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

OctFormer ScanNet200	0.326 12	0.539 9	0.265 9	0.131 11	0.806 8	0.670 12	0.943 9	0.535 11	0.662 4	0.705 15	0.423 8	0.407 5	0.505 12	0.003 10	0.765 12	0.582 7	0.686 14	0.227 15	0.680 7	0.943 10	0.601 3	0.854 10	0.892 5	0.335 5	0.417 16	0.357 9	0.724 9	0.453 10	0.632 6	0.596 4	0.432 3	0.783 10	0.512 15	0.021 12	0.244 14	0.637 1	0.000 1	0.787 11	0.873 9	0.743 10	0.000 16	0.000 7	0.534 8	0.110 3	0.499 6	0.289 9	0.626 6	0.620 11	0.168 14	0.204 2	0.849 9	0.679 7	0.117 4	0.633 11	0.684 3	0.650 7	0.552 5	0.684 8	0.312 3	0.000 3	0.175 10	0.429 10	0.865 4	0.413 4	0.837 11	0.000 6	0.145 9	0.626 8	0.451 7	0.487 11	0.513 3	0.000 1	0.529 7	0.613 12	0.000 4	0.033 6	0.000 1	0.000 5	0.828 4	0.871 3	0.622 8	0.587 8	0.411 7	0.137 10	0.645 13	0.343 11	0.000 3	0.000 7	0.000 1	0.022 11	0.000 3	0.026 16	0.829 10	0.000 1	0.022 7	0.089 6	0.842 3	0.253 14	0.318 10	0.296 3	0.178 10	0.291 3	0.224 2	0.584 2	0.200 13	0.132 8	0.000 4	0.128 10	0.227 13	0.000 3	0.230 12	0.047 10	0.149 7	0.331 9	0.412 9	0.618 6	0.164 9	0.102 10	0.522 1	0.000 1	0.655 4	0.378 12	0.469 14	0.000 1	0.000 10	0.000 11	0.105 8	0.000 7	0.000 8	0.483 3	0.000 11	0.000 7	0.028 7	0.000 1	0.000 4	0.906 1	0.000 1	0.339 14	0.000 1	0.000 12	0.457 8	0.000 1	0.612 7	0.000 1	0.000 1	0.408 3	0.000 14	0.900 10	0.000 9	0.000 10	0.000 1	0.029 6	0.000 1	0.074 13	0.455 14	0.479 5	0.427 6	0.079 8	0.140 9	0.496 7	0.414 13	0.022 6	0.000 1	0.471 13	0.000 2	0.000 2	0.000 11	0.722 6	0.000 2	0.000 1	0.000 1	0.138 13	0.000 8	0.000 3	0.000 10	0.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CeCo	0.340 6	0.551 8	0.247 12	0.181 5	0.784 12	0.661 13	0.939 12	0.564 5	0.624 12	0.721 11	0.484 4	0.429 4	0.575 4	0.027 8	0.774 11	0.503 13	0.753 5	0.242 12	0.656 10	0.945 9	0.534 7	0.865 6	0.860 10	0.177 16	0.616 8	0.400 4	0.818 2	0.579 1	0.615 10	0.367 13	0.408 6	0.726 14	0.633 4	0.162 1	0.360 8	0.619 2	0.000 1	0.828 8	0.873 9	0.924 2	0.109 11	0.083 3	0.564 5	0.057 14	0.475 12	0.266 10	0.781 1	0.767 7	0.257 6	0.100 10	0.825 10	0.663 9	0.048 14	0.620 13	0.551 11	0.595 12	0.532 7	0.692 7	0.246 5	0.000 3	0.213 5	0.615 1	0.861 6	0.376 7	0.900 7	0.000 6	0.102 15	0.660 7	0.321 14	0.547 4	0.226 12	0.000 1	0.311 12	0.742 5	0.011 3	0.006 8	0.000 1	0.000 5	0.546 14	0.824 8	0.345 13	0.665 3	0.450 5	0.435 1	0.683 7	0.411 7	0.338 1	0.000 7	0.000 1	0.030 8	0.000 3	0.068 7	0.892 7	0.000 1	0.063 4	0.000 10	0.257 12	0.304 13	0.387 5	0.079 13	0.228 5	0.190 10	0.000 13	0.586 1	0.347 4	0.133 7	0.000 4	0.037 12	0.377 9	0.000 3	0.384 8	0.006 15	0.003 12	0.421 3	0.410 10	0.643 5	0.171 8	0.121 8	0.142 11	0.000 1	0.510 12	0.447 11	0.474 13	0.000 1	0.000 10	0.286 3	0.083 10	0.000 7	0.000 8	0.603 1	0.096 7	0.063 4	0.000 10	0.000 1	0.000 4	0.898 3	0.000 1	0.429 6	0.000 1	0.400 1	0.550 3	0.000 1	0.633 6	0.000 1	0.000 1	0.377 4	0.000 14	0.916 5	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.102 10	0.499 9	0.296 13	0.463 5	0.089 5	0.304 1	0.740 2	0.401 15	0.010 7	0.000 1	0.560 3	0.000 2	0.000 2	0.709 2	0.652 9	0.000 2	0.000 1	0.000 1	0.143 7	0.000 8	0.000 3	0.609 3	0.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
AWCS	0.305 13	0.508 13	0.225 13	0.142 10	0.782 13	0.634 16	0.937 13	0.489 14	0.578 13	0.721 11	0.364 14	0.355 10	0.515 11	0.023 9	0.764 13	0.523 10	0.707 13	0.264 10	0.633 13	0.922 13	0.507 12	0.886 1	0.804 14	0.179 14	0.436 15	0.300 11	0.656 15	0.529 2	0.501 14	0.394 12	0.296 15	0.820 5	0.603 8	0.131 4	0.179 16	0.619 2	0.000 1	0.707 15	0.865 13	0.773 6	0.171 7	0.010 6	0.484 13	0.063 12	0.463 13	0.254 12	0.332 15	0.649 10	0.220 10	0.100 10	0.729 14	0.613 14	0.071 12	0.582 14	0.628 7	0.702 4	0.424 14	0.749 2	0.137 14	0.000 3	0.142 13	0.360 12	0.863 5	0.305 13	0.877 9	0.000 6	0.173 5	0.606 11	0.337 13	0.478 12	0.154 14	0.000 1	0.253 13	0.664 8	0.000 4	0.000 9	0.000 1	0.000 5	0.626 12	0.782 10	0.302 15	0.602 6	0.185 12	0.282 6	0.651 12	0.317 12	0.000 3	0.000 7	0.000 1	0.022 11	0.000 3	0.154 1	0.876 8	0.000 1	0.014 8	0.063 9	0.029 16	0.553 7	0.467 3	0.084 12	0.124 13	0.157 15	0.049 11	0.373 13	0.252 9	0.097 14	0.000 4	0.219 6	0.542 3	0.000 3	0.392 7	0.172 7	0.000 14	0.339 8	0.417 8	0.533 13	0.093 14	0.115 9	0.195 8	0.000 1	0.516 11	0.288 15	0.741 6	0.000 1	0.001 9	0.233 7	0.056 13	0.000 7	0.159 5	0.334 8	0.077 9	0.000 7	0.000 10	0.000 1	0.000 4	0.749 12	0.000 1	0.411 7	0.000 1	0.008 11	0.452 9	0.000 1	0.595 9	0.000 1	0.000 1	0.220 9	0.006 10	0.894 12	0.006 8	0.000 10	0.000 1	0.000 7	0.000 1	0.112 5	0.504 8	0.404 9	0.551 1	0.093 4	0.129 14	0.484 8	0.381 16	0.000 12	0.000 1	0.396 14	0.000 2	0.000 2	0.620 3	0.402 16	0.000 2	0.000 1	0.000 1	0.142 8	0.000 8	0.000 3	0.512 7	0.000 1

PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 2	0.851 2	0.687 6	0.971 2	0.586 2	0.755 1	0.752 7	0.505 1	0.404 6	0.575 4	0.000 12	0.848 2	0.616 4	0.761 3	0.349 1	0.738 2	0.978 2	0.546 6	0.860 8	0.926 2	0.346 3	0.654 3	0.384 6	0.828 1	0.523 3	0.699 3	0.583 5	0.387 7	0.822 3	0.688 2	0.118 5	0.474 2	0.603 4	0.000 1	0.832 7	0.903 2	0.753 8	0.140 9	0.000 7	0.650 3	0.109 4	0.520 3	0.457 1	0.497 9	0.871 4	0.281 3	0.192 3	0.887 4	0.748 3	0.168 1	0.727 7	0.733 2	0.740 1	0.644 1	0.714 4	0.190 12	0.000 3	0.256 3	0.449 9	0.914 1	0.514 3	0.759 14	0.337 2	0.172 6	0.692 6	0.617 2	0.636 1	0.325 7	0.000 1	0.641 2	0.782 1	0.000 4	0.065 3	0.000 1	0.000 5	0.842 3	0.903 2	0.661 4	0.662 4	0.612 1	0.405 2	0.731 3	0.566 3	0.000 3	0.000 7	0.000 1	0.017 14	0.301 1	0.088 5	0.941 2	0.000 1	0.077 3	0.000 10	0.717 7	0.790 2	0.310 11	0.026 16	0.264 4	0.349 1	0.220 3	0.397 12	0.366 2	0.115 12	0.000 4	0.337 1	0.463 6	0.000 3	0.531 5	0.218 3	0.593 2	0.455 2	0.469 1	0.708 3	0.210 3	0.592 2	0.108 15	0.000 1	0.728 1	0.682 3	0.671 8	0.000 1	0.000 10	0.407 1	0.136 3	0.022 3	0.575 1	0.436 5	0.259 3	0.428 1	0.048 5	0.000 1	0.000 4	0.879 5	0.000 1	0.480 2	0.000 1	0.133 9	0.597 1	0.000 1	0.690 2	0.000 1	0.000 1	0.009 15	0.000 14	0.921 4	0.000 9	0.151 4	0.000 1	0.000 7	0.000 1	0.109 7	0.494 11	0.622 2	0.394 8	0.073 10	0.141 8	0.798 1	0.528 7	0.026 5	0.000 1	0.551 4	0.000 2	0.000 2	0.134 6	0.717 7	0.000 2	0.000 1	0.000 1	0.188 3	0.000 8	0.000 3	0.791 2	0.000 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
OA-CNN-L_ScanNet200	0.333 10	0.558 4	0.269 8	0.124 12	0.821 5	0.703 3	0.946 6	0.569 4	0.662 4	0.748 8	0.487 3	0.455 3	0.572 6	0.000 12	0.789 9	0.534 8	0.736 8	0.271 7	0.713 4	0.949 6	0.498 13	0.877 3	0.860 10	0.332 6	0.706 1	0.474 2	0.788 6	0.406 12	0.637 5	0.495 10	0.355 11	0.805 7	0.592 11	0.015 13	0.396 7	0.602 5	0.000 1	0.799 10	0.876 7	0.713 12	0.276 2	0.000 7	0.493 12	0.080 8	0.448 14	0.363 4	0.661 4	0.833 6	0.262 5	0.125 6	0.823 11	0.665 8	0.076 8	0.720 8	0.557 10	0.637 8	0.517 8	0.672 9	0.227 7	0.000 3	0.158 11	0.496 7	0.843 10	0.352 9	0.835 12	0.000 6	0.103 14	0.711 5	0.527 3	0.526 5	0.320 8	0.000 1	0.568 6	0.625 11	0.067 1	0.000 9	0.000 1	0.001 4	0.806 5	0.836 7	0.621 9	0.591 7	0.373 8	0.314 5	0.668 9	0.398 8	0.003 2	0.000 7	0.000 1	0.016 15	0.024 2	0.043 12	0.906 6	0.000 1	0.052 5	0.000 10	0.384 11	0.330 12	0.342 7	0.100 11	0.223 6	0.183 12	0.112 6	0.476 5	0.313 7	0.130 9	0.196 3	0.112 11	0.370 10	0.000 3	0.234 11	0.071 8	0.160 6	0.403 4	0.398 13	0.492 14	0.197 5	0.076 12	0.272 4	0.000 1	0.200 16	0.560 10	0.735 7	0.000 1	0.000 10	0.000 11	0.110 7	0.002 6	0.021 7	0.412 6	0.000 11	0.000 7	0.000 10	0.000 1	0.000 4	0.794 10	0.000 1	0.445 5	0.000 1	0.022 10	0.509 6	0.000 1	0.517 12	0.000 1	0.000 1	0.001 16	0.245 5	0.915 6	0.024 6	0.089 6	0.000 1	0.262 2	0.000 1	0.103 9	0.524 7	0.392 10	0.515 2	0.013 16	0.251 4	0.411 11	0.662 2	0.001 11	0.000 1	0.473 12	0.000 2	0.000 2	0.150 5	0.699 8	0.000 2	0.000 1	0.000 1	0.166 5	0.000 8	0.024 2	0.000 10	0.000 1

