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

Minkowski 34D	0.253 12	0.463 12	0.154 13	0.102 12	0.771 12	0.650 12	0.932 11	0.483 12	0.571 12	0.710 11	0.331 12	0.250 12	0.492 10	0.044 4	0.703 12	0.419 13	0.606 13	0.227 12	0.621 12	0.865 13	0.531 7	0.771 13	0.813 10	0.291 7	0.484 11	0.242 12	0.612 13	0.282 13	0.440 13	0.351 11	0.299 11	0.622 12	0.593 9	0.027 10	0.293 9	0.310 13	0.000 1	0.757 10	0.858 11	0.737 11	0.150 6	0.164 1	0.368 13	0.084 6	0.381 13	0.142 13	0.357 11	0.720 8	0.214 10	0.092 12	0.724 12	0.596 13	0.056 10	0.655 6	0.525 10	0.581 13	0.352 13	0.594 12	0.056 13	0.000 3	0.014 13	0.224 12	0.772 11	0.205 13	0.720 12	0.000 3	0.159 5	0.531 12	0.163 13	0.294 12	0.136 13	0.000 1	0.169 12	0.589 11	0.000 4	0.000 7	0.000 1	0.002 2	0.663 6	0.466 13	0.265 13	0.582 8	0.337 9	0.016 12	0.559 11	0.084 13	0.000 3	0.000 6	0.000 1	0.036 4	0.000 3	0.125 3	0.670 11	0.000 1	0.102 1	0.071 7	0.164 11	0.406 6	0.386 5	0.046 12	0.068 13	0.159 11	0.117 4	0.284 12	0.111 12	0.094 12	0.000 3	0.000 13	0.197 13	0.000 1	0.044 11	0.013 11	0.002 10	0.228 13	0.307 13	0.588 8	0.025 13	0.545 3	0.134 11	0.000 1	0.655 4	0.302 11	0.282 13	0.000 1	0.060 2	0.000 8	0.035 13	0.000 5	0.000 8	0.097 13	0.000 8	0.000 5	0.005 8	0.000 1	0.000 2	0.096 13	0.000 1	0.334 12	0.000 1	0.000 9	0.274 12	0.000 1	0.513 12	0.000 1	0.000 1	0.280 7	0.194 5	0.897 8	0.000 6	0.000 7	0.000 1	0.000 5	0.000 1	0.108 8	0.279 13	0.189 12	0.141 13	0.059 12	0.272 2	0.307 13	0.445 6	0.003 7	0.000 1	0.353 12	0.000 2	0.026 1	0.000 8	0.581 11	0.001 1	0.000 1	0.000 1	0.093 13	0.002 4	0.000 3	0.000 7	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
PTv3 ScanNet200	0.393 2	0.592 2	0.330 1	0.216 2	0.851 2	0.687 5	0.971 2	0.586 1	0.755 1	0.752 6	0.505 1	0.404 5	0.575 3	0.000 10	0.848 1	0.616 3	0.761 2	0.349 1	0.738 2	0.978 2	0.546 5	0.860 7	0.926 2	0.346 2	0.654 3	0.384 5	0.828 1	0.523 3	0.699 2	0.583 4	0.387 6	0.822 2	0.688 2	0.118 4	0.474 2	0.603 4	0.000 1	0.832 4	0.903 2	0.753 8	0.140 7	0.000 7	0.650 3	0.109 3	0.520 2	0.457 1	0.497 8	0.871 3	0.281 2	0.192 3	0.887 3	0.748 2	0.168 1	0.727 4	0.733 2	0.740 1	0.644 1	0.714 4	0.190 9	0.000 3	0.256 3	0.449 7	0.914 1	0.514 2	0.759 11	0.337 1	0.172 4	0.692 5	0.617 1	0.636 1	0.325 4	0.000 1	0.641 1	0.782 1	0.000 4	0.065 3	0.000 1	0.000 4	0.842 2	0.903 1	0.661 3	0.662 3	0.612 1	0.405 2	0.731 1	0.566 2	0.000 3	0.000 6	0.000 1	0.017 11	0.301 1	0.088 5	0.941 2	0.000 1	0.077 2	0.000 9	0.717 4	0.790 1	0.310 10	0.026 13	0.264 3	0.349 1	0.220 3	0.397 9	0.366 1	0.115 9	0.000 3	0.337 1	0.463 6	0.000 1	0.531 2	0.218 2	0.593 1	0.455 2	0.469 1	0.708 3	0.210 2	0.592 2	0.108 12	0.000 1	0.728 1	0.682 2	0.671 5	0.000 1	0.000 8	0.407 1	0.136 2	0.022 2	0.575 1	0.436 4	0.259 3	0.428 1	0.048 4	0.000 1	0.000 2	0.879 5	0.000 1	0.480 2	0.000 1	0.133 6	0.597 1	0.000 1	0.690 2	0.000 1	0.000 1	0.009 12	0.000 11	0.921 3	0.000 6	0.151 3	0.000 1	0.000 5	0.000 1	0.109 7	0.494 10	0.622 2	0.394 7	0.073 10	0.141 7	0.798 1	0.528 4	0.026 2	0.000 1	0.551 4	0.000 2	0.000 2	0.134 6	0.717 6	0.000 2	0.000 1	0.000 1	0.188 3	0.000 5	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)
CeCo	0.340 5	0.551 7	0.247 9	0.181 4	0.784 9	0.661 10	0.939 9	0.564 4	0.624 9	0.721 8	0.484 3	0.429 3	0.575 3	0.027 6	0.774 8	0.503 10	0.753 4	0.242 9	0.656 9	0.945 6	0.534 6	0.865 6	0.860 7	0.177 13	0.616 6	0.400 3	0.818 2	0.579 1	0.615 7	0.367 10	0.408 5	0.726 11	0.633 3	0.162 1	0.360 5	0.619 2	0.000 1	0.828 5	0.873 8	0.924 2	0.109 9	0.083 3	0.564 4	0.057 13	0.475 9	0.266 7	0.781 1	0.767 6	0.257 5	0.100 9	0.825 7	0.663 8	0.048 11	0.620 10	0.551 8	0.595 11	0.532 6	0.692 7	0.246 4	0.000 3	0.213 5	0.615 1	0.861 5	0.376 6	0.900 4	0.000 3	0.102 12	0.660 6	0.321 11	0.547 4	0.226 9	0.000 1	0.311 9	0.742 2	0.011 3	0.006 6	0.000 1	0.000 4	0.546 11	0.824 7	0.345 10	0.665 2	0.450 5	0.435 1	0.683 4	0.411 6	0.338 1	0.000 6	0.000 1	0.030 6	0.000 3	0.068 7	0.892 6	0.000 1	0.063 3	0.000 9	0.257 9	0.304 10	0.387 4	0.079 10	0.228 4	0.190 8	0.000 12	0.586 1	0.347 3	0.133 6	0.000 3	0.037 9	0.377 9	0.000 1	0.384 5	0.006 12	0.003 9	0.421 3	0.410 9	0.643 5	0.171 5	0.121 5	0.142 10	0.000 1	0.510 9	0.447 8	0.474 10	0.000 1	0.000 8	0.286 3	0.083 9	0.000 5	0.000 8	0.603 1	0.096 5	0.063 4	0.000 9	0.000 1	0.000 2	0.898 3	0.000 1	0.429 5	0.000 1	0.400 1	0.550 3	0.000 1	0.633 5	0.000 1	0.000 1	0.377 4	0.000 11	0.916 4	0.000 6	0.000 7	0.000 1	0.000 5	0.000 1	0.102 10	0.499 8	0.296 10	0.463 4	0.089 5	0.304 1	0.740 2	0.401 12	0.010 4	0.000 1	0.560 3	0.000 2	0.000 2	0.709 2	0.652 8	0.000 2	0.000 1	0.000 1	0.143 7	0.000 5	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
OA-CNN-L_ScanNet200	0.333 7	0.558 3	0.269 7	0.124 9	0.821 4	0.703 2	0.946 5	0.569 3	0.662 3	0.748 7	0.487 2	0.455 2	0.572 5	0.000 10	0.789 6	0.534 7	0.736 7	0.271 4	0.713 3	0.949 5	0.498 12	0.877 3	0.860 7	0.332 5	0.706 1	0.474 1	0.788 6	0.406 9	0.637 4	0.495 7	0.355 8	0.805 4	0.592 10	0.015 12	0.396 4	0.602 5	0.000 1	0.799 7	0.876 6	0.713 12	0.276 2	0.000 7	0.493 9	0.080 7	0.448 11	0.363 3	0.661 3	0.833 5	0.262 4	0.125 5	0.823 8	0.665 7	0.076 7	0.720 5	0.557 7	0.637 7	0.517 7	0.672 9	0.227 6	0.000 3	0.158 9	0.496 5	0.843 9	0.352 8	0.835 9	0.000 3	0.103 11	0.711 4	0.527 2	0.526 5	0.320 5	0.000 1	0.568 5	0.625 8	0.067 1	0.000 7	0.000 1	0.001 3	0.806 4	0.836 6	0.621 8	0.591 6	0.373 7	0.314 5	0.668 6	0.398 7	0.003 2	0.000 6	0.000 1	0.016 12	0.024 2	0.043 11	0.906 5	0.000 1	0.052 4	0.000 9	0.384 8	0.330 9	0.342 6	0.100 8	0.223 5	0.183 9	0.112 5	0.476 5	0.313 6	0.130 8	0.196 2	0.112 8	0.370 10	0.000 1	0.234 8	0.071 7	0.160 5	0.403 4	0.398 10	0.492 11	0.197 3	0.076 9	0.272 3	0.000 1	0.200 13	0.560 7	0.735 4	0.000 1	0.000 8	0.000 8	0.110 6	0.002 4	0.021 7	0.412 5	0.000 8	0.000 5	0.000 9	0.000 1	0.000 2	0.794 8	0.000 1	0.445 4	0.000 1	0.022 7	0.509 6	0.000 1	0.517 11	0.000 1	0.000 1	0.001 13	0.245 3	0.915 5	0.024 3	0.089 4	0.000 1	0.262 2	0.000 1	0.103 9	0.524 6	0.392 9	0.515 2	0.013 13	0.251 4	0.411 11	0.662 2	0.001 8	0.000 1	0.473 9	0.000 2	0.000 2	0.150 5	0.699 7	0.000 2	0.000 1	0.000 1	0.166 5	0.000 5	0.024 2	0.000 7	0.000 1

