RFCR	0.702 40	0.889 15	0.745 63	0.813 31	0.672 45	0.818 57	0.493 44	0.815 41	0.623 35	0.610 38	0.947 57	0.470 57	0.249 62	0.594 56	0.848 36	0.705 42	0.779 36	0.646 32	0.892 49	0.823 49	0.611 60
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 41	0.825 32	0.796 31	0.723 62	0.716 34	0.832 39	0.433 75	0.816 39	0.634 32	0.609 39	0.969 10	0.418 83	0.344 12	0.559 68	0.833 40	0.715 37	0.808 13	0.560 71	0.902 41	0.847 38	0.680 40

JSENet	0.699 42	0.881 17	0.762 50	0.821 26	0.667 46	0.800 69	0.522 27	0.792 49	0.613 39	0.607 41	0.935 83	0.492 47	0.205 78	0.576 61	0.853 33	0.691 49	0.758 52	0.652 30	0.872 69	0.828 46	0.649 49
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 43	0.743 65	0.794 33	0.655 85	0.684 42	0.822 50	0.497 42	0.719 68	0.622 36	0.617 35	0.977 8	0.447 70	0.339 14	0.750 27	0.664 74	0.703 44	0.790 31	0.596 55	0.946 10	0.855 32	0.647 50
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 44	0.732 70	0.772 44	0.786 47	0.677 44	0.866 8	0.517 29	0.848 26	0.509 80	0.626 32	0.952 43	0.536 32	0.225 69	0.545 74	0.704 65	0.689 52	0.810 12	0.564 70	0.903 40	0.854 34	0.729 19
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 45	0.884 16	0.754 56	0.795 44	0.647 53	0.818 57	0.422 77	0.802 46	0.612 40	0.604 42	0.945 63	0.462 60	0.189 86	0.563 67	0.853 33	0.726 29	0.765 45	0.632 38	0.904 38	0.821 52	0.606 64
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 46	0.704 80	0.741 67	0.754 59	0.656 48	0.829 42	0.501 37	0.741 63	0.609 43	0.548 58	0.950 49	0.522 39	0.371 4	0.633 47	0.756 53	0.715 37	0.771 41	0.623 43	0.861 77	0.814 55	0.658 46
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 47	0.866 19	0.748 60	0.819 28	0.645 55	0.794 72	0.450 63	0.802 46	0.587 54	0.604 42	0.945 63	0.464 59	0.201 81	0.554 70	0.840 38	0.723 32	0.732 64	0.602 53	0.907 36	0.822 51	0.603 67

KP-FCNN	0.684 48	0.847 25	0.758 54	0.784 49	0.647 53	0.814 60	0.473 50	0.772 52	0.605 45	0.594 49	0.935 83	0.450 68	0.181 89	0.587 57	0.805 48	0.690 50	0.785 34	0.614 46	0.882 56	0.819 53	0.632 56
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++	0.684 48	0.728 73	0.757 55	0.776 52	0.690 38	0.804 67	0.464 56	0.816 39	0.577 60	0.587 51	0.945 63	0.508 44	0.276 46	0.671 37	0.710 63	0.663 60	0.750 58	0.589 60	0.881 57	0.832 45	0.653 48

DGNet	0.684 48	0.712 79	0.784 39	0.782 51	0.658 47	0.835 36	0.499 41	0.823 38	0.641 29	0.597 47	0.950 49	0.487 50	0.281 42	0.575 62	0.619 78	0.647 68	0.764 46	0.620 45	0.871 72	0.846 40	0.688 38

PointContrast_LA_SEM	0.683 51	0.757 59	0.784 39	0.786 47	0.639 57	0.824 48	0.408 80	0.775 51	0.604 46	0.541 60	0.934 87	0.532 35	0.269 53	0.552 71	0.777 51	0.645 71	0.793 28	0.640 35	0.913 35	0.824 48	0.671 42

Superpoint Network	0.683 51	0.851 24	0.728 71	0.800 43	0.653 50	0.806 65	0.468 53	0.804 44	0.572 61	0.602 44	0.946 60	0.453 67	0.239 66	0.519 79	0.822 42	0.689 52	0.762 49	0.595 57	0.895 47	0.827 47	0.630 57

VI-PointConv	0.676 53	0.770 54	0.754 56	0.783 50	0.621 61	0.814 60	0.552 13	0.758 56	0.571 63	0.557 56	0.954 35	0.529 36	0.268 55	0.530 77	0.682 69	0.675 55	0.719 67	0.603 52	0.888 52	0.833 43	0.665 44
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 54	0.789 41	0.748 60	0.763 57	0.635 59	0.814 60	0.407 82	0.747 60	0.581 58	0.573 53	0.950 49	0.484 51	0.271 51	0.607 54	0.754 54	0.649 65	0.774 38	0.596 55	0.883 55	0.823 49	0.606 64

SALANet	0.670 55	0.816 35	0.770 47	0.768 54	0.652 51	0.807 64	0.451 60	0.747 60	0.659 24	0.545 59	0.924 93	0.473 56	0.149 101	0.571 64	0.811 47	0.635 74	0.746 59	0.623 43	0.892 49	0.794 68	0.570 77

O3DSeg	0.668 56	0.822 33	0.771 46	0.496 105	0.651 52	0.833 38	0.541 18	0.761 55	0.555 69	0.611 37	0.966 13	0.489 49	0.370 5	0.388 99	0.580 81	0.776 13	0.751 56	0.570 65	0.956 6	0.817 54	0.646 51