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

PPT-SpUNet-F.T.	0.332 11	0.556 5	0.270 6	0.123 13	0.816 6	0.682 9	0.946 6	0.549 9	0.657 8	0.756 5	0.459 6	0.376 8	0.550 10	0.001 11	0.807 4	0.616 4	0.727 11	0.267 8	0.691 5	0.942 11	0.530 9	0.872 5	0.874 7	0.330 7	0.542 13	0.374 7	0.792 4	0.400 13	0.673 4	0.572 6	0.433 2	0.793 8	0.623 6	0.008 16	0.351 9	0.594 7	0.000 1	0.783 12	0.876 7	0.833 4	0.213 6	0.000 7	0.537 7	0.091 6	0.519 4	0.304 7	0.620 7	0.942 1	0.264 4	0.124 7	0.855 6	0.695 5	0.086 7	0.646 10	0.506 15	0.658 6	0.535 6	0.715 3	0.314 2	0.000 3	0.241 4	0.608 2	0.897 2	0.359 8	0.858 10	0.000 6	0.076 16	0.611 10	0.392 11	0.509 7	0.378 6	0.000 1	0.579 4	0.565 15	0.000 4	0.000 9	0.000 1	0.000 5	0.755 6	0.806 9	0.661 4	0.572 12	0.350 9	0.181 7	0.660 11	0.300 13	0.000 3	0.000 7	0.000 1	0.023 10	0.000 3	0.042 13	0.930 4	0.000 1	0.000 9	0.077 7	0.584 8	0.392 10	0.339 8	0.185 9	0.171 11	0.308 2	0.006 12	0.563 3	0.256 8	0.150 4	0.000 4	0.002 15	0.345 11	0.000 3	0.045 13	0.197 4	0.063 10	0.323 10	0.453 3	0.600 7	0.163 10	0.037 14	0.349 3	0.000 1	0.672 3	0.679 4	0.753 5	0.000 1	0.000 10	0.000 11	0.117 5	0.000 7	0.000 8	0.291 9	0.000 11	0.000 7	0.039 6	0.000 1	0.000 4	0.899 2	0.000 1	0.374 10	0.000 1	0.000 12	0.545 4	0.000 1	0.634 5	0.000 1	0.000 1	0.074 12	0.223 6	0.914 7	0.000 9	0.021 8	0.000 1	0.000 7	0.000 1	0.112 5	0.498 10	0.649 1	0.383 9	0.095 2	0.135 12	0.449 9	0.432 11	0.008 9	0.000 1	0.518 6	0.000 2	0.000 2	0.000 11	0.796 4	0.000 2	0.000 1	0.000 1	0.138 13	0.000 8	0.000 3	0.000 10	0.000 1
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
GSTran	0.334 9	0.533 11	0.250 11	0.179 7	0.799 10	0.684 7	0.940 10	0.554 8	0.633 10	0.741 9	0.405 10	0.337 11	0.560 8	0.060 5	0.794 8	0.517 12	0.732 10	0.274 5	0.647 11	0.948 7	0.459 15	0.849 11	0.864 8	0.306 8	0.648 5	0.282 13	0.717 11	0.496 6	0.624 8	0.533 7	0.363 9	0.821 4	0.573 13	0.009 15	0.411 4	0.593 8	0.000 1	0.841 4	0.873 9	0.704 13	0.242 5	0.000 7	0.495 10	0.041 15	0.487 8	0.304 7	0.439 12	0.613 12	0.133 16	0.055 15	0.853 7	0.634 11	0.075 11	0.791 5	0.601 9	0.574 15	0.483 12	0.669 10	0.217 9	0.000 3	0.198 7	0.518 5	0.782 13	0.345 10	0.914 5	0.273 4	0.193 3	0.598 13	0.440 8	0.499 8	0.570 1	0.000 1	0.381 10	0.775 3	0.000 4	0.063 5	0.000 1	0.000 5	0.712 7	0.752 13	0.507 11	0.512 15	0.158 15	0.036 12	0.773 1	0.361 10	0.000 3	0.000 7	0.000 1	0.032 6	0.000 3	0.032 14	0.651 14	0.000 1	0.000 9	0.000 10	0.831 4	0.595 4	0.273 15	0.229 6	0.200 8	0.191 8	0.000 13	0.425 9	0.233 12	0.125 10	0.000 4	0.279 4	0.213 15	0.003 1	0.608 3	0.044 11	0.138 8	0.321 11	0.408 11	0.593 10	0.198 4	0.205 7	0.139 12	0.000 1	0.614 7	0.609 7	0.838 4	0.000 1	0.014 5	0.260 4	0.080 11	0.010 5	0.000 8	0.136 12	0.136 4	0.047 5	0.000 10	0.000 1	0.787 2	0.797 9	0.000 1	0.354 13	0.000 1	0.372 3	0.357 13	0.000 1	0.507 15	0.000 1	0.000 1	0.121 10	0.423 2	0.903 8	0.028 4	0.089 6	0.000 1	0.252 3	0.000 1	0.072 15	0.465 12	0.340 11	0.189 15	0.020 15	0.011 15	0.320 15	0.606 6	0.060 3	0.000 1	0.496 8	0.000 2	0.000 2	0.070 9	0.618 12	0.000 2	0.000 1	0.000 1	0.139 10	0.047 4	0.000 3	0.558 6	0.000 1

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

L3DETR-ScanNet_200	0.336 7	0.533 10	0.279 5	0.155 9	0.801 9	0.689 4	0.946 6	0.539 10	0.660 7	0.759 4	0.380 13	0.333 13	0.583 3	0.000 12	0.788 10	0.529 9	0.740 7	0.261 11	0.679 8	0.940 12	0.525 10	0.860 8	0.883 6	0.226 12	0.613 9	0.397 5	0.720 10	0.512 4	0.565 11	0.620 3	0.417 5	0.775 12	0.629 5	0.158 2	0.298 11	0.579 10	0.000 1	0.835 5	0.883 5	0.927 1	0.114 10	0.079 4	0.511 9	0.073 10	0.508 5	0.312 5	0.629 5	0.861 5	0.192 13	0.098 12	0.908 3	0.636 10	0.032 16	0.563 16	0.514 14	0.664 5	0.505 9	0.697 6	0.225 8	0.000 3	0.264 2	0.411 11	0.860 7	0.321 12	0.960 2	0.058 5	0.109 13	0.776 3	0.526 4	0.557 2	0.303 9	0.000 1	0.339 11	0.712 7	0.000 4	0.014 7	0.000 1	0.000 5	0.638 11	0.856 4	0.641 7	0.579 10	0.107 16	0.119 11	0.661 10	0.416 6	0.000 3	0.000 7	0.000 1	0.007 16	0.000 3	0.067 8	0.910 5	0.000 1	0.000 9	0.000 10	0.463 10	0.448 8	0.294 13	0.324 2	0.293 2	0.211 6	0.108 7	0.448 8	0.068 16	0.141 6	0.000 4	0.330 2	0.699 1	0.000 3	0.256 10	0.192 5	0.000 14	0.355 7	0.418 7	0.209 16	0.146 11	0.679 1	0.101 16	0.000 1	0.503 14	0.687 2	0.671 8	0.000 1	0.000 10	0.174 9	0.117 5	0.000 7	0.122 6	0.515 2	0.104 6	0.259 2	0.312 3	0.000 1	0.000 4	0.765 11	0.000 1	0.369 11	0.000 1	0.183 8	0.422 10	0.000 1	0.646 3	0.000 1	0.000 1	0.565 2	0.001 13	0.125 16	0.010 7	0.002 9	0.000 1	0.487 1	0.000 1	0.075 12	0.548 4	0.420 8	0.233 13	0.082 7	0.138 11	0.430 10	0.427 12	0.000 12	0.000 1	0.549 5	0.000 2	0.000 2	0.074 8	0.409 15	0.000 2	0.000 1	0.000 1	0.152 6	0.051 3	0.000 3	0.598 4	0.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
DITR	0.409 2	0.616 1	0.351 1	0.215 3	0.831 3	0.791 1	0.947 5	0.619 1	0.730 2	0.762 2	0.494 2	0.571 1	0.597 2	0.000 12	0.853 1	0.625 3	0.796 2	0.301 3	0.723 3	0.959 4	0.617 2	0.862 7	0.917 3	0.573 1	0.562 10	0.591 1	0.784 7	0.504 5	0.757 1	0.737 2	0.429 4	0.853 1	0.662 3	0.135 3	0.459 3	0.558 11	0.000 1	0.913 1	0.878 6	0.687 15	0.008 15	0.000 7	0.615 4	0.238 1	0.651 1	0.370 3	0.742 2	0.925 2	0.360 1	0.167 4	0.938 1	0.752 2	0.118 3	0.827 1	0.670 4	0.723 2	0.614 3	0.628 14	0.372 1	0.000 3	0.143 12	0.175 16	0.873 3	0.652 1	0.991 1	0.340 1	0.148 8	0.814 1	0.656 1	0.524 6	0.491 4	0.000 1	0.743 1	0.752 4	0.000 4	0.000 9	0.000 1	0.399 1	0.865 2	0.953 1	0.833 1	0.694 2	0.444 6	0.000 16	0.688 6	0.609 2	0.000 3	0.053 2	0.000 1	0.022 11	0.000 3	0.053 11	0.940 3	0.000 1	0.186 1	0.093 5	0.854 2	0.877 1	0.534 2	0.404 1	0.270 3	0.191 8	0.198 4	0.461 7	0.375 1	0.152 3	0.921 1	0.132 9	0.235 12	0.000 3	0.617 1	0.330 1	0.896 1	0.399 5	0.431 5	0.597 8	0.759 1	0.554 3	0.400 2	0.000 1	0.559 10	0.699 1	0.852 2	0.000 1	0.000 10	0.091 10	0.385 1	0.000 7	0.000 8	0.478 4	0.077 9	0.000 7	0.140 4	0.000 1	0.000 4	0.670 13	0.000 1	0.452 4	0.000 1	0.263 5	0.361 11	0.000 1	0.643 4	0.000 1	0.000 1	0.357 5	0.005 11	0.928 2	0.362 1	0.496 1	0.000 1	0.000 7	0.000 1	0.072 15	0.585 2	0.587 3	0.476 4	0.037 13	0.191 5	0.410 12	0.629 4	0.118 1	0.000 1	0.479 11	0.000 2	0.000 2	0.107 7	0.839 3	0.000 2	0.000 1	0.000 1	0.139 10	0.036 6	0.000 3	0.247 9	0.000 1