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

OctFormer ScanNet200	0.326 9	0.539 8	0.265 8	0.131 8	0.806 7	0.670 9	0.943 8	0.535 8	0.662 3	0.705 12	0.423 7	0.407 4	0.505 9	0.003 8	0.765 9	0.582 6	0.686 11	0.227 12	0.680 6	0.943 7	0.601 2	0.854 9	0.892 4	0.335 4	0.417 13	0.357 8	0.724 8	0.453 7	0.632 5	0.596 3	0.432 3	0.783 7	0.512 12	0.021 11	0.244 11	0.637 1	0.000 1	0.787 8	0.873 8	0.743 10	0.000 13	0.000 7	0.534 7	0.110 2	0.499 5	0.289 6	0.626 5	0.620 10	0.168 13	0.204 2	0.849 6	0.679 6	0.117 3	0.633 8	0.684 3	0.650 6	0.552 4	0.684 8	0.312 2	0.000 3	0.175 8	0.429 8	0.865 3	0.413 3	0.837 8	0.000 3	0.145 6	0.626 7	0.451 6	0.487 8	0.513 1	0.000 1	0.529 6	0.613 9	0.000 4	0.033 4	0.000 1	0.000 4	0.828 3	0.871 2	0.622 7	0.587 7	0.411 6	0.137 10	0.645 10	0.343 8	0.000 3	0.000 6	0.000 1	0.022 9	0.000 3	0.026 13	0.829 9	0.000 1	0.022 6	0.089 5	0.842 2	0.253 11	0.318 9	0.296 2	0.178 7	0.291 3	0.224 2	0.584 2	0.200 10	0.132 7	0.000 3	0.128 7	0.227 12	0.000 1	0.230 9	0.047 9	0.149 6	0.331 8	0.412 8	0.618 6	0.164 6	0.102 7	0.522 1	0.000 1	0.655 4	0.378 9	0.469 11	0.000 1	0.000 8	0.000 8	0.105 7	0.000 5	0.000 8	0.483 3	0.000 8	0.000 5	0.028 6	0.000 1	0.000 2	0.906 1	0.000 1	0.339 11	0.000 1	0.000 9	0.457 8	0.000 1	0.612 6	0.000 1	0.000 1	0.408 3	0.000 11	0.900 7	0.000 6	0.000 7	0.000 1	0.029 4	0.000 1	0.074 13	0.455 11	0.479 4	0.427 5	0.079 8	0.140 8	0.496 7	0.414 10	0.022 3	0.000 1	0.471 10	0.000 2	0.000 2	0.000 8	0.722 5	0.000 2	0.000 1	0.000 1	0.138 10	0.000 5	0.000 3	0.000 7	0.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
AWCS	0.305 10	0.508 10	0.225 10	0.142 7	0.782 10	0.634 13	0.937 10	0.489 11	0.578 10	0.721 8	0.364 11	0.355 9	0.515 8	0.023 7	0.764 10	0.523 9	0.707 10	0.264 7	0.633 10	0.922 10	0.507 11	0.886 1	0.804 11	0.179 11	0.436 12	0.300 10	0.656 12	0.529 2	0.501 11	0.394 9	0.296 12	0.820 3	0.603 7	0.131 3	0.179 13	0.619 2	0.000 1	0.707 12	0.865 10	0.773 6	0.171 5	0.010 6	0.484 10	0.063 11	0.463 10	0.254 9	0.332 12	0.649 9	0.220 9	0.100 9	0.729 11	0.613 11	0.071 9	0.582 11	0.628 6	0.702 3	0.424 11	0.749 2	0.137 11	0.000 3	0.142 10	0.360 10	0.863 4	0.305 10	0.877 6	0.000 3	0.173 3	0.606 10	0.337 10	0.478 9	0.154 11	0.000 1	0.253 10	0.664 5	0.000 4	0.000 7	0.000 1	0.000 4	0.626 9	0.782 9	0.302 12	0.602 5	0.185 11	0.282 6	0.651 9	0.317 9	0.000 3	0.000 6	0.000 1	0.022 9	0.000 3	0.154 1	0.876 7	0.000 1	0.014 7	0.063 8	0.029 13	0.553 4	0.467 2	0.084 9	0.124 10	0.157 12	0.049 10	0.373 10	0.252 8	0.097 11	0.000 3	0.219 4	0.542 3	0.000 1	0.392 4	0.172 6	0.000 11	0.339 7	0.417 7	0.533 10	0.093 11	0.115 6	0.195 7	0.000 1	0.516 8	0.288 12	0.741 3	0.000 1	0.001 7	0.233 5	0.056 10	0.000 5	0.159 5	0.334 7	0.077 7	0.000 5	0.000 9	0.000 1	0.000 2	0.749 10	0.000 1	0.411 6	0.000 1	0.008 8	0.452 9	0.000 1	0.595 8	0.000 1	0.000 1	0.220 8	0.006 8	0.894 9	0.006 5	0.000 7	0.000 1	0.000 5	0.000 1	0.112 5	0.504 7	0.404 8	0.551 1	0.093 4	0.129 13	0.484 8	0.381 13	0.000 9	0.000 1	0.396 11	0.000 2	0.000 2	0.620 3	0.402 13	0.000 2	0.000 1	0.000 1	0.142 8	0.000 5	0.000 3	0.512 5	0.000 1