PointConv	0.666 57	0.781 45	0.759 52	0.699 70	0.644 56	0.822 50	0.475 49	0.779 50	0.564 66	0.504 76	0.953 39	0.428 77	0.203 80	0.586 59	0.754 54	0.661 61	0.753 55	0.588 61	0.902 41	0.813 57	0.642 52
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 57	0.703 81	0.781 41	0.751 61	0.655 49	0.830 41	0.471 51	0.769 53	0.474 90	0.537 62	0.951 45	0.475 55	0.279 44	0.635 45	0.698 68	0.675 55	0.751 56	0.553 76	0.816 88	0.806 59	0.703 34
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 59	0.746 63	0.708 74	0.722 63	0.638 58	0.820 53	0.451 60	0.566 96	0.599 49	0.541 60	0.950 49	0.510 43	0.313 25	0.648 42	0.819 45	0.616 79	0.682 82	0.590 59	0.869 73	0.810 58	0.656 47
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN	0.658 60	0.558 102	0.751 58	0.655 85	0.690 38	0.722 94	0.453 59	0.867 18	0.579 59	0.576 52	0.893 105	0.523 38	0.293 34	0.733 30	0.571 83	0.692 47	0.659 89	0.606 50	0.875 63	0.804 61	0.668 43

DCM-Net	0.658 60	0.778 46	0.702 77	0.806 39	0.619 62	0.813 63	0.468 53	0.693 76	0.494 83	0.524 68	0.941 75	0.449 69	0.298 32	0.510 81	0.821 43	0.675 55	0.727 66	0.568 68	0.826 86	0.803 62	0.637 54
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 62	0.698 83	0.743 65	0.650 87	0.564 79	0.820 53	0.505 35	0.758 56	0.631 33	0.479 80	0.945 63	0.480 53	0.226 67	0.572 63	0.774 52	0.690 50	0.735 62	0.614 46	0.853 80	0.776 83	0.597 70
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 63	0.752 60	0.734 69	0.664 83	0.583 74	0.815 59	0.399 84	0.754 58	0.639 30	0.535 64	0.942 73	0.470 57	0.309 27	0.665 38	0.539 85	0.650 64	0.708 72	0.635 37	0.857 79	0.793 70	0.642 52
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 64	0.778 46	0.731 70	0.699 70	0.577 75	0.829 42	0.446 65	0.736 64	0.477 89	0.523 70	0.945 63	0.454 64	0.269 53	0.484 89	0.749 57	0.618 77	0.738 60	0.599 54	0.827 85	0.792 73	0.621 59

PointConv-SFPN	0.641 65	0.776 48	0.703 76	0.721 64	0.557 82	0.826 45	0.451 60	0.672 81	0.563 67	0.483 79	0.943 72	0.425 80	0.162 96	0.644 43	0.726 59	0.659 62	0.709 71	0.572 64	0.875 63	0.786 78	0.559 83

MVPNet	0.641 65	0.831 29	0.715 72	0.671 80	0.590 70	0.781 78	0.394 86	0.679 78	0.642 28	0.553 57	0.937 80	0.462 60	0.256 59	0.649 41	0.406 99	0.626 75	0.691 79	0.666 28	0.877 61	0.792 73	0.608 63
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 67	0.717 78	0.701 78	0.692 73	0.576 76	0.801 68	0.467 55	0.716 69	0.563 67	0.459 86	0.953 39	0.429 76	0.169 93	0.581 60	0.854 32	0.605 80	0.710 69	0.550 78	0.894 48	0.793 70	0.575 75

FPConv	0.639 68	0.785 43	0.760 51	0.713 68	0.603 65	0.798 70	0.392 87	0.534 101	0.603 47	0.524 68	0.948 55	0.457 62	0.250 61	0.538 75	0.723 61	0.598 84	0.696 77	0.614 46	0.872 69	0.799 63	0.567 80
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 69	0.797 39	0.769 48	0.641 93	0.590 70	0.820 53	0.461 57	0.537 100	0.637 31	0.536 63	0.947 57	0.388 90	0.206 77	0.656 39	0.668 72	0.647 68	0.732 64	0.585 62	0.868 74	0.793 70	0.473 103

PointSPNet	0.637 70	0.734 69	0.692 85	0.714 67	0.576 76	0.797 71	0.446 65	0.743 62	0.598 50	0.437 91	0.942 73	0.403 86	0.150 100	0.626 49	0.800 50	0.649 65	0.697 76	0.557 74	0.846 82	0.777 82	0.563 81

SConv	0.636 71	0.830 30	0.697 81	0.752 60	0.572 78	0.780 80	0.445 67	0.716 69	0.529 73	0.530 65	0.951 45	0.446 71	0.170 92	0.507 84	0.666 73	0.636 73	0.682 82	0.541 84	0.886 53	0.799 63	0.594 71

Supervoxel-CNN	0.635 72	0.656 88	0.711 73	0.719 65	0.613 63	0.757 89	0.444 70	0.765 54	0.534 72	0.566 54	0.928 91	0.478 54	0.272 49	0.636 44	0.531 87	0.664 59	0.645 93	0.508 91	0.864 76	0.792 73	0.611 60

joint point-based	0.634 73	0.614 96	0.778 42	0.667 82	0.633 60	0.825 46	0.420 78	0.804 44	0.467 92	0.561 55	0.951 45	0.494 46	0.291 36	0.566 65	0.458 94	0.579 90	0.764 46	0.559 73	0.838 83	0.814 55	0.598 69
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 74	0.731 71	0.688 88	0.675 77	0.591 69	0.784 77	0.444 70	0.565 97	0.610 41	0.492 77	0.949 53	0.456 63	0.254 60	0.587 57	0.706 64	0.599 83	0.665 88	0.612 49	0.868 74	0.791 76	0.579 74

3DSM_DMMF	0.631 75	0.626 93	0.745 63	0.801 42	0.607 64	0.751 90	0.506 34	0.729 67	0.565 65	0.491 78	0.866 108	0.434 72	0.197 84	0.595 55	0.630 77	0.709 40	0.705 74	0.560 71	0.875 63	0.740 93	0.491 98

PointNet2-SFPN	0.631 75	0.771 52	0.692 85	0.672 78	0.524 87	0.837 33	0.440 72	0.706 74	0.538 71	0.446 88	0.944 69	0.421 82	0.219 72	0.552 71	0.751 56	0.591 86	0.737 61	0.543 83	0.901 43	0.768 85	0.557 84