PonderV2 ScanNet200	0.346 5	0.552 7	0.270 7	0.175 8	0.810 7	0.682 9	0.950 4	0.560 6	0.641 9	0.761 3	0.398 12	0.357 9	0.570 7	0.113 2	0.804 5	0.603 6	0.750 6	0.283 4	0.681 6	0.952 5	0.548 5	0.874 4	0.852 12	0.290 11	0.700 2	0.356 10	0.792 4	0.445 11	0.545 12	0.436 11	0.351 12	0.787 9	0.611 7	0.050 8	0.290 13	0.519 12	0.000 1	0.825 9	0.888 4	0.842 3	0.259 3	0.100 2	0.558 6	0.070 11	0.497 7	0.247 13	0.457 10	0.889 3	0.248 8	0.106 9	0.817 12	0.691 6	0.094 6	0.729 6	0.636 6	0.620 11	0.503 10	0.660 12	0.243 6	0.000 3	0.212 6	0.590 4	0.860 7	0.400 5	0.881 8	0.000 6	0.202 2	0.622 9	0.408 10	0.499 8	0.261 10	0.000 1	0.385 9	0.636 10	0.000 4	0.000 9	0.000 1	0.000 5	0.433 15	0.843 6	0.660 6	0.574 11	0.481 3	0.336 4	0.677 8	0.486 5	0.000 3	0.030 3	0.000 1	0.034 5	0.000 3	0.080 6	0.869 9	0.000 1	0.000 9	0.000 10	0.540 9	0.727 3	0.232 16	0.115 10	0.186 9	0.193 7	0.000 13	0.403 11	0.326 6	0.103 13	0.000 4	0.290 3	0.392 8	0.000 3	0.346 9	0.062 9	0.424 4	0.375 6	0.431 5	0.667 4	0.115 13	0.082 11	0.239 6	0.000 1	0.504 13	0.606 8	0.584 11	0.000 1	0.002 8	0.186 8	0.104 9	0.000 7	0.394 4	0.384 7	0.083 8	0.000 7	0.007 8	0.000 1	0.000 4	0.880 4	0.000 1	0.377 9	0.000 1	0.263 5	0.565 2	0.000 1	0.608 8	0.000 1	0.000 1	0.304 7	0.009 9	0.924 3	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.128 3	0.584 3	0.475 6	0.412 7	0.076 9	0.269 3	0.621 5	0.509 8	0.010 7	0.000 1	0.491 10	0.063 1	0.000 2	0.472 4	0.880 2	0.000 2	0.000 1	0.000 1	0.179 4	0.125 2	0.000 3	0.441 8	0.000 1
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
ALS-MinkowskiNet	0.414 1	0.610 2	0.322 3	0.271 1	0.852 1	0.710 2	0.973 1	0.572 3	0.719 3	0.795 1	0.477 5	0.506 2	0.601 1	0.000 12	0.804 5	0.646 2	0.804 1	0.344 2	0.777 1	0.984 1	0.671 1	0.879 2	0.936 1	0.342 4	0.632 7	0.449 3	0.817 3	0.475 9	0.723 2	0.798 1	0.376 8	0.832 2	0.693 1	0.031 9	0.564 1	0.510 13	0.000 1	0.893 2	0.905 1	0.672 16	0.314 1	0.000 7	0.718 1	0.153 2	0.542 2	0.397 2	0.726 3	0.752 8	0.252 7	0.226 1	0.916 2	0.800 1	0.047 15	0.807 3	0.769 1	0.709 3	0.630 2	0.769 1	0.217 9	0.000 3	0.285 1	0.598 3	0.846 9	0.535 2	0.956 3	0.000 6	0.137 11	0.784 2	0.464 6	0.463 13	0.230 11	0.000 1	0.598 3	0.662 9	0.000 4	0.087 2	0.000 1	0.135 2	0.900 1	0.780 11	0.703 2	0.741 1	0.571 2	0.149 9	0.697 5	0.646 1	0.000 3	0.076 1	0.000 1	0.025 9	0.000 3	0.106 4	0.981 1	0.000 1	0.043 6	0.113 3	0.888 1	0.248 15	0.404 4	0.252 5	0.314 1	0.220 5	0.245 1	0.466 6	0.366 2	0.159 2	0.000 4	0.149 7	0.690 2	0.000 3	0.531 5	0.253 2	0.285 5	0.460 1	0.440 4	0.813 1	0.230 2	0.283 5	0.159 10	0.000 1	0.728 1	0.666 5	0.958 1	0.000 1	0.021 4	0.252 6	0.118 4	0.000 7	0.445 3	0.223 10	0.285 1	0.194 3	0.390 2	0.000 1	0.475 3	0.842 7	0.000 1	0.455 3	0.000 1	0.250 7	0.458 7	0.000 1	0.865 1	0.000 1	0.000 1	0.635 1	0.359 4	0.972 1	0.087 3	0.447 2	0.000 1	0.000 7	0.000 1	0.129 2	0.532 6	0.446 7	0.503 3	0.071 11	0.135 12	0.699 3	0.717 1	0.097 2	0.000 1	0.665 1	0.000 2	0.000 2	1.000 1	0.752 5	0.000 2	0.000 1	0.000 1	0.142 8	0.200 1	0.259 1	1.000 1	0.000 1