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

PonderV2 ScanNet200	0.346 4	0.552 6	0.270 6	0.175 5	0.810 6	0.682 6	0.950 4	0.560 5	0.641 8	0.761 2	0.398 9	0.357 8	0.570 6	0.113 2	0.804 4	0.603 5	0.750 5	0.283 3	0.681 5	0.952 4	0.548 4	0.874 4	0.852 9	0.290 8	0.700 2	0.356 9	0.792 4	0.445 8	0.545 9	0.436 8	0.351 9	0.787 6	0.611 6	0.050 7	0.290 10	0.519 9	0.000 1	0.825 6	0.888 4	0.842 3	0.259 3	0.100 2	0.558 5	0.070 10	0.497 6	0.247 10	0.457 9	0.889 2	0.248 7	0.106 8	0.817 9	0.691 5	0.094 5	0.729 3	0.636 5	0.620 10	0.503 9	0.660 10	0.243 5	0.000 3	0.212 6	0.590 4	0.860 6	0.400 4	0.881 5	0.000 3	0.202 2	0.622 8	0.408 7	0.499 7	0.261 7	0.000 1	0.385 7	0.636 7	0.000 4	0.000 7	0.000 1	0.000 4	0.433 12	0.843 5	0.660 5	0.574 10	0.481 3	0.336 4	0.677 5	0.486 4	0.000 3	0.030 2	0.000 1	0.034 5	0.000 3	0.080 6	0.869 8	0.000 1	0.000 8	0.000 9	0.540 6	0.727 2	0.232 13	0.115 7	0.186 6	0.193 7	0.000 12	0.403 8	0.326 5	0.103 10	0.000 3	0.290 3	0.392 8	0.000 1	0.346 6	0.062 8	0.424 3	0.375 5	0.431 5	0.667 4	0.115 10	0.082 8	0.239 5	0.000 1	0.504 10	0.606 5	0.584 8	0.000 1	0.002 6	0.186 6	0.104 8	0.000 5	0.394 4	0.384 6	0.083 6	0.000 5	0.007 7	0.000 1	0.000 2	0.880 4	0.000 1	0.377 8	0.000 1	0.263 3	0.565 2	0.000 1	0.608 7	0.000 1	0.000 1	0.304 6	0.009 7	0.924 2	0.000 6	0.000 7	0.000 1	0.000 5	0.000 1	0.128 3	0.584 2	0.475 5	0.412 6	0.076 9	0.269 3	0.621 5	0.509 5	0.010 4	0.000 1	0.491 8	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 6	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.
L3DETR-ScanNet_200	0.336 6	0.533 9	0.279 4	0.155 6	0.801 8	0.689 3	0.946 5	0.539 7	0.660 6	0.759 3	0.380 10	0.333 10	0.583 2	0.000 10	0.788 7	0.529 8	0.740 6	0.261 8	0.679 7	0.940 9	0.525 9	0.860 7	0.883 5	0.226 9	0.613 7	0.397 4	0.720 9	0.512 4	0.565 8	0.620 2	0.417 4	0.775 9	0.629 4	0.158 2	0.298 8	0.579 8	0.000 1	0.835 2	0.883 5	0.927 1	0.114 8	0.079 4	0.511 8	0.073 9	0.508 4	0.312 4	0.629 4	0.861 4	0.192 12	0.098 11	0.908 2	0.636 9	0.032 13	0.563 13	0.514 11	0.664 4	0.505 8	0.697 6	0.225 7	0.000 3	0.264 2	0.411 9	0.860 6	0.321 9	0.960 1	0.058 2	0.109 10	0.776 2	0.526 3	0.557 2	0.303 6	0.000 1	0.339 8	0.712 4	0.000 4	0.014 5	0.000 1	0.000 4	0.638 8	0.856 3	0.641 6	0.579 9	0.107 13	0.119 11	0.661 7	0.416 5	0.000 3	0.000 6	0.000 1	0.007 13	0.000 3	0.067 8	0.910 4	0.000 1	0.000 8	0.000 9	0.463 7	0.448 5	0.294 12	0.324 1	0.293 2	0.211 6	0.108 6	0.448 7	0.068 13	0.141 5	0.000 3	0.330 2	0.699 1	0.000 1	0.256 7	0.192 4	0.000 11	0.355 6	0.418 6	0.209 13	0.146 8	0.679 1	0.101 13	0.000 1	0.503 11	0.687 1	0.671 5	0.000 1	0.000 8	0.174 7	0.117 4	0.000 5	0.122 6	0.515 2	0.104 4	0.259 2	0.312 3	0.000 1	0.000 2	0.765 9	0.000 1	0.369 10	0.000 1	0.183 5	0.422 10	0.000 1	0.646 3	0.000 1	0.000 1	0.565 2	0.001 10	0.125 13	0.010 4	0.002 6	0.000 1	0.487 1	0.000 1	0.075 12	0.548 3	0.420 7	0.233 12	0.082 7	0.138 10	0.430 10	0.427 9	0.000 9	0.000 1	0.549 5	0.000 2	0.000 2	0.074 7	0.409 12	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
PPT-SpUNet-F.T.	0.332 8	0.556 4	0.270 5	0.123 10	0.816 5	0.682 6	0.946 5	0.549 6	0.657 7	0.756 4	0.459 5	0.376 7	0.550 7	0.001 9	0.807 3	0.616 3	0.727 8	0.267 5	0.691 4	0.942 8	0.530 8	0.872 5	0.874 6	0.330 6	0.542 10	0.374 6	0.792 4	0.400 10	0.673 3	0.572 5	0.433 2	0.793 5	0.623 5	0.008 13	0.351 6	0.594 6	0.000 1	0.783 9	0.876 6	0.833 4	0.213 4	0.000 7	0.537 6	0.091 5	0.519 3	0.304 5	0.620 6	0.942 1	0.264 3	0.124 6	0.855 5	0.695 4	0.086 6	0.646 7	0.506 12	0.658 5	0.535 5	0.715 3	0.314 1	0.000 3	0.241 4	0.608 2	0.897 2	0.359 7	0.858 7	0.000 3	0.076 13	0.611 9	0.392 8	0.509 6	0.378 3	0.000 1	0.579 3	0.565 12	0.000 4	0.000 7	0.000 1	0.000 4	0.755 5	0.806 8	0.661 3	0.572 11	0.350 8	0.181 7	0.660 8	0.300 10	0.000 3	0.000 6	0.000 1	0.023 8	0.000 3	0.042 12	0.930 3	0.000 1	0.000 8	0.077 6	0.584 5	0.392 7	0.339 7	0.185 6	0.171 8	0.308 2	0.006 11	0.563 3	0.256 7	0.150 3	0.000 3	0.002 12	0.345 11	0.000 1	0.045 10	0.197 3	0.063 7	0.323 9	0.453 3	0.600 7	0.163 7	0.037 11	0.349 2	0.000 1	0.672 3	0.679 3	0.753 2	0.000 1	0.000 8	0.000 8	0.117 4	0.000 5	0.000 8	0.291 8	0.000 8	0.000 5	0.039 5	0.000 1	0.000 2	0.899 2	0.000 1	0.374 9	0.000 1	0.000 9	0.545 4	0.000 1	0.634 4	0.000 1	0.000 1	0.074 9	0.223 4	0.914 6	0.000 6	0.021 5	0.000 1	0.000 5	0.000 1	0.112 5	0.498 9	0.649 1	0.383 8	0.095 2	0.135 11	0.449 9	0.432 8	0.008 6	0.000 1	0.518 6	0.000 2	0.000 2	0.000 8	0.796 3	0.000 2	0.000 1	0.000 1	0.138 10	0.000 5	0.000 3	0.000 7	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
LGround	0.272 11	0.485 11	0.184 11	0.106 11	0.778 11	0.676 8	0.932 11	0.479 13	0.572 11	0.718 10	0.399 8	0.265 11	0.453 12	0.085 3	0.745 11	0.446 11	0.726 9	0.232 11	0.622 11	0.901 11	0.512 10	0.826 11	0.786 12	0.178 12	0.549 8	0.277 11	0.659 11	0.381 11	0.518 10	0.295 13	0.323 10	0.777 8	0.599 8	0.028 9	0.321 7	0.363 12	0.000 1	0.708 11	0.858 11	0.746 9	0.063 11	0.022 5	0.457 11	0.077 8	0.476 8	0.243 11	0.402 10	0.397 13	0.233 8	0.077 13	0.720 13	0.610 12	0.103 4	0.629 9	0.437 13	0.626 9	0.446 10	0.702 5	0.190 9	0.005 1	0.058 12	0.322 11	0.702 12	0.244 11	0.768 10	0.000 3	0.134 9	0.552 11	0.279 12	0.395 11	0.147 12	0.000 1	0.207 11	0.612 10	0.000 4	0.000 7	0.000 1	0.000 4	0.658 7	0.566 11	0.323 11	0.525 13	0.229 10	0.179 8	0.467 13	0.154 12	0.000 3	0.002 4	0.000 1	0.051 1	0.000 3	0.127 2	0.703 10	0.000 1	0.000 8	0.216 1	0.112 12	0.358 8	0.547 1	0.187 5	0.092 12	0.156 13	0.055 8	0.296 11	0.252 8	0.143 4	0.000 3	0.014 10	0.398 7	0.000 1	0.028 12	0.173 5	0.000 11	0.265 12	0.348 11	0.415 12	0.179 4	0.019 12	0.218 6	0.000 1	0.597 6	0.274 13	0.565 9	0.000 1	0.012 5	0.000 8	0.039 12	0.022 2	0.000 8	0.117 11	0.000 8	0.000 5	0.000 9	0.000 1	0.000 2	0.324 12	0.000 1	0.384 7	0.000 1	0.000 9	0.251 13	0.000 1	0.566 9	0.000 1	0.000 1	0.066 10	0.404 1	0.886 10	0.199 1	0.000 7	0.000 1	0.059 3	0.000 1	0.136 1	0.540 4	0.127 13	0.295 9	0.085 6	0.143 6	0.514 6	0.413 11	0.000 9	0.000 1	0.498 7	0.000 2	0.000 2	0.000 8	0.623 9	0.000 2	0.000 1	0.000 1	0.132 12	0.000 5	0.000 3	0.000 7	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrain	0.249 13	0.455 13	0.171 12	0.079 13	0.766 13	0.659 11	0.930 13	0.494 10	0.542 13	0.700 13	0.314 13	0.215 13	0.430 13	0.121 1	0.697 13	0.441 12	0.683 12	0.235 10	0.609 13	0.895 12	0.476 13	0.816 12	0.770 13	0.186 10	0.634 4	0.216 13	0.734 7	0.340 12	0.471 12	0.307 12	0.293 13	0.591 13	0.542 11	0.076 6	0.205 12	0.464 11	0.000 1	0.484 13	0.832 13	0.766 7	0.052 12	0.000 7	0.413 12	0.059 12	0.418 12	0.222 12	0.318 13	0.609 11	0.206 11	0.112 7	0.743 10	0.625 10	0.076 7	0.579 12	0.548 9	0.590 12	0.371 12	0.552 13	0.081 12	0.003 2	0.142 10	0.201 13	0.638 13	0.233 12	0.686 13	0.000 3	0.142 7	0.444 13	0.375 9	0.247 13	0.198 10	0.000 1	0.128 13	0.454 13	0.019 2	0.097 1	0.000 1	0.000 4	0.553 10	0.557 12	0.373 9	0.545 12	0.164 12	0.014 13	0.547 12	0.174 11	0.000 3	0.002 4	0.000 1	0.037 3	0.000 3	0.063 9	0.664 12	0.000 1	0.000 8	0.130 2	0.170 10	0.152 13	0.335 8	0.079 10	0.110 11	0.175 10	0.098 7	0.175 13	0.166 11	0.045 13	0.207 1	0.014 10	0.465 5	0.000 1	0.001 13	0.001 13	0.046 8	0.299 11	0.327 12	0.537 9	0.033 12	0.012 13	0.186 8	0.000 1	0.205 12	0.377 10	0.463 12	0.000 1	0.058 3	0.000 8	0.055 11	0.041 1	0.000 8	0.105 12	0.000 8	0.000 5	0.000 9	0.000 1	0.000 2	0.398 11	0.000 1	0.308 13	0.000 1	0.000 9	0.319 11	0.000 1	0.543 10	0.000 1	0.000 1	0.062 11	0.004 9	0.862 12	0.000 6	0.000 7	0.000 1	0.000 5	0.000 1	0.123 4	0.316 12	0.225 11	0.250 11	0.094 3	0.180 5	0.332 12	0.441 7	0.000 9	0.000 1	0.310 13	0.000 2	0.000 2	0.000 8	0.592 10	0.000 2	0.000 1	0.000 1	0.203 2	0.000 5	0.000 3	0.000 7	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021

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

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

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
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
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
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.

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 20	0.854 6	0.770 11	0.856 13	0.555 13	0.943 1	0.660 23	0.735 2	0.979 1	0.606 6	0.492 1	0.792 3	0.934 3	0.841 2	0.819 4	0.716 7	0.947 9	0.906 1	0.822 1

PTv3 ScanNet	0.794 2	0.941 3	0.813 19	0.851 8	0.782 6	0.890 2	0.597 1	0.916 3	0.696 8	0.713 4	0.979 1	0.635 1	0.384 3	0.793 2	0.907 9	0.821 5	0.790 32	0.696 12	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)
PonderV2	0.785 3	0.978 1	0.800 28	0.833 24	0.788 4	0.853 17	0.545 17	0.910 6	0.713 1	0.705 5	0.979 1	0.596 8	0.390 2	0.769 13	0.832 42	0.821 5	0.792 31	0.730 1	0.975 1	0.897 5	0.785 5
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 4	0.964 2	0.855 1	0.843 17	0.781 7	0.858 12	0.575 6	0.831 34	0.685 14	0.714 3	0.979 1	0.594 9	0.310 27	0.801 1	0.892 17	0.841 2	0.819 4	0.723 4	0.940 14	0.887 7	0.725 25
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 5	0.861 21	0.818 14	0.836 21	0.790 3	0.875 4	0.576 5	0.905 7	0.704 5	0.739 1	0.969 11	0.611 2	0.349 11	0.756 23	0.958 1	0.702 46	0.805 15	0.708 8	0.916 33	0.898 4	0.801 3

TTT-KD	0.773 6	0.646 92	0.818 14	0.809 36	0.774 9	0.878 3	0.581 2	0.943 1	0.687 12	0.704 6	0.978 5	0.607 5	0.336 16	0.775 9	0.912 7	0.838 4	0.823 2	0.694 13	0.967 3	0.899 3	0.794 4
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 7	0.939 4	0.824 6	0.854 6	0.771 10	0.840 31	0.564 10	0.900 9	0.686 13	0.677 13	0.961 17	0.537 32	0.348 12	0.769 13	0.903 11	0.785 11	0.815 7	0.676 23	0.939 15	0.880 12	0.772 9

PPT-SpUNet-Joint	0.766 8	0.932 5	0.794 34	0.829 26	0.751 23	0.854 15	0.540 21	0.903 8	0.630 35	0.672 16	0.963 15	0.565 22	0.357 9	0.788 4	0.900 13	0.737 26	0.802 16	0.685 18	0.950 7	0.887 7	0.780 6
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 8	0.925 7	0.808 24	0.849 10	0.786 5	0.846 27	0.566 9	0.876 16	0.690 10	0.674 15	0.960 18	0.576 18	0.226 68	0.753 25	0.904 10	0.777 13	0.815 7	0.722 5	0.923 28	0.877 14	0.776 8
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
OccuSeg+Semantic	0.764 10	0.758 59	0.796 32	0.839 19	0.746 26	0.907 1	0.562 11	0.850 26	0.680 16	0.672 16	0.978 5	0.610 3	0.335 18	0.777 7	0.819 46	0.847 1	0.830 1	0.691 15	0.972 2	0.885 9	0.727 23

CU-Hybrid Net	0.764 10	0.924 8	0.819 12	0.840 18	0.757 18	0.853 17	0.580 3	0.848 27	0.709 3	0.643 25	0.958 22	0.587 13	0.295 34	0.753 25	0.884 21	0.758 20	0.815 7	0.725 3	0.927 25	0.867 23	0.743 16

O-CNN	0.762 12	0.924 8	0.823 7	0.844 16	0.770 11	0.852 19	0.577 4	0.847 29	0.711 2	0.640 29	0.958 22	0.592 10	0.217 74	0.762 18	0.888 18	0.758 20	0.813 11	0.726 2	0.932 23	0.868 22	0.744 15
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 13	0.843 27	0.820 10	0.847 13	0.791 2	0.862 10	0.511 34	0.870 18	0.707 4	0.652 21	0.954 36	0.604 7	0.279 45	0.760 19	0.942 2	0.734 27	0.766 45	0.701 11	0.884 55	0.874 20	0.736 17