APCF-Net	0.631 75	0.742 66	0.687 90	0.672 78	0.557 82	0.792 75	0.408 80	0.665 82	0.545 70	0.508 73	0.952 43	0.428 77	0.186 87	0.634 46	0.702 66	0.620 76	0.706 73	0.555 75	0.873 67	0.798 65	0.581 73
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv	0.630 78	0.604 98	0.741 67	0.766 56	0.590 70	0.747 91	0.501 37	0.734 65	0.503 82	0.527 66	0.919 97	0.454 64	0.323 22	0.550 73	0.420 98	0.678 54	0.688 80	0.544 81	0.896 46	0.795 67	0.627 58
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 79	0.800 38	0.625 101	0.719 65	0.545 84	0.806 65	0.445 67	0.597 90	0.448 97	0.519 71	0.938 79	0.481 52	0.328 20	0.489 88	0.499 92	0.657 63	0.759 51	0.592 58	0.881 57	0.797 66	0.634 55

SegGroup_sem	0.627 80	0.818 34	0.747 62	0.701 69	0.602 66	0.764 86	0.385 91	0.629 87	0.490 85	0.508 73	0.931 90	0.409 85	0.201 81	0.564 66	0.725 60	0.618 77	0.692 78	0.539 85	0.873 67	0.794 68	0.548 87
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 81	0.830 30	0.694 83	0.757 58	0.563 80	0.772 84	0.448 64	0.647 85	0.520 76	0.509 72	0.949 53	0.431 75	0.191 85	0.496 86	0.614 79	0.647 68	0.672 86	0.535 87	0.876 62	0.783 79	0.571 76

dtc_net	0.625 81	0.703 81	0.751 58	0.794 45	0.535 85	0.848 23	0.480 48	0.676 80	0.528 74	0.469 83	0.944 69	0.454 64	0.004 114	0.464 91	0.636 76	0.704 43	0.758 52	0.548 80	0.924 26	0.787 77	0.492 97

HPEIN	0.618 83	0.729 72	0.668 91	0.647 89	0.597 68	0.766 85	0.414 79	0.680 77	0.520 76	0.525 67	0.946 60	0.432 73	0.215 74	0.493 87	0.599 80	0.638 72	0.617 98	0.570 65	0.897 45	0.806 59	0.605 66
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 84	0.858 23	0.772 44	0.489 106	0.532 86	0.792 75	0.404 83	0.643 86	0.570 64	0.507 75	0.935 83	0.414 84	0.046 111	0.510 81	0.702 66	0.602 82	0.705 74	0.549 79	0.859 78	0.773 84	0.534 90
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 85	0.760 57	0.667 92	0.649 88	0.521 88	0.793 73	0.457 58	0.648 84	0.528 74	0.434 93	0.947 57	0.401 87	0.153 99	0.454 92	0.721 62	0.648 67	0.717 68	0.536 86	0.904 38	0.765 86	0.485 99
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer	0.600 86	0.634 92	0.743 65	0.697 72	0.601 67	0.781 78	0.437 74	0.585 93	0.493 84	0.446 88	0.933 88	0.394 88	0.011 113	0.654 40	0.661 75	0.603 81	0.733 63	0.526 88	0.832 84	0.761 88	0.480 100

LAP-D	0.594 87	0.720 76	0.692 85	0.637 94	0.456 98	0.773 83	0.391 89	0.730 66	0.587 54	0.445 90	0.940 77	0.381 91	0.288 37	0.434 95	0.453 96	0.591 86	0.649 91	0.581 63	0.777 92	0.749 92	0.610 62

DPC	0.592 88	0.720 76	0.700 79	0.602 98	0.480 94	0.762 88	0.380 92	0.713 72	0.585 57	0.437 91	0.940 77	0.369 93	0.288 37	0.434 95	0.509 91	0.590 88	0.639 96	0.567 69	0.772 94	0.755 90	0.592 72
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 89	0.766 56	0.659 96	0.683 75	0.470 97	0.740 93	0.387 90	0.620 89	0.490 85	0.476 81	0.922 95	0.355 96	0.245 64	0.511 80	0.511 90	0.571 91	0.643 94	0.493 95	0.872 69	0.762 87	0.600 68

ROSMRF	0.580 90	0.772 51	0.707 75	0.681 76	0.563 80	0.764 86	0.362 94	0.515 102	0.465 93	0.465 85	0.936 82	0.427 79	0.207 76	0.438 93	0.577 82	0.536 94	0.675 85	0.486 96	0.723 100	0.779 80	0.524 93

SD-DETR	0.576 91	0.746 63	0.609 105	0.445 110	0.517 89	0.643 105	0.366 93	0.714 71	0.456 95	0.468 84	0.870 107	0.432 73	0.264 56	0.558 69	0.674 70	0.586 89	0.688 80	0.482 97	0.739 98	0.733 95	0.537 89

SQN_0.1%	0.569 92	0.676 85	0.696 82	0.657 84	0.497 90	0.779 81	0.424 76	0.548 98	0.515 78	0.376 98	0.902 104	0.422 81	0.357 8	0.379 100	0.456 95	0.596 85	0.659 89	0.544 81	0.685 103	0.665 106	0.556 85

TextureNet	0.566 93	0.672 87	0.664 93	0.671 80	0.494 92	0.719 95	0.445 67	0.678 79	0.411 103	0.396 96	0.935 83	0.356 95	0.225 69	0.412 97	0.535 86	0.565 92	0.636 97	0.464 99	0.794 91	0.680 103	0.568 79
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 94	0.648 89	0.700 79	0.770 53	0.586 73	0.687 99	0.333 98	0.650 83	0.514 79	0.475 82	0.906 101	0.359 94	0.223 71	0.340 102	0.442 97	0.422 105	0.668 87	0.501 92	0.708 101	0.779 80	0.534 90