CSC-Pretrain	0.249 16	0.455 16	0.171 15	0.079 16	0.766 16	0.659 14	0.930 16	0.494 13	0.542 16	0.700 16	0.314 16	0.215 16	0.430 16	0.121 1	0.697 16	0.441 15	0.683 15	0.235 13	0.609 16	0.895 15	0.476 14	0.816 15	0.770 16	0.186 13	0.634 6	0.216 16	0.734 8	0.340 15	0.471 15	0.307 15	0.293 16	0.591 16	0.542 14	0.076 7	0.205 15	0.464 14	0.000 1	0.484 16	0.832 16	0.766 7	0.052 14	0.000 7	0.413 15	0.059 13	0.418 15	0.222 15	0.318 16	0.609 13	0.206 12	0.112 8	0.743 13	0.625 13	0.076 8	0.579 15	0.548 12	0.590 13	0.371 15	0.552 16	0.081 15	0.003 2	0.142 13	0.201 15	0.638 16	0.233 15	0.686 16	0.000 6	0.142 10	0.444 16	0.375 12	0.247 16	0.198 13	0.000 1	0.128 16	0.454 16	0.019 2	0.097 1	0.000 1	0.000 5	0.553 13	0.557 15	0.373 12	0.545 13	0.164 13	0.014 15	0.547 15	0.174 14	0.000 3	0.002 5	0.000 1	0.037 3	0.000 3	0.063 9	0.664 13	0.000 1	0.000 9	0.130 2	0.170 13	0.152 16	0.335 9	0.079 13	0.110 14	0.175 13	0.098 8	0.175 16	0.166 14	0.045 16	0.207 2	0.014 13	0.465 5	0.000 3	0.001 16	0.001 16	0.046 11	0.299 14	0.327 15	0.537 12	0.033 15	0.012 16	0.186 9	0.000 1	0.205 15	0.377 13	0.463 15	0.000 1	0.058 3	0.000 11	0.055 14	0.041 1	0.000 8	0.105 15	0.000 11	0.000 7	0.000 10	0.000 1	0.000 4	0.398 14	0.000 1	0.308 16	0.000 1	0.000 12	0.319 14	0.000 1	0.543 11	0.000 1	0.000 1	0.062 14	0.004 12	0.862 15	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.123 4	0.316 15	0.225 14	0.250 12	0.094 3	0.180 6	0.332 13	0.441 10	0.000 12	0.000 1	0.310 16	0.000 2	0.000 2	0.000 11	0.592 13	0.000 2	0.000 1	0.000 1	0.203 2	0.000 8	0.000 3	0.000 10	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround	0.272 14	0.485 14	0.184 14	0.106 14	0.778 14	0.676 11	0.932 14	0.479 16	0.572 14	0.718 13	0.399 11	0.265 14	0.453 15	0.085 3	0.745 14	0.446 14	0.726 12	0.232 14	0.622 14	0.901 14	0.512 11	0.826 14	0.786 15	0.178 15	0.549 11	0.277 14	0.659 14	0.381 14	0.518 13	0.295 16	0.323 13	0.777 11	0.599 9	0.028 10	0.321 10	0.363 15	0.000 1	0.708 14	0.858 14	0.746 9	0.063 13	0.022 5	0.457 14	0.077 9	0.476 11	0.243 14	0.402 13	0.397 16	0.233 9	0.077 14	0.720 16	0.610 15	0.103 5	0.629 12	0.437 16	0.626 10	0.446 13	0.702 5	0.190 12	0.005 1	0.058 15	0.322 13	0.702 15	0.244 14	0.768 13	0.000 6	0.134 12	0.552 14	0.279 15	0.395 14	0.147 15	0.000 1	0.207 14	0.612 13	0.000 4	0.000 9	0.000 1	0.000 5	0.658 10	0.566 14	0.323 14	0.525 14	0.229 11	0.179 8	0.467 16	0.154 15	0.000 3	0.002 5	0.000 1	0.051 1	0.000 3	0.127 2	0.703 11	0.000 1	0.000 9	0.216 1	0.112 15	0.358 11	0.547 1	0.187 8	0.092 15	0.156 16	0.055 9	0.296 14	0.252 9	0.143 5	0.000 4	0.014 13	0.398 7	0.000 3	0.028 15	0.173 6	0.000 14	0.265 15	0.348 14	0.415 15	0.179 7	0.019 15	0.218 7	0.000 1	0.597 8	0.274 16	0.565 12	0.000 1	0.012 7	0.000 11	0.039 15	0.022 3	0.000 8	0.117 14	0.000 11	0.000 7	0.000 10	0.000 1	0.000 4	0.324 15	0.000 1	0.384 8	0.000 1	0.000 12	0.251 16	0.000 1	0.566 10	0.000 1	0.000 1	0.066 13	0.404 3	0.886 13	0.199 2	0.000 10	0.000 1	0.059 5	0.000 1	0.136 1	0.540 5	0.127 16	0.295 10	0.085 6	0.143 7	0.514 6	0.413 14	0.000 12	0.000 1	0.498 7	0.000 2	0.000 2	0.000 11	0.623 11	0.000 2	0.000 1	0.000 1	0.132 15	0.000 8	0.000 3	0.000 10	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34D	0.253 15	0.463 15	0.154 16	0.102 15	0.771 15	0.650 15	0.932 14	0.483 15	0.571 15	0.710 14	0.331 15	0.250 15	0.492 13	0.044 6	0.703 15	0.419 16	0.606 16	0.227 15	0.621 15	0.865 16	0.531 8	0.771 16	0.813 13	0.291 10	0.484 14	0.242 15	0.612 16	0.282 16	0.440 16	0.351 14	0.299 14	0.622 15	0.593 10	0.027 11	0.293 12	0.310 16	0.000 1	0.757 13	0.858 14	0.737 11	0.150 8	0.164 1	0.368 16	0.084 7	0.381 16	0.142 16	0.357 14	0.720 9	0.214 11	0.092 13	0.724 15	0.596 16	0.056 13	0.655 9	0.525 13	0.581 14	0.352 16	0.594 15	0.056 16	0.000 3	0.014 16	0.224 14	0.772 14	0.205 16	0.720 15	0.000 6	0.159 7	0.531 15	0.163 16	0.294 15	0.136 16	0.000 1	0.169 15	0.589 14	0.000 4	0.000 9	0.000 1	0.002 3	0.663 9	0.466 16	0.265 16	0.582 9	0.337 10	0.016 14	0.559 14	0.084 16	0.000 3	0.000 7	0.000 1	0.036 4	0.000 3	0.125 3	0.670 12	0.000 1	0.102 2	0.071 8	0.164 14	0.406 9	0.386 6	0.046 15	0.068 16	0.159 14	0.117 5	0.284 15	0.111 15	0.094 15	0.000 4	0.000 16	0.197 16	0.000 3	0.044 14	0.013 14	0.002 13	0.228 16	0.307 16	0.588 11	0.025 16	0.545 4	0.134 14	0.000 1	0.655 4	0.302 14	0.282 16	0.000 1	0.060 2	0.000 11	0.035 16	0.000 7	0.000 8	0.097 16	0.000 11	0.000 7	0.005 9	0.000 1	0.000 4	0.096 16	0.000 1	0.334 15	0.000 1	0.000 12	0.274 15	0.000 1	0.513 13	0.000 1	0.000 1	0.280 8	0.194 7	0.897 11	0.000 9	0.000 10	0.000 1	0.000 7	0.000 1	0.108 8	0.279 16	0.189 15	0.141 16	0.059 12	0.272 2	0.307 16	0.445 9	0.003 10	0.000 1	0.353 15	0.000 2	0.026 1	0.000 11	0.581 14	0.001 1	0.000 1	0.000 1	0.093 16	0.002 7	0.000 3	0.000 10	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019

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

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

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
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
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.
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

ScanNet Benchmark

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

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

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

PTv3 ScanNet	0.794 2	0.941 3	0.813 20	0.851 10	0.782 6	0.890 3	0.597 1	0.916 5	0.696 9	0.713 5	0.979 1	0.635 2	0.384 3	0.793 3	0.907 10	0.821 5	0.790 34	0.696 14	0.967 3	0.903 2	0.805 2
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
DITR ScanNet	0.793 3	0.811 40	0.852 2	0.889 1	0.774 10	0.907 1	0.592 2	0.927 3	0.719 1	0.718 3	0.961 17	0.652 1	0.348 12	0.817 1	0.927 5	0.795 10	0.824 2	0.749 1	0.948 9	0.887 7	0.771 11

PonderV2	0.785 4	0.978 1	0.800 30	0.833 27	0.788 4	0.853 20	0.545 19	0.910 8	0.713 2	0.705 6	0.979 1	0.596 9	0.390 2	0.769 16	0.832 44	0.821 5	0.792 33	0.730 2	0.975 1	0.897 5	0.785 6
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Mix3D	0.781 5	0.964 2	0.855 1	0.843 19	0.781 7	0.858 13	0.575 7	0.831 37	0.685 15	0.714 4	0.979 1	0.594 10	0.310 30	0.801 2	0.892 18	0.841 2	0.819 5	0.723 5	0.940 15	0.887 7	0.725 28
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3D	0.779 6	0.861 22	0.818 15	0.836 24	0.790 3	0.875 5	0.576 6	0.905 9	0.704 6	0.739 1	0.969 11	0.611 3	0.349 11	0.756 26	0.958 1	0.702 50	0.805 17	0.708 9	0.916 37	0.898 4	0.801 3

TTT-KD	0.773 7	0.646 96	0.818 15	0.809 39	0.774 10	0.878 4	0.581 3	0.943 1	0.687 13	0.704 7	0.978 5	0.607 6	0.336 18	0.775 12	0.912 8	0.838 4	0.823 3	0.694 15	0.967 3	0.899 3	0.794 5
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
ResLFE_HDS	0.772 8	0.939 4	0.824 7	0.854 7	0.771 12	0.840 35	0.564 11	0.900 11	0.686 14	0.677 14	0.961 17	0.537 35	0.348 12	0.769 16	0.903 12	0.785 14	0.815 8	0.676 26	0.939 16	0.880 13	0.772 10