OA-CNN-L_ScanNet20	0.756 14	0.783 45	0.826 5	0.858 4	0.776 8	0.837 34	0.548 16	0.896 12	0.649 27	0.675 14	0.962 16	0.586 14	0.335 18	0.771 12	0.802 50	0.770 16	0.787 34	0.691 15	0.936 18	0.880 12	0.761 11

ConDaFormer	0.755 15	0.927 6	0.822 8	0.836 21	0.801 1	0.849 22	0.516 31	0.864 23	0.651 26	0.680 12	0.958 22	0.584 16	0.282 42	0.759 21	0.855 32	0.728 29	0.802 16	0.678 20	0.880 60	0.873 21	0.756 13
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
PNE	0.755 15	0.786 43	0.835 4	0.834 23	0.758 16	0.849 22	0.570 8	0.836 33	0.648 28	0.668 18	0.978 5	0.581 17	0.367 7	0.683 36	0.856 30	0.804 7	0.801 20	0.678 20	0.961 5	0.889 6	0.716 30
P. Hermosilla: Point Neighborhood Embeddings.
DMF-Net	0.752 17	0.906 13	0.793 36	0.802 42	0.689 41	0.825 47	0.556 12	0.867 19	0.681 15	0.602 45	0.960 18	0.555 28	0.365 8	0.779 6	0.859 27	0.747 23	0.795 28	0.717 6	0.917 32	0.856 31	0.764 10
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 17	0.742 67	0.809 23	0.872 1	0.758 16	0.860 11	0.552 14	0.891 14	0.610 42	0.687 7	0.960 18	0.559 26	0.304 30	0.766 16	0.926 5	0.767 17	0.797 24	0.644 34	0.942 12	0.876 17	0.722 27
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 19	0.909 11	0.818 14	0.811 34	0.752 21	0.839 33	0.485 48	0.842 30	0.673 18	0.644 24	0.957 26	0.528 38	0.305 29	0.773 10	0.859 27	0.788 9	0.818 6	0.693 14	0.916 33	0.856 31	0.723 26
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 19	0.793 41	0.790 37	0.807 38	0.750 25	0.856 13	0.524 27	0.881 15	0.588 54	0.642 28	0.977 9	0.591 11	0.274 48	0.781 5	0.929 4	0.804 7	0.796 25	0.642 35	0.947 9	0.885 9	0.715 31
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP	0.748 21	0.623 95	0.804 26	0.859 3	0.745 27	0.824 49	0.501 38	0.912 5	0.690 10	0.685 9	0.956 27	0.567 21	0.320 24	0.768 15	0.918 6	0.720 34	0.802 16	0.676 23	0.921 30	0.881 11	0.779 7

StratifiedFormer	0.747 22	0.901 14	0.803 27	0.845 15	0.757 18	0.846 27	0.512 33	0.825 37	0.696 8	0.645 23	0.956 27	0.576 18	0.262 59	0.744 30	0.861 26	0.742 24	0.770 43	0.705 9	0.899 45	0.860 28	0.734 18
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion	0.746 23	0.771 53	0.819 12	0.848 12	0.702 38	0.865 9	0.397 86	0.899 10	0.699 6	0.664 19	0.948 56	0.588 12	0.330 20	0.746 29	0.851 36	0.764 18	0.796 25	0.704 10	0.935 19	0.866 24	0.728 21
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNet	0.746 23	0.870 19	0.838 2	0.858 4	0.729 32	0.850 21	0.501 38	0.874 17	0.587 55	0.658 20	0.956 27	0.564 23	0.299 32	0.765 17	0.900 13	0.716 37	0.812 12	0.631 40	0.939 15	0.858 29	0.709 32
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
DiffSeg3D2	0.745 25	0.725 76	0.814 18	0.837 20	0.751 23	0.831 41	0.514 32	0.896 12	0.674 17	0.684 10	0.960 18	0.564 23	0.303 31	0.773 10	0.820 45	0.713 40	0.798 23	0.690 17	0.923 28	0.875 18	0.757 12

Retro-FPN	0.744 26	0.842 28	0.800 28	0.767 56	0.740 28	0.836 36	0.541 19	0.914 4	0.672 19	0.626 33	0.958 22	0.552 29	0.272 50	0.777 7	0.886 20	0.696 47	0.801 20	0.674 26	0.941 13	0.858 29	0.717 28
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 27	0.620 96	0.799 31	0.849 10	0.730 31	0.822 51	0.493 45	0.897 11	0.664 20	0.681 11	0.955 30	0.562 25	0.378 4	0.760 19	0.903 11	0.738 25	0.801 20	0.673 27	0.907 37	0.877 14	0.745 14
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT	0.742 28	0.860 22	0.765 50	0.819 29	0.769 13	0.848 24	0.533 23	0.829 35	0.663 21	0.631 32	0.955 30	0.586 14	0.274 48	0.753 25	0.896 15	0.729 28	0.760 51	0.666 29	0.921 30	0.855 33	0.733 19

LRPNet	0.742 28	0.816 36	0.806 25	0.807 38	0.752 21	0.828 45	0.575 6	0.839 32	0.699 6	0.637 30	0.954 36	0.520 41	0.320 24	0.755 24	0.834 40	0.760 19	0.772 40	0.676 23	0.915 35	0.862 26	0.717 28

LargeKernel3D	0.739 30	0.909 11	0.820 10	0.806 40	0.740 28	0.852 19	0.545 17	0.826 36	0.594 53	0.643 25	0.955 30	0.541 31	0.263 58	0.723 34	0.858 29	0.775 15	0.767 44	0.678 20	0.933 21	0.848 38	0.694 37
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 31	0.776 49	0.790 37	0.851 8	0.754 20	0.854 15	0.491 47	0.866 21	0.596 52	0.686 8	0.955 30	0.536 33	0.342 14	0.624 51	0.869 23	0.787 10	0.802 16	0.628 41	0.927 25	0.875 18	0.704 34

MinkowskiNet	0.736 31	0.859 23	0.818 14	0.832 25	0.709 36	0.840 31	0.521 29	0.853 25	0.660 23	0.643 25	0.951 46	0.544 30	0.286 40	0.731 32	0.893 16	0.675 56	0.772 40	0.683 19	0.874 67	0.852 36	0.727 23
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 33	0.890 15	0.837 3	0.864 2	0.726 33	0.873 5	0.530 26	0.824 38	0.489 88	0.647 22	0.978 5	0.609 4	0.336 16	0.624 51	0.733 59	0.758 20	0.776 38	0.570 66	0.949 8	0.877 14	0.728 21

PointTransformer++	0.725 34	0.727 75	0.811 22	0.819 29	0.765 14	0.841 30	0.502 37	0.814 43	0.621 38	0.623 35	0.955 30	0.556 27	0.284 41	0.620 53	0.866 24	0.781 12	0.757 55	0.648 32	0.932 23	0.862 26	0.709 32

SparseConvNet	0.725 34	0.647 91	0.821 9	0.846 14	0.721 34	0.869 6	0.533 23	0.754 59	0.603 48	0.614 37	0.955 30	0.572 20	0.325 22	0.710 35	0.870 22	0.724 32	0.823 2	0.628 41	0.934 20	0.865 25	0.683 40

MatchingNet	0.724 36	0.812 38	0.812 20	0.810 35	0.735 30	0.834 38	0.495 44	0.860 24	0.572 62	0.602 45	0.954 36	0.512 43	0.280 44	0.757 22	0.845 38	0.725 31	0.780 36	0.606 51	0.937 17	0.851 37	0.700 36

INS-Conv-semantic	0.717 37	0.751 62	0.759 53	0.812 33	0.704 37	0.868 7	0.537 22	0.842 30	0.609 44	0.608 41	0.953 40	0.534 35	0.293 35	0.616 54	0.864 25	0.719 36	0.793 29	0.640 36	0.933 21	0.845 42	0.663 46

PointMetaBase	0.714 38	0.835 29	0.785 39	0.821 27	0.684 43	0.846 27	0.531 25	0.865 22	0.614 39	0.596 49	0.953 40	0.500 46	0.246 64	0.674 37	0.888 18	0.692 48	0.764 47	0.624 43	0.849 82	0.844 43	0.675 42

contrastBoundary	0.705 39	0.769 56	0.775 44	0.809 36	0.687 42	0.820 54	0.439 74	0.812 44	0.661 22	0.591 51	0.945 64	0.515 42	0.171 92	0.633 48	0.856 30	0.720 34	0.796 25	0.668 28	0.889 52	0.847 39	0.689 38
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 40	0.774 51	0.800 28	0.793 47	0.760 15	0.847 26	0.471 52	0.802 47	0.463 95	0.634 31	0.968 13	0.491 49	0.271 52	0.726 33	0.910 8	0.706 42	0.815 7	0.551 78	0.878 61	0.833 44	0.570 78

RFCR	0.702 41	0.889 16	0.745 64	0.813 32	0.672 46	0.818 58	0.493 45	0.815 42	0.623 36	0.610 39	0.947 58	0.470 58	0.249 63	0.594 57	0.848 37	0.705 43	0.779 37	0.646 33	0.892 50	0.823 50	0.611 61
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 42	0.825 33	0.796 32	0.723 63	0.716 35	0.832 40	0.433 76	0.816 40	0.634 33	0.609 40	0.969 11	0.418 84	0.344 13	0.559 69	0.833 41	0.715 38	0.808 14	0.560 72	0.902 42	0.847 39	0.680 41

JSENet	0.699 43	0.881 18	0.762 51	0.821 27	0.667 47	0.800 70	0.522 28	0.792 50	0.613 40	0.607 42	0.935 84	0.492 48	0.205 79	0.576 62	0.853 34	0.691 50	0.758 53	0.652 31	0.872 70	0.828 47	0.649 50
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 44	0.743 66	0.794 34	0.655 86	0.684 43	0.822 51	0.497 43	0.719 69	0.622 37	0.617 36	0.977 9	0.447 71	0.339 15	0.750 28	0.664 75	0.703 45	0.790 32	0.596 56	0.946 11	0.855 33	0.647 51
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 45	0.732 71	0.772 45	0.786 48	0.677 45	0.866 8	0.517 30	0.848 27	0.509 81	0.626 33	0.952 44	0.536 33	0.225 70	0.545 75	0.704 66	0.689 53	0.810 13	0.564 71	0.903 41	0.854 35	0.729 20
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 46	0.884 17	0.754 57	0.795 45	0.647 54	0.818 58	0.422 78	0.802 47	0.612 41	0.604 43	0.945 64	0.462 61	0.189 87	0.563 68	0.853 34	0.726 30	0.765 46	0.632 39	0.904 39	0.821 53	0.606 65
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 47	0.704 81	0.741 68	0.754 60	0.656 49	0.829 43	0.501 38	0.741 64	0.609 44	0.548 59	0.950 50	0.522 40	0.371 5	0.633 48	0.756 54	0.715 38	0.771 42	0.623 44	0.861 78	0.814 56	0.658 47
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 48	0.866 20	0.748 61	0.819 29	0.645 56	0.794 73	0.450 64	0.802 47	0.587 55	0.604 43	0.945 64	0.464 60	0.201 82	0.554 71	0.840 39	0.723 33	0.732 65	0.602 54	0.907 37	0.822 52	0.603 68