Pointnet++ & Feature	0.557 95	0.735 68	0.661 95	0.686 74	0.491 93	0.744 92	0.392 87	0.539 99	0.451 96	0.375 99	0.946 60	0.376 92	0.205 78	0.403 98	0.356 102	0.553 93	0.643 94	0.497 93	0.824 87	0.756 89	0.515 94

GMLPs	0.538 96	0.495 107	0.693 84	0.647 89	0.471 96	0.793 73	0.300 101	0.477 103	0.505 81	0.358 101	0.903 103	0.327 99	0.081 108	0.472 90	0.529 88	0.448 103	0.710 69	0.509 89	0.746 96	0.737 94	0.554 86

PanopticFusion-label	0.529 97	0.491 108	0.688 88	0.604 97	0.386 103	0.632 106	0.225 111	0.705 75	0.434 100	0.293 107	0.815 109	0.348 97	0.241 65	0.499 85	0.669 71	0.507 96	0.649 91	0.442 105	0.796 90	0.602 110	0.561 82
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 98	0.676 85	0.591 108	0.609 95	0.442 99	0.774 82	0.335 97	0.597 90	0.422 102	0.357 102	0.932 89	0.341 98	0.094 107	0.298 104	0.528 89	0.473 101	0.676 84	0.495 94	0.602 109	0.721 98	0.349 110

Online SegFusion	0.515 99	0.607 97	0.644 99	0.579 100	0.434 100	0.630 107	0.353 95	0.628 88	0.440 98	0.410 94	0.762 113	0.307 101	0.167 94	0.520 78	0.403 100	0.516 95	0.565 101	0.447 103	0.678 104	0.701 100	0.514 95
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	0.501 100	0.558 102	0.608 106	0.424 112	0.478 95	0.690 98	0.246 107	0.586 92	0.468 91	0.450 87	0.911 99	0.394 88	0.160 97	0.438 93	0.212 109	0.432 104	0.541 107	0.475 98	0.742 97	0.727 96	0.477 101

PCNN	0.498 101	0.559 101	0.644 99	0.560 102	0.420 102	0.711 97	0.229 109	0.414 104	0.436 99	0.352 103	0.941 75	0.324 100	0.155 98	0.238 109	0.387 101	0.493 97	0.529 108	0.509 89	0.813 89	0.751 91	0.504 96

Weakly-Openseg v3	0.489 102	0.749 62	0.664 93	0.646 91	0.496 91	0.559 111	0.122 114	0.577 94	0.257 114	0.364 100	0.805 110	0.198 112	0.096 106	0.510 81	0.496 93	0.361 109	0.563 102	0.359 112	0.777 92	0.644 107	0.532 92

3DMV	0.484 103	0.484 109	0.538 110	0.643 92	0.424 101	0.606 110	0.310 99	0.574 95	0.433 101	0.378 97	0.796 111	0.301 102	0.214 75	0.537 76	0.208 110	0.472 102	0.507 111	0.413 108	0.693 102	0.602 110	0.539 88
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 104	0.577 100	0.611 104	0.356 114	0.321 111	0.715 96	0.299 103	0.376 108	0.328 110	0.319 105	0.944 69	0.285 104	0.164 95	0.216 112	0.229 107	0.484 99	0.545 106	0.456 101	0.755 95	0.709 99	0.475 102
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 105	0.679 84	0.604 107	0.578 101	0.380 104	0.682 100	0.291 104	0.106 114	0.483 88	0.258 112	0.920 96	0.258 108	0.025 112	0.231 111	0.325 103	0.480 100	0.560 104	0.463 100	0.725 99	0.666 105	0.231 114
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 106	0.474 110	0.623 102	0.463 108	0.366 106	0.651 103	0.310 99	0.389 107	0.349 108	0.330 104	0.937 80	0.271 106	0.126 103	0.285 105	0.224 108	0.350 111	0.577 100	0.445 104	0.625 107	0.723 97	0.394 106
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	0.442 107	0.548 104	0.548 109	0.597 99	0.363 107	0.628 108	0.300 101	0.292 109	0.374 105	0.307 106	0.881 106	0.268 107	0.186 87	0.238 109	0.204 111	0.407 106	0.506 112	0.449 102	0.667 105	0.620 109	0.462 104

SurfaceConvPF	0.442 107	0.505 106	0.622 103	0.380 113	0.342 109	0.654 102	0.227 110	0.397 106	0.367 106	0.276 109	0.924 93	0.240 109	0.198 83	0.359 101	0.262 105	0.366 107	0.581 99	0.435 106	0.640 106	0.668 104	0.398 105
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 109	0.437 112	0.646 98	0.474 107	0.369 105	0.645 104	0.353 95	0.258 111	0.282 112	0.279 108	0.918 98	0.298 103	0.147 102	0.283 106	0.294 104	0.487 98	0.562 103	0.427 107	0.619 108	0.633 108	0.352 109
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 110	0.525 105	0.647 97	0.522 103	0.324 110	0.488 114	0.077 115	0.712 73	0.353 107	0.401 95	0.636 115	0.281 105	0.176 90	0.340 102	0.565 84	0.175 115	0.551 105	0.398 109	0.370 115	0.602 110	0.361 108

SPLAT Net	0.393 111	0.472 111	0.511 111	0.606 96	0.311 112	0.656 101	0.245 108	0.405 105	0.328 110	0.197 113	0.927 92	0.227 111	0.000 116	0.001 116	0.249 106	0.271 114	0.510 109	0.383 111	0.593 110	0.699 101	0.267 112
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	0.383 112	0.297 114	0.491 112	0.432 111	0.358 108	0.612 109	0.274 105	0.116 113	0.411 103	0.265 110	0.904 102	0.229 110	0.079 109	0.250 107	0.185 112	0.320 112	0.510 109	0.385 110	0.548 111	0.597 113	0.394 106