OctFormer	0.766 9	0.925 7	0.808 26	0.849 12	0.786 5	0.846 30	0.566 10	0.876 19	0.690 11	0.674 16	0.960 19	0.576 21	0.226 71	0.753 28	0.904 11	0.777 16	0.815 8	0.722 6	0.923 32	0.877 16	0.776 9
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 36	0.829 29	0.751 26	0.854 18	0.540 23	0.903 10	0.630 38	0.672 18	0.963 15	0.565 25	0.357 9	0.788 5	0.900 14	0.737 30	0.802 18	0.685 20	0.950 7	0.887 7	0.780 7
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OccuSeg+Semantic	0.764 11	0.758 61	0.796 34	0.839 22	0.746 30	0.907 1	0.562 12	0.850 29	0.680 17	0.672 18	0.978 5	0.610 4	0.335 20	0.777 10	0.819 48	0.847 1	0.830 1	0.691 17	0.972 2	0.885 10	0.727 26

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

O-CNN	0.762 13	0.924 8	0.823 8	0.844 18	0.770 13	0.852 22	0.577 5	0.847 32	0.711 3	0.640 32	0.958 23	0.592 11	0.217 77	0.762 21	0.888 19	0.758 23	0.813 12	0.726 3	0.932 26	0.868 25	0.744 18
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
DiffSegNet	0.758 14	0.725 78	0.789 41	0.843 19	0.762 17	0.856 15	0.562 12	0.920 4	0.657 28	0.658 22	0.958 23	0.589 13	0.337 17	0.782 6	0.879 23	0.787 12	0.779 40	0.678 22	0.926 30	0.880 13	0.799 4

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

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

PNE	0.755 17	0.786 45	0.835 5	0.834 26	0.758 19	0.849 25	0.570 9	0.836 36	0.648 31	0.668 20	0.978 5	0.581 20	0.367 7	0.683 39	0.856 32	0.804 7	0.801 22	0.678 22	0.961 5	0.889 6	0.716 34
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer	0.755 17	0.927 6	0.822 9	0.836 24	0.801 1	0.849 25	0.516 34	0.864 26	0.651 29	0.680 13	0.958 23	0.584 18	0.282 45	0.759 24	0.855 34	0.728 33	0.802 18	0.678 22	0.880 64	0.873 23	0.756 15
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
DMF-Net	0.752 19	0.906 13	0.793 38	0.802 45	0.689 44	0.825 51	0.556 14	0.867 23	0.681 16	0.602 49	0.960 19	0.555 31	0.365 8	0.779 9	0.859 29	0.747 26	0.795 30	0.717 7	0.917 36	0.856 35	0.764 12
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointTransformerV2	0.752 19	0.742 68	0.809 25	0.872 2	0.758 19	0.860 12	0.552 16	0.891 17	0.610 45	0.687 8	0.960 19	0.559 29	0.304 33	0.766 19	0.926 6	0.767 20	0.797 26	0.644 37	0.942 13	0.876 19	0.722 30
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
BPNet	0.749 21	0.909 11	0.818 15	0.811 37	0.752 24	0.839 37	0.485 52	0.842 33	0.673 20	0.644 27	0.957 28	0.528 41	0.305 32	0.773 13	0.859 29	0.788 11	0.818 7	0.693 16	0.916 37	0.856 35	0.723 29
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer	0.749 21	0.793 43	0.790 39	0.807 41	0.750 28	0.856 15	0.524 30	0.881 18	0.588 57	0.642 31	0.977 9	0.591 12	0.274 51	0.781 8	0.929 4	0.804 7	0.796 27	0.642 38	0.947 10	0.885 10	0.715 35
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP	0.748 23	0.623 99	0.804 28	0.859 4	0.745 31	0.824 53	0.501 41	0.912 7	0.690 11	0.685 10	0.956 30	0.567 24	0.320 27	0.768 18	0.918 7	0.720 38	0.802 18	0.676 26	0.921 34	0.881 12	0.779 8

StratifiedFormer	0.747 24	0.901 14	0.803 29	0.845 17	0.757 21	0.846 30	0.512 36	0.825 40	0.696 9	0.645 26	0.956 30	0.576 21	0.262 62	0.744 34	0.861 28	0.742 28	0.770 47	0.705 11	0.899 49	0.860 32	0.734 21
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion	0.746 25	0.771 55	0.819 13	0.848 14	0.702 42	0.865 10	0.397 89	0.899 12	0.699 7	0.664 21	0.948 60	0.588 14	0.330 22	0.746 33	0.851 38	0.764 21	0.796 27	0.704 12	0.935 21	0.866 27	0.728 24
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNet	0.746 25	0.870 20	0.838 3	0.858 5	0.729 36	0.850 24	0.501 41	0.874 20	0.587 58	0.658 22	0.956 30	0.564 26	0.299 35	0.765 20	0.900 14	0.716 41	0.812 13	0.631 43	0.939 16	0.858 33	0.709 36
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
DiffSeg3D2	0.745 27	0.725 78	0.814 19	0.837 23	0.751 26	0.831 45	0.514 35	0.896 15	0.674 19	0.684 11	0.960 19	0.564 26	0.303 34	0.773 13	0.820 47	0.713 44	0.798 25	0.690 19	0.923 32	0.875 20	0.757 14

Retro-FPN	0.744 28	0.842 29	0.800 30	0.767 59	0.740 32	0.836 40	0.541 21	0.914 6	0.672 21	0.626 37	0.958 23	0.552 32	0.272 53	0.777 10	0.886 21	0.696 51	0.801 22	0.674 29	0.941 14	0.858 33	0.717 32
Peng Xiang, Xin Wen, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
EQ-Net	0.743 29	0.620 100	0.799 33	0.849 12	0.730 35	0.822 55	0.493 49	0.897 13	0.664 22	0.681 12	0.955 33	0.562 28	0.378 4	0.760 22	0.903 12	0.738 29	0.801 22	0.673 30	0.907 41	0.877 16	0.745 16
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
MVF-GNN	0.743 29	0.731 73	0.810 24	0.726 66	0.775 9	0.843 33	0.528 29	0.897 13	0.679 18	0.674 16	0.954 39	0.583 19	0.322 26	0.782 6	0.720 68	0.802 9	0.785 37	0.707 10	0.935 21	0.863 29	0.745 16

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

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

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

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

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

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

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

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

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

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

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

RFCR	0.702 45	0.889 17	0.745 68	0.813 35	0.672 49	0.818 63	0.493 49	0.815 45	0.623 39	0.610 43	0.947 62	0.470 61	0.249 66	0.594 60	0.848 39	0.705 47	0.779 40	0.646 36	0.892 54	0.823 54	0.611 64
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click	0.701 46	0.825 34	0.796 34	0.723 67	0.716 39	0.832 44	0.433 79	0.816 43	0.634 36	0.609 44	0.969 11	0.418 87	0.344 14	0.559 72	0.833 43	0.715 42	0.808 16	0.560 75	0.902 46	0.847 43	0.680 45