VACNN++	0.684 49	0.728 74	0.757 56	0.776 53	0.690 39	0.804 68	0.464 57	0.816 40	0.577 61	0.587 52	0.945 64	0.508 45	0.276 47	0.671 38	0.710 64	0.663 61	0.750 59	0.589 61	0.881 58	0.832 46	0.653 49

DGNet	0.684 49	0.712 80	0.784 40	0.782 52	0.658 48	0.835 37	0.499 42	0.823 39	0.641 30	0.597 48	0.950 50	0.487 51	0.281 43	0.575 63	0.619 79	0.647 69	0.764 47	0.620 46	0.871 73	0.846 41	0.688 39

KP-FCNN	0.684 49	0.847 26	0.758 55	0.784 50	0.647 54	0.814 61	0.473 51	0.772 53	0.605 46	0.594 50	0.935 84	0.450 69	0.181 90	0.587 58	0.805 49	0.690 51	0.785 35	0.614 47	0.882 57	0.819 54	0.632 57
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
Superpoint Network	0.683 52	0.851 25	0.728 72	0.800 44	0.653 51	0.806 66	0.468 54	0.804 45	0.572 62	0.602 45	0.946 61	0.453 68	0.239 67	0.519 80	0.822 43	0.689 53	0.762 50	0.595 58	0.895 48	0.827 48	0.630 58

PointContrast_LA_SEM	0.683 52	0.757 60	0.784 40	0.786 48	0.639 58	0.824 49	0.408 81	0.775 52	0.604 47	0.541 61	0.934 88	0.532 36	0.269 54	0.552 72	0.777 52	0.645 72	0.793 29	0.640 36	0.913 36	0.824 49	0.671 43

VI-PointConv	0.676 54	0.770 55	0.754 57	0.783 51	0.621 62	0.814 61	0.552 14	0.758 57	0.571 64	0.557 57	0.954 36	0.529 37	0.268 56	0.530 78	0.682 70	0.675 56	0.719 68	0.603 53	0.888 53	0.833 44	0.665 45
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 55	0.789 42	0.748 61	0.763 58	0.635 60	0.814 61	0.407 83	0.747 61	0.581 59	0.573 54	0.950 50	0.484 52	0.271 52	0.607 55	0.754 55	0.649 66	0.774 39	0.596 56	0.883 56	0.823 50	0.606 65

SALANet	0.670 56	0.816 36	0.770 48	0.768 55	0.652 52	0.807 65	0.451 61	0.747 61	0.659 25	0.545 60	0.924 94	0.473 57	0.149 102	0.571 65	0.811 48	0.635 75	0.746 60	0.623 44	0.892 50	0.794 69	0.570 78

O3DSeg	0.668 57	0.822 34	0.771 47	0.496 106	0.651 53	0.833 39	0.541 19	0.761 56	0.555 70	0.611 38	0.966 14	0.489 50	0.370 6	0.388 100	0.580 82	0.776 14	0.751 57	0.570 66	0.956 6	0.817 55	0.646 52

PointASNL	0.666 58	0.703 82	0.781 42	0.751 62	0.655 50	0.830 42	0.471 52	0.769 54	0.474 91	0.537 63	0.951 46	0.475 56	0.279 45	0.635 46	0.698 69	0.675 56	0.751 57	0.553 77	0.816 89	0.806 60	0.703 35
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConv	0.666 58	0.781 46	0.759 53	0.699 71	0.644 57	0.822 51	0.475 50	0.779 51	0.564 67	0.504 77	0.953 40	0.428 78	0.203 81	0.586 60	0.754 55	0.661 62	0.753 56	0.588 62	0.902 42	0.813 58	0.642 53
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++	0.663 60	0.746 64	0.708 75	0.722 64	0.638 59	0.820 54	0.451 61	0.566 97	0.599 50	0.541 61	0.950 50	0.510 44	0.313 26	0.648 43	0.819 46	0.616 80	0.682 83	0.590 60	0.869 74	0.810 59	0.656 48
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 61	0.778 47	0.702 78	0.806 40	0.619 63	0.813 64	0.468 54	0.693 77	0.494 84	0.524 69	0.941 76	0.449 70	0.298 33	0.510 82	0.821 44	0.675 56	0.727 67	0.568 69	0.826 87	0.803 63	0.637 55
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MVF-GNN	0.658 61	0.558 103	0.751 59	0.655 86	0.690 39	0.722 95	0.453 60	0.867 19	0.579 60	0.576 53	0.893 106	0.523 39	0.293 35	0.733 31	0.571 84	0.692 48	0.659 90	0.606 51	0.875 64	0.804 62	0.668 44

HPGCNN	0.656 63	0.698 84	0.743 66	0.650 88	0.564 80	0.820 54	0.505 36	0.758 57	0.631 34	0.479 81	0.945 64	0.480 54	0.226 68	0.572 64	0.774 53	0.690 51	0.735 63	0.614 47	0.853 81	0.776 84	0.597 71
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 64	0.752 61	0.734 70	0.664 84	0.583 75	0.815 60	0.399 85	0.754 59	0.639 31	0.535 65	0.942 74	0.470 58	0.309 28	0.665 39	0.539 86	0.650 65	0.708 73	0.635 38	0.857 80	0.793 71	0.642 53
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 65	0.778 47	0.731 71	0.699 71	0.577 76	0.829 43	0.446 66	0.736 65	0.477 90	0.523 71	0.945 64	0.454 65	0.269 54	0.484 90	0.749 58	0.618 78	0.738 61	0.599 55	0.827 86	0.792 74	0.621 60

PointConv-SFPN	0.641 66	0.776 49	0.703 77	0.721 65	0.557 83	0.826 46	0.451 61	0.672 82	0.563 68	0.483 80	0.943 73	0.425 81	0.162 97	0.644 44	0.726 60	0.659 63	0.709 72	0.572 65	0.875 64	0.786 79	0.559 84

MVPNet	0.641 66	0.831 30	0.715 73	0.671 81	0.590 71	0.781 79	0.394 87	0.679 79	0.642 29	0.553 58	0.937 81	0.462 61	0.256 60	0.649 42	0.406 100	0.626 76	0.691 80	0.666 29	0.877 62	0.792 74	0.608 64
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 68	0.717 79	0.701 79	0.692 74	0.576 77	0.801 69	0.467 56	0.716 70	0.563 68	0.459 87	0.953 40	0.429 77	0.169 94	0.581 61	0.854 33	0.605 81	0.710 70	0.550 79	0.894 49	0.793 71	0.575 76

FPConv	0.639 69	0.785 44	0.760 52	0.713 69	0.603 66	0.798 71	0.392 88	0.534 102	0.603 48	0.524 69	0.948 56	0.457 63	0.250 62	0.538 76	0.723 62	0.598 85	0.696 78	0.614 47	0.872 70	0.799 64	0.567 81
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 70	0.797 40	0.769 49	0.641 94	0.590 71	0.820 54	0.461 58	0.537 101	0.637 32	0.536 64	0.947 58	0.388 91	0.206 78	0.656 40	0.668 73	0.647 69	0.732 65	0.585 63	0.868 75	0.793 71	0.473 104

PointSPNet	0.637 71	0.734 70	0.692 86	0.714 68	0.576 77	0.797 72	0.446 66	0.743 63	0.598 51	0.437 92	0.942 74	0.403 87	0.150 101	0.626 50	0.800 51	0.649 66	0.697 77	0.557 75	0.846 83	0.777 83	0.563 82

SConv	0.636 72	0.830 31	0.697 82	0.752 61	0.572 79	0.780 81	0.445 68	0.716 70	0.529 74	0.530 66	0.951 46	0.446 72	0.170 93	0.507 85	0.666 74	0.636 74	0.682 83	0.541 85	0.886 54	0.799 64	0.594 72

Supervoxel-CNN	0.635 73	0.656 89	0.711 74	0.719 66	0.613 64	0.757 90	0.444 71	0.765 55	0.534 73	0.566 55	0.928 92	0.478 55	0.272 50	0.636 45	0.531 88	0.664 60	0.645 94	0.508 92	0.864 77	0.792 74	0.611 61

joint point-based	0.634 74	0.614 97	0.778 43	0.667 83	0.633 61	0.825 47	0.420 79	0.804 45	0.467 93	0.561 56	0.951 46	0.494 47	0.291 37	0.566 66	0.458 95	0.579 91	0.764 47	0.559 74	0.838 84	0.814 56	0.598 70
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 75	0.731 72	0.688 89	0.675 78	0.591 70	0.784 78	0.444 71	0.565 98	0.610 42	0.492 78	0.949 54	0.456 64	0.254 61	0.587 58	0.706 65	0.599 84	0.665 89	0.612 50	0.868 75	0.791 77	0.579 75

3DSM_DMMF	0.631 76	0.626 94	0.745 64	0.801 43	0.607 65	0.751 91	0.506 35	0.729 68	0.565 66	0.491 79	0.866 109	0.434 73	0.197 85	0.595 56	0.630 78	0.709 41	0.705 75	0.560 72	0.875 64	0.740 94	0.491 99

PointNet2-SFPN	0.631 76	0.771 53	0.692 86	0.672 79	0.524 88	0.837 34	0.440 73	0.706 75	0.538 72	0.446 89	0.944 70	0.421 83	0.219 73	0.552 72	0.751 57	0.591 87	0.737 62	0.543 84	0.901 44	0.768 86	0.557 85

APCF-Net	0.631 76	0.742 67	0.687 91	0.672 79	0.557 83	0.792 76	0.408 81	0.665 83	0.545 71	0.508 74	0.952 44	0.428 78	0.186 88	0.634 47	0.702 67	0.620 77	0.706 74	0.555 76	0.873 68	0.798 66	0.581 74
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv	0.630 79	0.604 99	0.741 68	0.766 57	0.590 71	0.747 92	0.501 38	0.734 66	0.503 83	0.527 67	0.919 98	0.454 65	0.323 23	0.550 74	0.420 99	0.678 55	0.688 81	0.544 82	0.896 47	0.795 68	0.627 59
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 80	0.800 39	0.625 102	0.719 66	0.545 85	0.806 66	0.445 68	0.597 91	0.448 98	0.519 72	0.938 80	0.481 53	0.328 21	0.489 89	0.499 93	0.657 64	0.759 52	0.592 59	0.881 58	0.797 67	0.634 56