PointNet++	0.339 113	0.584 99	0.478 113	0.458 109	0.256 114	0.360 115	0.250 106	0.247 112	0.278 113	0.261 111	0.677 114	0.183 113	0.117 104	0.212 113	0.145 114	0.364 108	0.346 115	0.232 115	0.548 111	0.523 114	0.252 113
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNet	0.308 114	0.353 113	0.290 115	0.278 115	0.166 115	0.553 112	0.169 113	0.286 110	0.147 115	0.148 115	0.908 100	0.182 114	0.064 110	0.023 115	0.018 116	0.354 110	0.363 113	0.345 113	0.546 113	0.685 102	0.278 111

ScanNet	0.306 115	0.203 115	0.366 114	0.501 104	0.311 112	0.524 113	0.211 112	0.002 116	0.342 109	0.189 114	0.786 112	0.145 115	0.102 105	0.245 108	0.152 113	0.318 113	0.348 114	0.300 114	0.460 114	0.437 115	0.182 115
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 116	0.000 116	0.041 116	0.172 116	0.030 116	0.062 116	0.001 116	0.035 115	0.004 116	0.051 116	0.143 116	0.019 116	0.003 115	0.041 114	0.050 115	0.003 116	0.054 116	0.018 116	0.005 116	0.264 116	0.082 116

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

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

Spherical Mask(CtoF)	0.812 1	1.000 1	0.973 4	0.852 12	0.718 4	0.917 6	0.574 4	0.677 26	0.748 9	0.729 10	0.715 5	0.795 2	0.809 1	1.000 1	0.831 2	0.854 8	0.787 8	1.000 1	0.638 4

EV3D	0.811 2	1.000 1	0.968 5	0.852 12	0.717 5	0.921 5	0.574 5	0.677 26	0.748 9	0.730 9	0.703 9	0.795 2	0.809 1	1.000 1	0.831 2	0.854 8	0.778 12	1.000 1	0.638 5

SIM3D	0.803 3	1.000 1	0.967 6	0.863 11	0.692 14	0.924 4	0.552 8	0.732 20	0.667 18	0.732 8	0.662 12	0.796 1	0.789 8	1.000 1	0.803 6	0.864 5	0.766 17	1.000 1	0.643 3

OneFormer3D	0.801 4	1.000 1	0.973 3	0.909 5	0.698 11	0.928 2	0.582 3	0.668 31	0.685 15	0.780 2	0.687 10	0.698 15	0.702 13	1.000 1	0.794 8	0.900 2	0.784 10	0.986 48	0.635 6
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
UniPerception	0.800 5	1.000 1	0.930 8	0.872 9	0.727 3	0.862 20	0.454 15	0.764 13	0.820 1	0.746 6	0.706 7	0.750 5	0.772 9	0.926 41	0.764 14	0.818 24	0.826 1	0.997 36	0.660 2

InsSSM	0.799 6	1.000 1	0.915 10	0.710 37	0.729 2	0.925 3	0.664 1	0.670 29	0.770 6	0.766 3	0.739 2	0.737 6	0.700 14	1.000 1	0.792 9	0.829 18	0.815 3	0.997 36	0.625 8

TST3D	0.795 7	1.000 1	0.929 9	0.918 4	0.709 8	0.884 15	0.596 2	0.704 23	0.769 7	0.734 7	0.644 17	0.699 14	0.751 11	1.000 1	0.794 7	0.876 4	0.757 19	0.997 36	0.550 28

MG-Former	0.791 8	1.000 1	0.980 2	0.837 16	0.626 22	0.897 8	0.543 9	0.759 15	0.800 5	0.766 4	0.659 13	0.769 4	0.697 17	1.000 1	0.791 10	0.707 44	0.791 7	1.000 1	0.610 14

ExtMask3D	0.789 9	1.000 1	0.988 1	0.756 30	0.706 9	0.912 7	0.429 16	0.647 36	0.806 4	0.755 5	0.673 11	0.689 16	0.772 10	1.000 1	0.789 11	0.852 10	0.811 4	1.000 1	0.617 11

Queryformer	0.787 10	1.000 1	0.933 7	0.601 46	0.754 1	0.886 13	0.558 7	0.661 33	0.767 8	0.665 15	0.716 4	0.639 21	0.808 4	1.000 1	0.844 1	0.897 3	0.804 5	1.000 1	0.624 9

MAFT	0.786 11	1.000 1	0.894 15	0.807 20	0.694 13	0.893 11	0.486 11	0.674 28	0.740 11	0.786 1	0.704 8	0.727 8	0.739 12	1.000 1	0.707 20	0.849 12	0.756 20	1.000 1	0.685 1

Mask3D	0.780 12	1.000 1	0.786 39	0.716 35	0.696 12	0.885 14	0.500 10	0.714 21	0.810 3	0.672 14	0.715 5	0.679 17	0.809 1	1.000 1	0.831 2	0.833 16	0.787 8	1.000 1	0.602 16
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormer	0.770 13	0.903 52	0.903 12	0.806 21	0.609 28	0.886 12	0.568 6	0.815 6	0.705 14	0.711 11	0.655 14	0.652 20	0.685 20	1.000 1	0.789 12	0.809 25	0.776 14	1.000 1	0.583 21
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++	0.769 14	1.000 1	0.803 32	0.937 1	0.684 15	0.865 17	0.213 31	0.870 2	0.664 19	0.571 21	0.758 1	0.702 12	0.807 5	1.000 1	0.653 27	0.902 1	0.792 6	1.000 1	0.626 7