JSENet	0.699 47	0.881 19	0.762 55	0.821 30	0.667 50	0.800 75	0.522 31	0.792 53	0.613 43	0.607 46	0.935 88	0.492 51	0.205 82	0.576 65	0.853 36	0.691 53	0.758 57	0.652 34	0.872 73	0.828 51	0.649 53
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click	0.693 48	0.743 67	0.794 36	0.655 90	0.684 46	0.822 55	0.497 46	0.719 72	0.622 40	0.617 40	0.977 9	0.447 74	0.339 16	0.750 31	0.664 80	0.703 49	0.790 34	0.596 58	0.946 12	0.855 37	0.647 54
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-II	0.692 49	0.732 72	0.772 49	0.786 51	0.677 48	0.866 9	0.517 33	0.848 30	0.509 84	0.626 37	0.952 48	0.536 36	0.225 73	0.545 78	0.704 71	0.689 56	0.810 14	0.564 74	0.903 45	0.854 39	0.729 23
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 50	0.884 18	0.754 62	0.795 48	0.647 57	0.818 63	0.422 81	0.802 50	0.612 44	0.604 47	0.945 68	0.462 64	0.189 90	0.563 71	0.853 36	0.726 34	0.765 50	0.632 42	0.904 43	0.821 57	0.606 68
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 51	0.704 85	0.741 72	0.754 63	0.656 52	0.829 47	0.501 41	0.741 67	0.609 47	0.548 62	0.950 54	0.522 43	0.371 5	0.633 51	0.756 57	0.715 42	0.771 46	0.623 47	0.861 81	0.814 60	0.658 50
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 52	0.866 21	0.748 65	0.819 32	0.645 59	0.794 78	0.450 67	0.802 50	0.587 58	0.604 47	0.945 68	0.464 63	0.201 85	0.554 74	0.840 41	0.723 37	0.732 70	0.602 56	0.907 41	0.822 56	0.603 71

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

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

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

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

VI-PointConv	0.676 58	0.770 57	0.754 62	0.783 54	0.621 65	0.814 66	0.552 16	0.758 60	0.571 67	0.557 60	0.954 39	0.529 40	0.268 59	0.530 81	0.682 75	0.675 59	0.719 73	0.603 55	0.888 57	0.833 48	0.665 48
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D	0.673 59	0.789 44	0.748 65	0.763 61	0.635 63	0.814 66	0.407 86	0.747 64	0.581 62	0.573 57	0.950 54	0.484 55	0.271 55	0.607 58	0.754 58	0.649 69	0.774 43	0.596 58	0.883 60	0.823 54	0.606 68

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

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

PointConv	0.666 62	0.781 48	0.759 58	0.699 75	0.644 60	0.822 55	0.475 54	0.779 54	0.564 70	0.504 81	0.953 44	0.428 81	0.203 84	0.586 63	0.754 58	0.661 65	0.753 60	0.588 64	0.902 46	0.813 62	0.642 56
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 62	0.703 86	0.781 45	0.751 65	0.655 53	0.830 46	0.471 56	0.769 57	0.474 94	0.537 66	0.951 50	0.475 59	0.279 48	0.635 49	0.698 74	0.675 59	0.751 61	0.553 80	0.816 93	0.806 64	0.703 39
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++	0.663 64	0.746 65	0.708 79	0.722 68	0.638 62	0.820 58	0.451 64	0.566 100	0.599 53	0.541 64	0.950 54	0.510 47	0.313 29	0.648 46	0.819 48	0.616 83	0.682 88	0.590 62	0.869 77	0.810 63	0.656 51
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net	0.658 65	0.778 49	0.702 82	0.806 43	0.619 66	0.813 69	0.468 58	0.693 80	0.494 87	0.524 73	0.941 80	0.449 73	0.298 36	0.510 86	0.821 46	0.675 59	0.727 72	0.568 72	0.826 91	0.803 66	0.637 58
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 66	0.698 88	0.743 70	0.650 91	0.564 83	0.820 58	0.505 39	0.758 60	0.631 37	0.479 85	0.945 68	0.480 57	0.226 71	0.572 67	0.774 56	0.690 54	0.735 68	0.614 50	0.853 85	0.776 87	0.597 74
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 67	0.752 63	0.734 74	0.664 88	0.583 78	0.815 65	0.399 88	0.754 62	0.639 34	0.535 68	0.942 78	0.470 61	0.309 31	0.665 42	0.539 90	0.650 68	0.708 78	0.635 41	0.857 84	0.793 74	0.642 56
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 68	0.778 49	0.731 75	0.699 75	0.577 79	0.829 47	0.446 69	0.736 68	0.477 93	0.523 75	0.945 68	0.454 68	0.269 57	0.484 93	0.749 61	0.618 81	0.738 65	0.599 57	0.827 90	0.792 77	0.621 63

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

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

FPConv	0.639 72	0.785 46	0.760 57	0.713 73	0.603 69	0.798 76	0.392 91	0.534 105	0.603 51	0.524 73	0.948 60	0.457 66	0.250 65	0.538 79	0.723 66	0.598 88	0.696 83	0.614 50	0.872 73	0.799 67	0.567 84
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net	0.638 73	0.797 42	0.769 53	0.641 96	0.590 74	0.820 58	0.461 62	0.537 104	0.637 35	0.536 67	0.947 62	0.388 94	0.206 81	0.656 43	0.668 78	0.647 72	0.732 70	0.585 66	0.868 78	0.793 74	0.473 107

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

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

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

joint point-based	0.634 77	0.614 101	0.778 46	0.667 87	0.633 64	0.825 51	0.420 82	0.804 48	0.467 96	0.561 59	0.951 50	0.494 50	0.291 40	0.566 69	0.458 98	0.579 95	0.764 51	0.559 77	0.838 88	0.814 60	0.598 73
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL	0.632 78	0.731 73	0.688 93	0.675 82	0.591 73	0.784 83	0.444 74	0.565 101	0.610 45	0.492 82	0.949 58	0.456 67	0.254 64	0.587 61	0.706 70	0.599 87	0.665 94	0.612 53	0.868 78	0.791 80	0.579 78

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

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

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

SegGroup_sem	0.627 84	0.818 36	0.747 67	0.701 74	0.602 70	0.764 92	0.385 95	0.629 92	0.490 89	0.508 78	0.931 96	0.409 89	0.201 85	0.564 70	0.725 64	0.618 81	0.692 84	0.539 89	0.873 71	0.794 72	0.548 91
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv	0.625 85	0.830 32	0.694 88	0.757 62	0.563 84	0.772 90	0.448 68	0.647 90	0.520 80	0.509 77	0.949 58	0.431 79	0.191 89	0.496 90	0.614 85	0.647 72	0.672 92	0.535 91	0.876 67	0.783 83	0.571 80

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

HPEIN	0.618 87	0.729 75	0.668 96	0.647 93	0.597 72	0.766 91	0.414 83	0.680 82	0.520 80	0.525 72	0.946 65	0.432 77	0.215 78	0.493 91	0.599 86	0.638 76	0.617 103	0.570 69	0.897 50	0.806 64	0.605 70
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCN	0.610 88	0.858 25	0.772 49	0.489 111	0.532 90	0.792 81	0.404 87	0.643 91	0.570 68	0.507 80	0.935 88	0.414 88	0.046 115	0.510 86	0.702 72	0.602 86	0.705 80	0.549 83	0.859 82	0.773 88	0.534 94
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 89	0.760 60	0.667 97	0.649 92	0.521 92	0.793 79	0.457 63	0.648 89	0.528 78	0.434 98	0.947 62	0.401 91	0.153 104	0.454 96	0.721 67	0.648 71	0.717 74	0.536 90	0.904 43	0.765 91	0.485 103
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
Weakly-Openseg v3	0.604 90	0.901 14	0.762 55	0.627 98	0.478 98	0.820 58	0.346 101	0.689 81	0.353 111	0.528 70	0.933 93	0.217 116	0.172 95	0.530 81	0.725 64	0.593 90	0.737 66	0.515 93	0.858 83	0.772 89	0.515 97

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

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

DPC	0.592 93	0.720 80	0.700 84	0.602 102	0.480 97	0.762 94	0.380 96	0.713 76	0.585 61	0.437 96	0.940 82	0.369 97	0.288 41	0.434 99	0.509 96	0.590 93	0.639 101	0.567 73	0.772 98	0.755 95	0.592 76
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet	0.589 94	0.766 59	0.659 100	0.683 80	0.470 101	0.740 99	0.387 94	0.620 94	0.490 89	0.476 86	0.922 101	0.355 100	0.245 68	0.511 85	0.511 95	0.571 96	0.643 99	0.493 100	0.872 73	0.762 92	0.600 72