SegGroup_sem	0.627 81	0.818 35	0.747 63	0.701 70	0.602 67	0.764 87	0.385 92	0.629 88	0.490 86	0.508 74	0.931 91	0.409 86	0.201 82	0.564 67	0.725 61	0.618 78	0.692 79	0.539 86	0.873 68	0.794 69	0.548 88
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 82	0.830 31	0.694 84	0.757 59	0.563 81	0.772 85	0.448 65	0.647 86	0.520 77	0.509 73	0.949 54	0.431 76	0.191 86	0.496 87	0.614 80	0.647 69	0.672 87	0.535 88	0.876 63	0.783 80	0.571 77

dtc_net	0.625 82	0.703 82	0.751 59	0.794 46	0.535 86	0.848 24	0.480 49	0.676 81	0.528 75	0.469 84	0.944 70	0.454 65	0.004 115	0.464 92	0.636 77	0.704 44	0.758 53	0.548 81	0.924 27	0.787 78	0.492 98

HPEIN	0.618 84	0.729 73	0.668 92	0.647 90	0.597 69	0.766 86	0.414 80	0.680 78	0.520 77	0.525 68	0.946 61	0.432 74	0.215 75	0.493 88	0.599 81	0.638 73	0.617 99	0.570 66	0.897 46	0.806 60	0.605 67
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 85	0.858 24	0.772 45	0.489 107	0.532 87	0.792 76	0.404 84	0.643 87	0.570 65	0.507 76	0.935 84	0.414 85	0.046 112	0.510 82	0.702 67	0.602 83	0.705 75	0.549 80	0.859 79	0.773 85	0.534 91
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 86	0.760 58	0.667 93	0.649 89	0.521 89	0.793 74	0.457 59	0.648 85	0.528 75	0.434 94	0.947 58	0.401 88	0.153 100	0.454 93	0.721 63	0.648 68	0.717 69	0.536 87	0.904 39	0.765 87	0.485 100
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 87	0.634 93	0.743 66	0.697 73	0.601 68	0.781 79	0.437 75	0.585 94	0.493 85	0.446 89	0.933 89	0.394 89	0.011 114	0.654 41	0.661 76	0.603 82	0.733 64	0.526 89	0.832 85	0.761 89	0.480 101

LAP-D	0.594 88	0.720 77	0.692 86	0.637 95	0.456 99	0.773 84	0.391 90	0.730 67	0.587 55	0.445 91	0.940 78	0.381 92	0.288 38	0.434 96	0.453 97	0.591 87	0.649 92	0.581 64	0.777 93	0.749 93	0.610 63

DPC	0.592 89	0.720 77	0.700 80	0.602 99	0.480 95	0.762 89	0.380 93	0.713 73	0.585 58	0.437 92	0.940 78	0.369 94	0.288 38	0.434 96	0.509 92	0.590 89	0.639 97	0.567 70	0.772 95	0.755 91	0.592 73
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 90	0.766 57	0.659 97	0.683 76	0.470 98	0.740 94	0.387 91	0.620 90	0.490 86	0.476 82	0.922 96	0.355 97	0.245 65	0.511 81	0.511 91	0.571 92	0.643 95	0.493 96	0.872 70	0.762 88	0.600 69

ROSMRF	0.580 91	0.772 52	0.707 76	0.681 77	0.563 81	0.764 87	0.362 95	0.515 103	0.465 94	0.465 86	0.936 83	0.427 80	0.207 77	0.438 94	0.577 83	0.536 95	0.675 86	0.486 97	0.723 101	0.779 81	0.524 94

SD-DETR	0.576 92	0.746 64	0.609 106	0.445 111	0.517 90	0.643 106	0.366 94	0.714 72	0.456 96	0.468 85	0.870 108	0.432 74	0.264 57	0.558 70	0.674 71	0.586 90	0.688 81	0.482 98	0.739 99	0.733 96	0.537 90

SQN_0.1%	0.569 93	0.676 86	0.696 83	0.657 85	0.497 91	0.779 82	0.424 77	0.548 99	0.515 79	0.376 99	0.902 105	0.422 82	0.357 9	0.379 101	0.456 96	0.596 86	0.659 90	0.544 82	0.685 104	0.665 107	0.556 86

TextureNet	0.566 94	0.672 88	0.664 94	0.671 81	0.494 93	0.719 96	0.445 68	0.678 80	0.411 104	0.396 97	0.935 84	0.356 96	0.225 70	0.412 98	0.535 87	0.565 93	0.636 98	0.464 100	0.794 92	0.680 104	0.568 80
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 95	0.648 90	0.700 80	0.770 54	0.586 74	0.687 100	0.333 99	0.650 84	0.514 80	0.475 83	0.906 102	0.359 95	0.223 72	0.340 103	0.442 98	0.422 106	0.668 88	0.501 93	0.708 102	0.779 81	0.534 91

Pointnet++ & Feature	0.557 96	0.735 69	0.661 96	0.686 75	0.491 94	0.744 93	0.392 88	0.539 100	0.451 97	0.375 100	0.946 61	0.376 93	0.205 79	0.403 99	0.356 103	0.553 94	0.643 95	0.497 94	0.824 88	0.756 90	0.515 95

GMLPs	0.538 97	0.495 108	0.693 85	0.647 90	0.471 97	0.793 74	0.300 102	0.477 104	0.505 82	0.358 102	0.903 104	0.327 100	0.081 109	0.472 91	0.529 89	0.448 104	0.710 70	0.509 90	0.746 97	0.737 95	0.554 87

PanopticFusion-label	0.529 98	0.491 109	0.688 89	0.604 98	0.386 104	0.632 107	0.225 112	0.705 76	0.434 101	0.293 108	0.815 110	0.348 98	0.241 66	0.499 86	0.669 72	0.507 97	0.649 92	0.442 106	0.796 91	0.602 111	0.561 83
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 99	0.676 86	0.591 109	0.609 96	0.442 100	0.774 83	0.335 98	0.597 91	0.422 103	0.357 103	0.932 90	0.341 99	0.094 108	0.298 105	0.528 90	0.473 102	0.676 85	0.495 95	0.602 110	0.721 99	0.349 111

Online SegFusion	0.515 100	0.607 98	0.644 100	0.579 101	0.434 101	0.630 108	0.353 96	0.628 89	0.440 99	0.410 95	0.762 114	0.307 102	0.167 95	0.520 79	0.403 101	0.516 96	0.565 102	0.447 104	0.678 105	0.701 101	0.514 96
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	0.501 101	0.558 103	0.608 107	0.424 113	0.478 96	0.690 99	0.246 108	0.586 93	0.468 92	0.450 88	0.911 100	0.394 89	0.160 98	0.438 94	0.212 110	0.432 105	0.541 108	0.475 99	0.742 98	0.727 97	0.477 102

PCNN	0.498 102	0.559 102	0.644 100	0.560 103	0.420 103	0.711 98	0.229 110	0.414 105	0.436 100	0.352 104	0.941 76	0.324 101	0.155 99	0.238 110	0.387 102	0.493 98	0.529 109	0.509 90	0.813 90	0.751 92	0.504 97

Weakly-Openseg v3	0.489 103	0.749 63	0.664 94	0.646 92	0.496 92	0.559 112	0.122 115	0.577 95	0.257 115	0.364 101	0.805 111	0.198 113	0.096 107	0.510 82	0.496 94	0.361 110	0.563 103	0.359 113	0.777 93	0.644 108	0.532 93

3DMV	0.484 104	0.484 110	0.538 111	0.643 93	0.424 102	0.606 111	0.310 100	0.574 96	0.433 102	0.378 98	0.796 112	0.301 103	0.214 76	0.537 77	0.208 111	0.472 103	0.507 112	0.413 109	0.693 103	0.602 111	0.539 89
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 105	0.577 101	0.611 105	0.356 115	0.321 112	0.715 97	0.299 104	0.376 109	0.328 111	0.319 106	0.944 70	0.285 105	0.164 96	0.216 113	0.229 108	0.484 100	0.545 107	0.456 102	0.755 96	0.709 100	0.475 103
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 106	0.679 85	0.604 108	0.578 102	0.380 105	0.682 101	0.291 105	0.106 115	0.483 89	0.258 113	0.920 97	0.258 109	0.025 113	0.231 112	0.325 104	0.480 101	0.560 105	0.463 101	0.725 100	0.666 106	0.231 115
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 107	0.474 111	0.623 103	0.463 109	0.366 107	0.651 104	0.310 100	0.389 108	0.349 109	0.330 105	0.937 81	0.271 107	0.126 104	0.285 106	0.224 109	0.350 112	0.577 101	0.445 105	0.625 108	0.723 98	0.394 107
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	0.442 108	0.548 105	0.548 110	0.597 100	0.363 108	0.628 109	0.300 102	0.292 110	0.374 106	0.307 107	0.881 107	0.268 108	0.186 88	0.238 110	0.204 112	0.407 107	0.506 113	0.449 103	0.667 106	0.620 110	0.462 105

SurfaceConvPF	0.442 108	0.505 107	0.622 104	0.380 114	0.342 110	0.654 103	0.227 111	0.397 107	0.367 107	0.276 110	0.924 94	0.240 110	0.198 84	0.359 102	0.262 106	0.366 108	0.581 100	0.435 107	0.640 107	0.668 105	0.398 106
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 110	0.437 113	0.646 99	0.474 108	0.369 106	0.645 105	0.353 96	0.258 112	0.282 113	0.279 109	0.918 99	0.298 104	0.147 103	0.283 107	0.294 105	0.487 99	0.562 104	0.427 108	0.619 109	0.633 109	0.352 110
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 111	0.525 106	0.647 98	0.522 104	0.324 111	0.488 115	0.077 116	0.712 74	0.353 108	0.401 96	0.636 116	0.281 106	0.176 91	0.340 103	0.565 85	0.175 116	0.551 106	0.398 110	0.370 116	0.602 111	0.361 109

SPLAT Net	0.393 112	0.472 112	0.511 112	0.606 97	0.311 113	0.656 102	0.245 109	0.405 106	0.328 111	0.197 114	0.927 93	0.227 112	0.000 117	0.001 117	0.249 107	0.271 115	0.510 110	0.383 112	0.593 111	0.699 102	0.267 113
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	0.383 113	0.297 115	0.491 113	0.432 112	0.358 109	0.612 110	0.274 106	0.116 114	0.411 104	0.265 111	0.904 103	0.229 111	0.079 110	0.250 108	0.185 113	0.320 113	0.510 110	0.385 111	0.548 112	0.597 114	0.394 107

PointNet++	0.339 114	0.584 100	0.478 114	0.458 110	0.256 115	0.360 116	0.250 107	0.247 113	0.278 114	0.261 112	0.677 115	0.183 114	0.117 105	0.212 114	0.145 115	0.364 109	0.346 116	0.232 116	0.548 112	0.523 115	0.252 114
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNet	0.308 115	0.353 114	0.290 116	0.278 116	0.166 116	0.553 113	0.169 114	0.286 111	0.147 116	0.148 116	0.908 101	0.182 115	0.064 111	0.023 116	0.018 117	0.354 111	0.363 114	0.345 114	0.546 114	0.685 103	0.278 112