SoftGroup	0.761 15	1.000 1	0.808 28	0.845 14	0.716 6	0.862 19	0.243 28	0.824 4	0.655 21	0.620 16	0.734 3	0.699 13	0.791 7	0.981 35	0.716 18	0.844 13	0.769 15	1.000 1	0.594 19
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNet	0.757 16	1.000 1	0.904 11	0.731 33	0.678 16	0.895 9	0.458 13	0.644 38	0.670 17	0.710 12	0.620 22	0.732 7	0.650 22	1.000 1	0.756 15	0.778 28	0.779 11	1.000 1	0.614 12
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
TD3D	0.751 17	1.000 1	0.774 40	0.867 10	0.621 24	0.934 1	0.404 17	0.706 22	0.812 2	0.605 19	0.633 20	0.626 22	0.690 19	1.000 1	0.640 29	0.820 21	0.777 13	1.000 1	0.612 13
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNet	0.747 18	1.000 1	0.818 24	0.837 17	0.713 7	0.844 22	0.457 14	0.647 36	0.711 13	0.614 17	0.617 24	0.657 19	0.650 22	1.000 1	0.692 21	0.822 20	0.765 18	1.000 1	0.595 18
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut	0.732 19	1.000 1	0.788 37	0.724 34	0.642 21	0.859 21	0.248 27	0.787 11	0.618 24	0.596 20	0.653 16	0.722 10	0.583 43	1.000 1	0.766 13	0.861 6	0.825 2	1.000 1	0.504 34

IPCA-Inst	0.731 20	1.000 1	0.788 38	0.884 8	0.698 10	0.788 38	0.252 26	0.760 14	0.646 22	0.511 29	0.637 19	0.665 18	0.804 6	1.000 1	0.644 28	0.778 29	0.747 22	1.000 1	0.561 25

TopoSeg	0.725 21	1.000 1	0.806 31	0.933 2	0.668 18	0.758 42	0.272 25	0.734 19	0.630 23	0.549 25	0.654 15	0.606 23	0.697 18	0.966 38	0.612 33	0.839 14	0.754 21	1.000 1	0.573 22

DKNet	0.718 22	1.000 1	0.814 25	0.782 24	0.619 25	0.872 16	0.224 29	0.751 17	0.569 28	0.677 13	0.585 28	0.724 9	0.633 33	0.981 35	0.515 43	0.819 22	0.736 23	1.000 1	0.617 10
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC	0.707 23	1.000 1	0.850 17	0.924 3	0.648 19	0.747 45	0.162 33	0.862 3	0.572 27	0.520 27	0.624 21	0.549 26	0.649 31	1.000 1	0.560 38	0.706 45	0.768 16	1.000 1	0.591 20

HAIS	0.699 24	1.000 1	0.849 18	0.820 18	0.675 17	0.808 32	0.279 23	0.757 16	0.465 34	0.517 28	0.596 26	0.559 25	0.600 37	1.000 1	0.654 26	0.767 31	0.676 27	0.994 44	0.560 26
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet	0.698 25	1.000 1	0.697 56	0.888 7	0.556 35	0.803 33	0.387 18	0.626 40	0.417 39	0.556 24	0.585 29	0.702 11	0.600 37	1.000 1	0.824 5	0.720 43	0.692 25	1.000 1	0.509 33
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup	0.694 26	1.000 1	0.799 34	0.811 19	0.622 23	0.817 27	0.376 19	0.805 9	0.590 26	0.487 33	0.568 32	0.525 30	0.650 22	0.835 51	0.600 34	0.829 17	0.655 30	1.000 1	0.526 30

DANCENET	0.680 27	1.000 1	0.807 29	0.733 32	0.600 29	0.768 41	0.375 20	0.543 48	0.538 29	0.610 18	0.599 25	0.498 31	0.632 35	0.981 35	0.739 17	0.856 7	0.633 36	0.882 59	0.454 43

SphereSeg	0.680 27	1.000 1	0.856 16	0.744 31	0.618 26	0.893 10	0.151 34	0.651 35	0.713 12	0.537 26	0.579 31	0.430 40	0.651 21	1.000 1	0.389 54	0.744 38	0.697 24	0.991 46	0.601 17

Box2Mask	0.677 29	1.000 1	0.847 19	0.771 26	0.509 44	0.816 28	0.277 24	0.558 47	0.482 31	0.562 23	0.640 18	0.448 36	0.700 14	1.000 1	0.666 22	0.852 11	0.578 43	0.997 36	0.488 38
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance	0.672 30	1.000 1	0.758 48	0.682 39	0.576 33	0.842 23	0.477 12	0.504 54	0.524 30	0.567 22	0.585 30	0.451 35	0.557 45	1.000 1	0.751 16	0.797 26	0.563 46	1.000 1	0.467 42
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group	0.664 31	1.000 1	0.822 23	0.764 29	0.616 27	0.815 29	0.139 38	0.694 25	0.597 25	0.459 37	0.566 33	0.599 24	0.600 37	0.516 61	0.715 19	0.819 23	0.635 34	1.000 1	0.603 15
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance	0.657 32	1.000 1	0.760 46	0.667 41	0.581 31	0.863 18	0.323 21	0.655 34	0.477 32	0.473 35	0.549 35	0.432 39	0.650 22	1.000 1	0.655 25	0.738 39	0.585 42	0.944 51	0.472 41

CSC-Pretrained	0.648 33	1.000 1	0.810 26	0.768 27	0.523 42	0.813 30	0.143 37	0.819 5	0.389 42	0.422 46	0.511 39	0.443 37	0.650 22	1.000 1	0.624 31	0.732 40	0.634 35	1.000 1	0.375 50

PE	0.645 34	1.000 1	0.773 42	0.798 23	0.538 37	0.786 39	0.088 46	0.799 10	0.350 46	0.435 44	0.547 36	0.545 27	0.646 32	0.933 40	0.562 37	0.761 34	0.556 51	0.997 36	0.501 36
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 35	1.000 1	0.758 47	0.582 52	0.539 36	0.826 26	0.046 51	0.765 12	0.372 44	0.436 43	0.588 27	0.539 29	0.650 22	1.000 1	0.577 35	0.750 36	0.653 32	0.997 36	0.495 37
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3D	0.641 36	1.000 1	0.841 20	0.893 6	0.531 39	0.802 34	0.115 43	0.588 45	0.448 36	0.438 41	0.537 38	0.430 41	0.550 46	0.857 43	0.534 41	0.764 33	0.657 29	0.987 47	0.568 23
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN	0.638 37	1.000 1	0.895 14	0.800 22	0.480 48	0.676 50	0.144 36	0.737 18	0.354 45	0.447 38	0.400 52	0.365 47	0.700 14	1.000 1	0.569 36	0.836 15	0.599 38	1.000 1	0.473 40