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

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

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

TextureNet	0.566 98	0.672 92	0.664 98	0.671 85	0.494 95	0.719 100	0.445 71	0.678 84	0.411 107	0.396 101	0.935 88	0.356 99	0.225 73	0.412 101	0.535 91	0.565 97	0.636 102	0.464 104	0.794 96	0.680 108	0.568 83
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet	0.562 99	0.648 94	0.700 84	0.770 57	0.586 77	0.687 104	0.333 103	0.650 88	0.514 83	0.475 87	0.906 107	0.359 98	0.223 75	0.340 106	0.442 101	0.422 110	0.668 93	0.501 97	0.708 105	0.779 84	0.534 94

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

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

PanopticFusion-label	0.529 102	0.491 112	0.688 93	0.604 101	0.386 107	0.632 111	0.225 117	0.705 79	0.434 104	0.293 111	0.815 115	0.348 101	0.241 69	0.499 89	0.669 77	0.507 101	0.649 96	0.442 110	0.796 95	0.602 115	0.561 86
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak	0.516 103	0.676 90	0.591 112	0.609 99	0.442 103	0.774 88	0.335 102	0.597 95	0.422 106	0.357 106	0.932 95	0.341 102	0.094 111	0.298 108	0.528 94	0.473 106	0.676 90	0.495 99	0.602 113	0.721 103	0.349 115

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

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

3DMV	0.484 107	0.484 113	0.538 115	0.643 95	0.424 105	0.606 115	0.310 104	0.574 99	0.433 105	0.378 102	0.796 116	0.301 106	0.214 79	0.537 80	0.208 114	0.472 107	0.507 115	0.413 113	0.693 106	0.602 115	0.539 92
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 108	0.577 105	0.611 108	0.356 119	0.321 115	0.715 101	0.299 108	0.376 112	0.328 115	0.319 109	0.944 74	0.285 108	0.164 100	0.216 116	0.229 111	0.484 104	0.545 110	0.456 106	0.755 99	0.709 104	0.475 106
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 109	0.679 89	0.604 111	0.578 105	0.380 108	0.682 105	0.291 109	0.106 119	0.483 92	0.258 117	0.920 102	0.258 112	0.025 116	0.231 115	0.325 107	0.480 105	0.560 108	0.463 105	0.725 103	0.666 110	0.231 119
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 110	0.474 114	0.623 106	0.463 113	0.366 110	0.651 108	0.310 104	0.389 111	0.349 113	0.330 108	0.937 85	0.271 110	0.126 108	0.285 109	0.224 112	0.350 115	0.577 105	0.445 109	0.625 111	0.723 102	0.394 111
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET2	0.442 111	0.548 108	0.548 114	0.597 103	0.363 111	0.628 113	0.300 106	0.292 114	0.374 109	0.307 110	0.881 111	0.268 111	0.186 91	0.238 113	0.204 115	0.407 111	0.506 116	0.449 107	0.667 109	0.620 114	0.462 109

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

SPLAT Net	0.393 115	0.472 115	0.511 116	0.606 100	0.311 116	0.656 106	0.245 114	0.405 109	0.328 115	0.197 118	0.927 98	0.227 115	0.000 120	0.001 121	0.249 110	0.271 118	0.510 113	0.383 116	0.593 114	0.699 106	0.267 117
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF	0.383 116	0.297 118	0.491 117	0.432 116	0.358 112	0.612 114	0.274 111	0.116 118	0.411 107	0.265 114	0.904 108	0.229 114	0.079 113	0.250 111	0.185 116	0.320 116	0.510 113	0.385 115	0.548 115	0.597 118	0.394 111

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

PointGroup	0.407 39	0.639 49	0.496 43	0.415 32	0.243 48	0.645 38	0.021 51	0.570 17	0.114 49	0.211 51	0.359 40	0.217 45	0.428 36	0.660 38	0.256 40	0.562 42	0.341 49	0.860 37	0.291 35
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
CSC-Pretrained	0.405 40	0.738 31	0.465 48	0.331 47	0.205 52	0.655 35	0.051 39	0.601 10	0.092 53	0.211 52	0.329 43	0.198 46	0.459 29	0.775 13	0.195 49	0.524 49	0.400 39	0.878 30	0.184 54

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

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

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

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

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

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

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

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

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

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

MTML	0.282 57	0.577 55	0.380 52	0.182 59	0.107 63	0.430 58	0.001 64	0.422 42	0.057 59	0.179 57	0.162 60	0.070 60	0.229 55	0.511 51	0.161 54	0.491 52	0.313 54	0.650 63	0.162 56
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet	0.262 58	0.667 42	0.335 57	0.067 67	0.123 61	0.427 59	0.022 48	0.280 62	0.058 57	0.216 48	0.211 58	0.039 63	0.142 63	0.519 49	0.106 64	0.338 64	0.310 56	0.721 58	0.138 59
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.254 59	0.463 61	0.249 65	0.113 61	0.167 56	0.412 61	0.000 66	0.374 49	0.073 54	0.173 59	0.243 53	0.130 54	0.228 56	0.368 59	0.160 55	0.356 62	0.208 62	0.711 59	0.136 60
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet	0.253 60	0.519 57	0.324 60	0.251 53	0.137 60	0.345 66	0.031 45	0.419 43	0.069 55	0.162 60	0.131 62	0.052 61	0.202 59	0.338 61	0.147 59	0.301 67	0.303 58	0.651 62	0.178 55
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
SPG_WSIS	0.251 61	0.380 64	0.274 63	0.289 50	0.144 58	0.413 60	0.000 67	0.311 55	0.065 56	0.113 63	0.130 63	0.029 66	0.204 58	0.388 58	0.108 63	0.459 56	0.311 55	0.769 52	0.127 61

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EMSANet (Instance)	0.241 1	0.401 1	0.439 1	0.085 1	0.242 1	0.220 1	0.081 1	0.289 2	0.117 2	0.121 1	0.182 1	0.126 1	0.346 1	0.181 2	0.181 2	0.358 1	0.156 1	0.675 2	0.131 1
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
UniDet_RVC	0.205 2	0.381 2	0.323 3	0.037 3	0.226 3	0.177 3	0.063 2	0.277 3	0.120 1	0.067 3	0.131 3	0.074 3	0.317 2	0.080 3	0.235 1	0.289 3	0.141 3	0.678 1	0.080 3

FKNet	0.204 3	0.334 3	0.358 2	0.038 2	0.234 2	0.184 2	0.025 3	0.318 1	0.042 4	0.088 2	0.141 2	0.053 4	0.300 3	0.207 1	0.171 3	0.292 2	0.149 2	0.636 3	0.109 2

MaskRCNN_ScanNet	0.119 4	0.129 4	0.212 4	0.002 4	0.112 4	0.148 4	0.014 4	0.205 4	0.044 3	0.066 4	0.078 4	0.095 2	0.142 4	0.030 4	0.128 4	0.139 4	0.080 4	0.459 4	0.057 4
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17

Presenting the ScanNet200 Benchmark

ScanNet200 Benchmark

ScanNet200 3D Semantic Label Benchmark

ScanNet200 3D Semantic Instance Benchmark

ScanNet Benchmark

3D Semantic Label Benchmark

3D Semantic Instance Benchmark

2D Semantic Label Benchmark

2D Semantic Instance Benchmark

Scene Type Classification Benchmark

Method	avg recall	apartment	bathroom	bedroom / hotel	bookstore / library	conference room	copy/mail room	hallway	kitchen	laundry room	living room / lounge	misc	office	storage / basement / garage

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

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