ScanNet	0.306 116	0.203 116	0.366 115	0.501 105	0.311 113	0.524 114	0.211 113	0.002 117	0.342 110	0.189 115	0.786 113	0.145 116	0.102 106	0.245 109	0.152 114	0.318 114	0.348 115	0.300 115	0.460 115	0.437 116	0.182 116
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 117	0.000 117	0.041 117	0.172 117	0.030 117	0.062 117	0.001 117	0.035 116	0.004 117	0.051 117	0.143 117	0.019 117	0.003 116	0.041 115	0.050 116	0.003 117	0.054 117	0.018 117	0.005 117	0.264 117	0.082 117

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 2	0.541 8	0.502 2	0.772 4	0.314 4	0.598 11	0.425 7	0.504 7	0.565 1	0.650 5	0.716 2	0.809 7	0.476 10	0.747 4	0.618 1	0.963 3	0.364 18
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
SIM3D	0.617 2	0.952 4	0.629 14	0.539 9	0.426 13	0.768 8	0.302 6	0.681 2	0.425 8	0.473 13	0.511 13	0.701 1	0.717 1	0.821 6	0.467 13	0.774 1	0.559 13	0.914 15	0.448 2

Spherical Mask(CtoF)	0.616 3	0.946 5	0.654 10	0.555 5	0.434 10	0.769 7	0.271 10	0.604 8	0.447 4	0.505 5	0.549 2	0.698 2	0.716 2	0.775 14	0.480 7	0.747 5	0.575 9	0.925 11	0.436 4

EV3D	0.615 4	0.946 5	0.652 11	0.555 5	0.433 11	0.773 3	0.271 11	0.604 8	0.447 4	0.506 4	0.544 5	0.698 2	0.716 2	0.775 14	0.480 7	0.747 5	0.572 11	0.925 11	0.435 5

ExtMask3D	0.598 5	0.852 15	0.692 6	0.433 28	0.461 6	0.791 1	0.264 12	0.488 33	0.493 1	0.508 3	0.528 12	0.594 10	0.706 6	0.791 8	0.483 5	0.734 9	0.595 3	0.911 17	0.437 3

MAFT	0.596 6	0.889 13	0.721 1	0.448 21	0.460 7	0.768 9	0.251 13	0.558 21	0.408 9	0.504 6	0.539 7	0.616 8	0.618 10	0.858 3	0.482 6	0.684 18	0.551 15	0.931 10	0.450 1

UniPerception	0.588 7	0.963 3	0.667 8	0.493 13	0.472 5	0.750 12	0.229 16	0.528 26	0.468 3	0.498 10	0.542 6	0.643 6	0.530 19	0.661 35	0.463 14	0.695 17	0.599 2	0.972 1	0.420 6

MG-Former	0.587 8	0.852 15	0.639 13	0.454 20	0.393 18	0.758 11	0.338 2	0.572 16	0.480 2	0.527 2	0.491 19	0.671 4	0.527 20	0.867 1	0.485 4	0.601 28	0.590 6	0.938 9	0.390 10

InsSSM	0.586 9	1.000 1	0.593 18	0.440 24	0.480 3	0.771 5	0.345 1	0.437 37	0.444 6	0.495 11	0.548 4	0.579 13	0.621 9	0.720 26	0.409 20	0.712 11	0.593 4	0.960 4	0.395 8

Queryformer	0.583 10	0.926 8	0.702 4	0.393 34	0.504 1	0.733 18	0.276 9	0.527 27	0.373 15	0.479 12	0.534 9	0.533 20	0.697 7	0.720 27	0.436 18	0.745 7	0.592 5	0.958 5	0.363 19

Competitor-SPFormer	0.580 11	0.721 31	0.705 3	0.593 3	0.444 9	0.786 2	0.286 7	0.564 19	0.376 14	0.498 9	0.534 10	0.546 18	0.390 41	0.785 10	0.577 1	0.708 15	0.579 8	0.954 6	0.388 11

PBNet	0.573 12	0.926 8	0.575 23	0.619 1	0.472 4	0.736 16	0.239 15	0.487 34	0.383 13	0.459 16	0.506 16	0.533 19	0.585 12	0.767 16	0.404 21	0.717 10	0.559 14	0.969 2	0.381 14
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 13	0.778 23	0.675 7	0.598 2	0.451 8	0.727 19	0.280 8	0.476 36	0.395 10	0.472 14	0.457 25	0.583 11	0.580 14	0.777 11	0.462 16	0.735 8	0.547 17	0.919 14	0.333 25
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
Mask3D	0.566 14	0.926 8	0.597 17	0.408 31	0.420 15	0.737 15	0.239 14	0.598 11	0.386 12	0.458 17	0.549 2	0.568 16	0.716 2	0.601 41	0.480 7	0.646 22	0.575 9	0.922 13	0.364 17
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
OneFormer3D	0.566 14	0.781 22	0.697 5	0.562 4	0.431 12	0.770 6	0.331 3	0.400 43	0.373 16	0.529 1	0.504 17	0.568 15	0.475 25	0.732 24	0.470 11	0.762 2	0.550 16	0.871 32	0.379 15
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
ISBNet	0.559 16	0.939 7	0.655 9	0.383 37	0.426 14	0.763 10	0.180 18	0.534 25	0.386 11	0.499 8	0.509 15	0.621 7	0.427 35	0.704 30	0.467 12	0.649 21	0.571 12	0.948 7	0.401 7
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 17	1.000 1	0.611 16	0.438 25	0.392 19	0.714 20	0.139 21	0.598 13	0.327 19	0.389 20	0.510 14	0.598 9	0.427 36	0.754 19	0.463 15	0.761 3	0.588 7	0.903 20	0.329 26

SPFormer	0.549 18	0.745 26	0.640 12	0.484 14	0.395 17	0.739 14	0.311 5	0.566 18	0.335 18	0.468 15	0.492 18	0.555 17	0.478 24	0.747 21	0.436 17	0.712 12	0.540 18	0.893 24	0.343 24
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DKNet	0.532 19	0.815 19	0.624 15	0.517 10	0.377 21	0.749 13	0.107 23	0.509 30	0.304 21	0.437 18	0.475 20	0.581 12	0.539 17	0.775 13	0.339 26	0.640 24	0.506 21	0.901 21	0.385 13
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst	0.520 20	0.889 13	0.551 27	0.548 7	0.418 16	0.665 30	0.064 32	0.585 14	0.260 29	0.277 34	0.471 22	0.500 21	0.644 8	0.785 9	0.369 22	0.591 31	0.511 19	0.878 29	0.362 20

SoftGroup++	0.513 21	0.704 33	0.578 22	0.398 33	0.363 27	0.704 21	0.061 33	0.647 5	0.297 26	0.378 23	0.537 8	0.343 24	0.614 11	0.828 5	0.295 31	0.710 14	0.505 23	0.875 31	0.394 9

SSTNet	0.506 22	0.738 29	0.549 28	0.497 12	0.316 32	0.693 24	0.178 19	0.377 46	0.198 35	0.330 25	0.463 24	0.576 14	0.515 21	0.857 4	0.494 2	0.637 25	0.457 27	0.943 8	0.290 35
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SoftGroup	0.504 23	0.667 40	0.579 20	0.372 39	0.381 20	0.694 23	0.072 29	0.677 3	0.303 22	0.387 21	0.531 11	0.319 28	0.582 13	0.754 18	0.318 27	0.643 23	0.492 24	0.907 19	0.388 12
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 23	0.926 8	0.579 19	0.472 16	0.367 24	0.626 40	0.165 20	0.432 38	0.221 31	0.408 19	0.449 27	0.411 22	0.564 15	0.746 22	0.421 19	0.707 16	0.438 30	0.846 40	0.288 36

TD3D	0.489 25	0.852 15	0.511 37	0.434 26	0.322 31	0.735 17	0.101 26	0.512 29	0.355 17	0.349 24	0.468 23	0.283 32	0.514 22	0.676 34	0.268 36	0.671 19	0.510 20	0.908 18	0.329 27
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
OccuSeg+instance	0.486 26	0.802 21	0.536 30	0.428 29	0.369 23	0.702 22	0.205 17	0.331 51	0.301 23	0.379 22	0.474 21	0.327 25	0.437 30	0.862 2	0.485 3	0.601 29	0.394 38	0.846 42	0.273 39
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg	0.479 27	0.704 33	0.564 24	0.467 18	0.366 25	0.633 38	0.068 30	0.554 22	0.262 28	0.328 26	0.447 28	0.323 26	0.534 18	0.722 25	0.288 33	0.614 26	0.482 25	0.912 16	0.358 22

DualGroup	0.469 28	0.815 19	0.552 26	0.398 32	0.374 22	0.683 26	0.130 22	0.539 24	0.310 20	0.327 27	0.407 31	0.276 33	0.447 29	0.535 45	0.342 25	0.659 20	0.455 28	0.900 23	0.301 31

SSEC	0.465 29	0.667 40	0.578 21	0.502 11	0.362 28	0.641 37	0.035 42	0.605 7	0.291 27	0.323 28	0.451 26	0.296 30	0.417 39	0.677 33	0.245 40	0.501 49	0.506 22	0.900 22	0.366 16

HAIS	0.457 30	0.704 33	0.561 25	0.457 19	0.364 26	0.673 27	0.046 41	0.547 23	0.194 36	0.308 29	0.426 29	0.288 31	0.454 28	0.711 28	0.262 37	0.563 39	0.434 32	0.889 26	0.344 23
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group	0.436 31	0.630 48	0.508 40	0.480 15	0.310 34	0.624 42	0.065 31	0.638 6	0.174 37	0.256 38	0.384 35	0.194 45	0.428 33	0.759 17	0.289 32	0.574 36	0.400 36	0.849 39	0.291 34
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 32	0.716 32	0.495 42	0.355 41	0.331 29	0.689 25	0.102 25	0.394 45	0.208 34	0.280 32	0.395 33	0.250 36	0.544 16	0.741 23	0.309 29	0.536 45	0.391 39	0.842 45	0.258 43

Mask-Group	0.434 33	0.778 23	0.516 35	0.471 17	0.330 30	0.658 31	0.029 44	0.526 28	0.249 30	0.256 37	0.400 32	0.309 29	0.384 44	0.296 61	0.368 23	0.575 35	0.425 33	0.877 30	0.362 21
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 34	0.741 27	0.463 47	0.433 27	0.283 37	0.625 41	0.103 24	0.298 56	0.125 46	0.260 36	0.424 30	0.322 27	0.472 26	0.701 31	0.363 24	0.711 13	0.309 55	0.882 27	0.272 41
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 35	0.630 48	0.508 39	0.367 40	0.249 44	0.658 32	0.016 52	0.673 4	0.131 44	0.234 41	0.383 36	0.270 34	0.434 31	0.748 20	0.274 35	0.609 27	0.406 35	0.842 44	0.267 42
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet	0.413 36	0.741 27	0.520 32	0.237 52	0.284 36	0.523 51	0.097 27	0.691 1	0.138 41	0.209 51	0.229 53	0.238 39	0.390 42	0.707 29	0.310 28	0.448 56	0.470 26	0.892 25	0.310 29