PointGroup	0.636 38	1.000 1	0.765 43	0.624 43	0.505 46	0.797 35	0.116 42	0.696 24	0.384 43	0.441 39	0.559 34	0.476 33	0.596 40	1.000 1	0.666 22	0.756 35	0.556 50	0.997 36	0.513 32
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group	0.635 39	0.667 54	0.797 36	0.714 36	0.562 34	0.774 40	0.146 35	0.810 8	0.429 38	0.476 34	0.546 37	0.399 43	0.633 33	1.000 1	0.632 30	0.722 42	0.609 37	1.000 1	0.514 31
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
Mask3D_evaluation	0.631 40	1.000 1	0.829 22	0.606 45	0.646 20	0.836 24	0.068 47	0.511 52	0.462 35	0.507 30	0.619 23	0.389 45	0.610 36	1.000 1	0.432 49	0.828 19	0.673 28	0.788 63	0.552 27

DENet	0.629 41	1.000 1	0.797 35	0.608 44	0.589 30	0.627 54	0.219 30	0.882 1	0.310 48	0.402 51	0.383 54	0.396 44	0.650 22	1.000 1	0.663 24	0.543 62	0.691 26	1.000 1	0.568 24

3D-MPA	0.611 42	1.000 1	0.833 21	0.765 28	0.526 41	0.756 43	0.136 40	0.588 45	0.470 33	0.438 42	0.432 48	0.358 49	0.650 22	0.857 43	0.429 50	0.765 32	0.557 49	1.000 1	0.430 45
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS	0.605 43	1.000 1	0.801 33	0.599 47	0.535 38	0.728 47	0.286 22	0.436 58	0.679 16	0.491 31	0.433 46	0.256 51	0.404 58	0.857 43	0.620 32	0.724 41	0.510 56	1.000 1	0.539 29

AOIA	0.601 44	1.000 1	0.761 45	0.687 38	0.485 47	0.828 25	0.008 58	0.663 32	0.405 41	0.405 50	0.425 49	0.490 32	0.596 40	0.714 54	0.553 40	0.779 27	0.597 39	0.992 45	0.424 47

PCJC	0.578 45	1.000 1	0.810 27	0.583 51	0.449 51	0.813 31	0.042 52	0.603 43	0.341 47	0.490 32	0.465 43	0.410 42	0.650 22	0.835 51	0.264 60	0.694 49	0.561 47	0.889 56	0.504 35

SSEN	0.575 46	1.000 1	0.761 44	0.473 54	0.477 49	0.795 36	0.066 48	0.529 50	0.658 20	0.460 36	0.461 44	0.380 46	0.331 60	0.859 42	0.401 53	0.692 51	0.653 31	1.000 1	0.348 52
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg	0.567 47	0.528 64	0.708 55	0.626 42	0.580 32	0.745 46	0.063 49	0.627 39	0.240 52	0.400 52	0.497 40	0.464 34	0.515 47	1.000 1	0.475 45	0.745 37	0.571 44	1.000 1	0.429 46

NeuralBF	0.555 48	0.667 54	0.896 13	0.843 15	0.517 43	0.751 44	0.029 53	0.519 51	0.414 40	0.439 40	0.465 42	0.000 70	0.484 49	0.857 43	0.287 58	0.693 50	0.651 33	1.000 1	0.485 39
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML	0.549 49	1.000 1	0.807 30	0.588 50	0.327 56	0.647 52	0.004 60	0.815 7	0.180 55	0.418 47	0.364 56	0.182 54	0.445 52	1.000 1	0.442 48	0.688 52	0.571 45	1.000 1	0.396 48
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance	0.539 50	1.000 1	0.621 59	0.300 57	0.530 40	0.698 48	0.127 41	0.533 49	0.222 53	0.430 45	0.400 51	0.365 47	0.574 44	0.938 39	0.472 46	0.659 54	0.543 52	0.944 51	0.347 53

One_Thing_One_Click	0.529 51	0.667 54	0.718 51	0.777 25	0.399 52	0.683 49	0.000 63	0.669 30	0.138 58	0.391 53	0.374 55	0.539 28	0.360 59	0.641 58	0.556 39	0.774 30	0.593 40	0.997 36	0.251 58
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN	0.515 52	1.000 1	0.538 64	0.282 58	0.468 50	0.790 37	0.173 32	0.345 60	0.429 37	0.413 49	0.484 41	0.176 55	0.595 42	0.591 59	0.522 42	0.668 53	0.476 57	0.986 49	0.327 54

Occipital-SCS	0.512 53	1.000 1	0.716 52	0.509 53	0.506 45	0.611 55	0.092 45	0.602 44	0.177 56	0.346 56	0.383 53	0.165 56	0.442 53	0.850 50	0.386 55	0.618 58	0.543 53	0.889 56	0.389 49

3D-BoNet	0.488 54	1.000 1	0.672 58	0.590 49	0.301 58	0.484 65	0.098 44	0.620 41	0.306 49	0.341 57	0.259 60	0.125 58	0.434 55	0.796 53	0.402 52	0.499 64	0.513 55	0.909 55	0.439 44
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst	0.478 55	0.667 54	0.712 54	0.595 48	0.259 61	0.550 61	0.000 63	0.613 42	0.175 57	0.250 62	0.434 45	0.437 38	0.411 57	0.857 43	0.485 44	0.591 61	0.267 67	0.944 51	0.359 51
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS	0.470 56	0.667 54	0.685 57	0.677 40	0.372 54	0.562 59	0.000 63	0.482 55	0.244 51	0.316 59	0.298 57	0.052 65	0.442 54	0.857 43	0.267 59	0.702 46	0.559 48	1.000 1	0.287 56

SALoss-ResNet	0.459 57	1.000 1	0.737 50	0.159 68	0.259 60	0.587 57	0.138 39	0.475 56	0.217 54	0.416 48	0.408 50	0.128 57	0.315 61	0.714 54	0.411 51	0.536 63	0.590 41	0.873 60	0.304 55
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.447 58	0.528 64	0.555 62	0.381 55	0.382 53	0.633 53	0.002 61	0.509 53	0.260 50	0.361 55	0.432 47	0.327 50	0.451 51	0.571 60	0.367 56	0.639 56	0.386 58	0.980 50	0.276 57
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_ins	0.445 59	0.667 54	0.773 41	0.185 65	0.317 57	0.656 51	0.000 63	0.407 59	0.134 59	0.381 54	0.267 59	0.217 53	0.476 50	0.714 54	0.452 47	0.629 57	0.514 54	1.000 1	0.222 61
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SIS	0.382 60	1.000 1	0.432 67	0.245 60	0.190 62	0.577 58	0.013 57	0.263 62	0.033 65	0.320 58	0.240 61	0.075 61	0.422 56	0.857 43	0.117 65	0.699 47	0.271 66	0.883 58	0.235 60
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.323 61	0.667 54	0.542 63	0.264 59	0.157 65	0.550 60	0.000 63	0.205 65	0.009 67	0.270 61	0.218 62	0.075 61	0.500 48	0.688 57	0.007 71	0.698 48	0.301 63	0.459 68	0.200 62
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone	0.319 62	0.667 54	0.715 53	0.233 61	0.189 63	0.479 66	0.008 58	0.218 63	0.067 64	0.201 64	0.173 63	0.107 59	0.123 66	0.438 62	0.150 62	0.615 59	0.355 59	0.916 54	0.093 70

R-PointNet	0.306 63	0.500 66	0.405 68	0.311 56	0.348 55	0.589 56	0.054 50	0.068 68	0.126 60	0.283 60	0.290 58	0.028 66	0.219 64	0.214 65	0.331 57	0.396 68	0.275 64	0.821 62	0.245 59

Region-18class	0.284 64	0.250 70	0.751 49	0.228 63	0.270 59	0.521 62	0.000 63	0.468 57	0.008 69	0.205 63	0.127 64	0.000 70	0.068 68	0.070 69	0.262 61	0.652 55	0.323 61	0.740 64	0.173 63

SemRegionNet-20cls	0.250 65	0.333 67	0.613 60	0.229 62	0.163 64	0.493 63	0.000 63	0.304 61	0.107 61	0.147 67	0.100 66	0.052 64	0.231 62	0.119 67	0.039 67	0.445 66	0.325 60	0.654 65	0.141 66

3D-BEVIS	0.248 66	0.667 54	0.566 61	0.076 69	0.035 71	0.394 69	0.027 55	0.035 70	0.098 62	0.099 69	0.030 70	0.025 67	0.098 67	0.375 64	0.126 64	0.604 60	0.181 69	0.854 61	0.171 64
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
tmp	0.248 66	0.667 54	0.437 66	0.188 64	0.153 66	0.491 64	0.000 63	0.208 64	0.094 63	0.153 66	0.099 67	0.057 63	0.217 65	0.119 67	0.039 67	0.466 65	0.302 62	0.640 66	0.140 67

Sem_Recon_ins	0.227 68	0.764 53	0.486 65	0.069 70	0.098 68	0.426 68	0.017 56	0.067 69	0.015 66	0.172 65	0.100 65	0.096 60	0.054 70	0.183 66	0.135 63	0.366 69	0.260 68	0.614 67	0.168 65

ASIS	0.199 69	0.333 67	0.253 70	0.167 67	0.140 67	0.438 67	0.000 63	0.177 66	0.008 68	0.121 68	0.069 68	0.004 69	0.231 63	0.429 63	0.036 69	0.445 67	0.273 65	0.333 70	0.119 69

Sgpn_scannet	0.143 70	0.208 71	0.390 69	0.169 66	0.065 69	0.275 70	0.029 54	0.069 67	0.000 70	0.087 70	0.043 69	0.014 68	0.027 71	0.000 70	0.112 66	0.351 70	0.168 70	0.438 69	0.138 68

MaskRCNN 2d->3d Proj	0.058 71	0.333 67	0.002 71	0.000 71	0.053 70	0.002 71	0.002 62	0.021 71	0.000 70	0.045 71	0.024 71	0.238 52	0.065 69	0.000 70	0.014 70	0.107 71	0.020 71	0.110 71	0.006 71

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

EMSANet (Instance)	0.380 1	0.549 3	0.651 1	0.147 1	0.397 3	0.399 1	0.167 2	0.437 3	0.319 2	0.210 1	0.301 1	0.235 1	0.463 2	0.245 2	0.372 3	0.511 1	0.296 2	0.876 1	0.268 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
FKNet	0.368 2	0.588 1	0.618 2	0.099 3	0.466 1	0.395 2	0.108 3	0.548 1	0.157 3	0.175 2	0.268 2	0.096 4	0.439 3	0.343 1	0.420 2	0.500 3	0.317 1	0.855 3	0.234 2

UniDet_RVC	0.358 3	0.554 2	0.543 3	0.128 2	0.402 2	0.381 3	0.200 1	0.461 2	0.328 1	0.138 3	0.232 3	0.148 3	0.466 1	0.109 3	0.538 1	0.506 2	0.294 3	0.862 2	0.159 3

MaskRCNN_ScanNet	0.227 4	0.228 4	0.381 4	0.013 4	0.237 4	0.339 4	0.089 4	0.339 4	0.150 4	0.134 4	0.143 4	0.179 2	0.255 4	0.053 4	0.331 4	0.244 4	0.154 4	0.687 4	0.127 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