PointGroup	0.407 37	0.639 47	0.496 41	0.415 30	0.243 46	0.645 36	0.021 49	0.570 17	0.114 47	0.211 49	0.359 38	0.217 43	0.428 34	0.660 36	0.256 38	0.562 40	0.341 47	0.860 35	0.291 33
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 38	0.738 29	0.465 46	0.331 45	0.205 50	0.655 33	0.051 37	0.601 10	0.092 51	0.211 50	0.329 41	0.198 44	0.459 27	0.775 12	0.195 47	0.524 47	0.400 37	0.878 28	0.184 52

PE	0.396 39	0.667 40	0.467 45	0.446 23	0.243 45	0.624 43	0.022 48	0.577 15	0.106 48	0.219 44	0.340 39	0.239 38	0.487 23	0.475 52	0.225 42	0.541 44	0.350 45	0.818 47	0.273 40
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3D	0.395 40	0.642 46	0.518 34	0.447 22	0.259 43	0.666 29	0.050 38	0.251 61	0.166 38	0.231 42	0.362 37	0.232 40	0.331 47	0.535 44	0.229 41	0.587 32	0.438 31	0.850 37	0.317 28
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS	0.392 41	0.778 23	0.530 31	0.220 54	0.278 38	0.567 48	0.083 28	0.330 52	0.299 24	0.270 35	0.310 44	0.143 51	0.260 51	0.624 39	0.277 34	0.568 38	0.361 43	0.865 34	0.301 30

AOIA	0.387 42	0.704 33	0.515 36	0.385 36	0.225 49	0.669 28	0.005 59	0.482 35	0.126 45	0.181 54	0.269 50	0.221 42	0.426 37	0.478 51	0.218 43	0.592 30	0.371 41	0.851 36	0.242 45

SSEN	0.384 43	0.852 15	0.494 43	0.192 55	0.226 48	0.648 35	0.022 47	0.398 44	0.299 25	0.277 33	0.317 43	0.231 41	0.194 58	0.514 48	0.196 45	0.586 33	0.444 29	0.843 43	0.184 51
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 44	0.593 50	0.520 33	0.390 35	0.314 33	0.600 44	0.018 51	0.287 59	0.151 40	0.281 31	0.387 34	0.169 49	0.429 32	0.654 37	0.172 51	0.578 34	0.384 40	0.670 58	0.278 38

PCJC	0.375 45	0.704 33	0.542 29	0.284 49	0.197 52	0.649 34	0.006 56	0.426 39	0.138 42	0.242 39	0.304 45	0.183 48	0.388 43	0.629 38	0.141 58	0.546 43	0.344 46	0.738 53	0.283 37

ClickSeg_Instance	0.366 46	0.654 44	0.375 51	0.184 56	0.302 35	0.592 46	0.050 39	0.300 55	0.093 50	0.283 30	0.277 47	0.249 37	0.426 38	0.615 40	0.299 30	0.504 48	0.367 42	0.832 46	0.191 50

SphereSeg	0.357 47	0.651 45	0.411 49	0.345 42	0.264 42	0.630 39	0.059 34	0.289 58	0.212 32	0.240 40	0.336 40	0.158 50	0.305 48	0.557 42	0.159 54	0.455 55	0.341 48	0.726 55	0.294 32

3D-MPA	0.355 48	0.457 60	0.484 44	0.299 47	0.277 39	0.591 47	0.047 40	0.332 49	0.212 33	0.217 45	0.278 46	0.193 46	0.413 40	0.410 55	0.195 46	0.574 37	0.352 44	0.849 38	0.213 48
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 49	0.593 50	0.511 38	0.375 38	0.264 41	0.597 45	0.008 54	0.332 50	0.160 39	0.229 43	0.274 49	0.000 72	0.206 55	0.678 32	0.155 55	0.485 51	0.422 34	0.816 48	0.254 44
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 50	0.475 57	0.456 48	0.320 46	0.275 40	0.476 53	0.020 50	0.491 32	0.056 58	0.212 48	0.320 42	0.261 35	0.302 49	0.520 46	0.182 49	0.557 41	0.285 57	0.867 33	0.197 49

GICN	0.341 51	0.580 52	0.371 52	0.344 43	0.198 51	0.469 54	0.052 36	0.564 20	0.093 49	0.212 47	0.212 55	0.127 53	0.347 46	0.537 43	0.206 44	0.525 46	0.329 50	0.729 54	0.241 46

One_Thing_One_Click	0.326 52	0.472 58	0.361 53	0.232 53	0.183 53	0.555 49	0.000 65	0.498 31	0.038 60	0.195 52	0.226 54	0.362 23	0.168 59	0.469 53	0.251 39	0.553 42	0.335 49	0.846 41	0.117 60
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 53	0.679 39	0.352 54	0.334 44	0.229 47	0.436 55	0.025 45	0.412 42	0.058 56	0.161 59	0.240 52	0.085 55	0.262 50	0.496 50	0.187 48	0.467 53	0.328 51	0.775 49	0.231 47

Sparse R-CNN	0.292 54	0.704 33	0.213 64	0.153 58	0.154 55	0.551 50	0.053 35	0.212 62	0.132 43	0.174 56	0.274 48	0.070 57	0.363 45	0.441 54	0.176 50	0.424 58	0.234 59	0.758 51	0.161 56

MTML	0.282 55	0.577 53	0.380 50	0.182 57	0.107 61	0.430 56	0.001 62	0.422 40	0.057 57	0.179 55	0.162 58	0.070 58	0.229 53	0.511 49	0.161 52	0.491 50	0.313 52	0.650 61	0.162 54
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet	0.262 56	0.667 40	0.335 55	0.067 65	0.123 59	0.427 57	0.022 46	0.280 60	0.058 55	0.216 46	0.211 56	0.039 61	0.142 61	0.519 47	0.106 62	0.338 62	0.310 54	0.721 56	0.138 57
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 57	0.463 59	0.249 63	0.113 59	0.167 54	0.412 59	0.000 64	0.374 47	0.073 52	0.173 57	0.243 51	0.130 52	0.228 54	0.368 57	0.160 53	0.356 60	0.208 60	0.711 57	0.136 58
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet	0.253 58	0.519 55	0.324 58	0.251 51	0.137 58	0.345 64	0.031 43	0.419 41	0.069 53	0.162 58	0.131 60	0.052 59	0.202 57	0.338 59	0.147 57	0.301 65	0.303 56	0.651 60	0.178 53
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 59	0.380 62	0.274 61	0.289 48	0.144 56	0.413 58	0.000 65	0.311 53	0.065 54	0.113 61	0.130 61	0.029 64	0.204 56	0.388 56	0.108 61	0.459 54	0.311 53	0.769 50	0.127 59

SegGroup_ins	0.246 60	0.556 54	0.335 56	0.062 67	0.115 60	0.490 52	0.000 65	0.297 57	0.018 64	0.186 53	0.142 59	0.083 56	0.233 52	0.216 63	0.153 56	0.469 52	0.251 58	0.744 52	0.083 63
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 61	0.250 67	0.330 57	0.275 50	0.103 62	0.228 70	0.000 65	0.345 48	0.024 62	0.088 63	0.203 57	0.186 47	0.167 60	0.367 58	0.125 59	0.221 68	0.112 70	0.666 59	0.162 55
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
UNet-backbone	0.161 62	0.519 55	0.259 62	0.084 61	0.059 64	0.325 66	0.002 60	0.093 67	0.009 66	0.077 65	0.064 64	0.045 60	0.044 68	0.161 65	0.045 64	0.331 63	0.180 62	0.566 62	0.033 72

3D-SIS	0.161 62	0.407 61	0.155 69	0.068 64	0.043 68	0.346 63	0.001 61	0.134 64	0.005 67	0.088 62	0.106 63	0.037 62	0.135 63	0.321 60	0.028 68	0.339 61	0.116 69	0.466 65	0.093 62
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet	0.158 64	0.356 63	0.173 67	0.113 60	0.140 57	0.359 60	0.012 53	0.023 70	0.039 59	0.134 60	0.123 62	0.008 68	0.089 64	0.149 66	0.117 60	0.221 67	0.128 67	0.563 63	0.094 61

Region-18class	0.146 65	0.175 71	0.321 59	0.080 62	0.062 63	0.357 61	0.000 65	0.307 54	0.002 69	0.066 66	0.044 66	0.000 72	0.018 70	0.036 71	0.054 63	0.447 57	0.133 65	0.472 64	0.060 67

SemRegionNet-20cls	0.121 66	0.296 65	0.203 65	0.071 63	0.058 65	0.349 62	0.000 65	0.150 63	0.019 63	0.054 68	0.034 69	0.017 67	0.052 66	0.042 70	0.013 71	0.209 69	0.183 61	0.371 66	0.057 68

Hier3D	0.117 67	0.222 69	0.161 68	0.054 69	0.027 70	0.289 67	0.000 65	0.124 65	0.001 71	0.079 64	0.061 65	0.027 65	0.141 62	0.240 62	0.005 72	0.310 64	0.129 66	0.153 72	0.081 64
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
3D-BEVIS	0.117 67	0.250 67	0.308 60	0.020 71	0.009 73	0.269 69	0.006 57	0.008 71	0.029 61	0.037 71	0.014 72	0.003 70	0.036 69	0.147 67	0.042 66	0.381 59	0.118 68	0.362 67	0.069 66
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
tmp	0.113 69	0.333 64	0.151 70	0.056 68	0.053 66	0.344 65	0.000 65	0.105 66	0.016 65	0.049 69	0.035 68	0.020 66	0.053 65	0.048 69	0.013 70	0.183 71	0.173 63	0.344 69	0.054 69

Sem_Recon_ins	0.098 70	0.295 66	0.187 66	0.015 72	0.036 69	0.213 71	0.005 58	0.038 69	0.003 68	0.056 67	0.037 67	0.036 63	0.015 71	0.051 68	0.044 65	0.209 70	0.098 71	0.354 68	0.071 65

ASIS	0.085 71	0.037 72	0.080 72	0.066 66	0.047 67	0.282 68	0.000 65	0.052 68	0.002 70	0.047 70	0.026 70	0.001 71	0.046 67	0.194 64	0.031 67	0.264 66	0.140 64	0.167 71	0.047 71

Sgpn_scannet	0.049 72	0.023 73	0.134 71	0.031 70	0.013 72	0.144 72	0.006 55	0.008 72	0.000 72	0.028 72	0.017 71	0.003 69	0.009 73	0.000 72	0.021 69	0.122 72	0.095 72	0.175 70	0.054 70

MaskRCNN 2d->3d Proj	0.022 73	0.185 70	0.000 73	0.000 73	0.015 71	0.000 73	0.000 63	0.006 73	0.000 72	0.010 73	0.006 73	0.107 54	0.012 72	0.000 72	0.002 73	0.027 73	0.004 73	0.022 73	0.001 73

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