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

PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 2	0.851 2	0.687 8	0.971 2	0.586 2	0.755 1	0.752 9	0.505 1	0.404 7	0.575 4	0.000 11	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 4	0.654 4	0.384 6	0.828 1	0.523 3	0.699 3	0.583 6	0.387 8	0.822 5	0.688 2	0.118 5	0.474 2	0.603 5	0.000 1	0.832 5	0.903 2	0.753 9	0.140 9	0.000 7	0.650 3	0.109 4	0.520 4	0.457 1	0.497 10	0.871 4	0.281 4	0.192 4	0.887 4	0.748 3	0.168 1	0.727 7	0.733 2	0.740 1	0.644 1	0.714 5	0.190 11	0.000 3	0.256 5	0.449 7	0.914 1	0.514 3	0.759 14	0.337 2	0.172 4	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 4	0.000 1	0.000 6	0.842 5	0.903 2	0.661 4	0.662 4	0.612 1	0.405 2	0.731 1	0.566 3	0.000 3	0.000 7	0.000 1	0.017 14	0.301 1	0.088 6	0.941 2	0.000 1	0.077 3	0.000 10	0.717 7	0.790 2	0.310 12	0.026 16	0.264 4	0.349 1	0.220 3	0.397 12	0.366 2	0.115 11	0.000 5	0.337 2	0.463 6	0.000 1	0.531 3	0.218 3	0.593 2	0.455 2	0.469 1	0.708 4	0.210 4	0.592 3	0.108 15	0.000 1	0.728 1	0.682 3	0.671 8	0.000 1	0.000 8	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 2	0.000 3	0.879 5	0.000 1	0.480 3	0.000 1	0.133 8	0.597 1	0.000 1	0.690 2	0.000 1	0.000 1	0.009 14	0.000 14	0.921 4	0.000 8	0.151 4	0.000 1	0.000 6	0.000 1	0.109 8	0.494 12	0.622 2	0.394 8	0.073 11	0.141 8	0.798 1	0.528 7	0.026 5	0.000 1	0.551 5	0.000 2	0.000 2	0.134 6	0.717 8	0.000 2	0.000 1	0.000 1	0.188 3	0.000 8	0.000 4	0.791 2	0.000 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
CeCo	0.340 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 6	0.774 11	0.503 13	0.753 6	0.242 12	0.656 11	0.945 8	0.534 8	0.865 6	0.860 9	0.177 16	0.616 7	0.400 4	0.818 2	0.579 1	0.615 10	0.367 13	0.408 7	0.726 14	0.633 6	0.162 1	0.360 8	0.619 2	0.000 1	0.828 6	0.873 11	0.924 2	0.109 11	0.083 3	0.564 5	0.057 16	0.475 12	0.266 10	0.781 1	0.767 8	0.257 7	0.100 12	0.825 9	0.663 10	0.048 14	0.620 13	0.551 10	0.595 14	0.532 7	0.692 8	0.246 5	0.000 3	0.213 7	0.615 1	0.861 7	0.376 7	0.900 6	0.000 4	0.102 13	0.660 7	0.321 14	0.547 4	0.226 12	0.000 1	0.311 12	0.742 3	0.011 3	0.006 8	0.000 1	0.000 6	0.546 14	0.824 9	0.345 13	0.665 3	0.450 5	0.435 1	0.683 5	0.411 8	0.338 1	0.000 7	0.000 1	0.030 7	0.000 4	0.068 8	0.892 7	0.000 1	0.063 4	0.000 10	0.257 12	0.304 13	0.387 5	0.079 12	0.228 6	0.190 11	0.000 14	0.586 1	0.347 4	0.133 8	0.000 5	0.037 12	0.377 11	0.000 1	0.384 7	0.006 15	0.003 12	0.421 3	0.410 10	0.643 7	0.171 7	0.121 7	0.142 13	0.000 1	0.510 11	0.447 9	0.474 13	0.000 1	0.000 8	0.286 3	0.083 12	0.000 6	0.000 9	0.603 1	0.096 7	0.063 6	0.000 11	0.000 2	0.000 3	0.898 3	0.000 1	0.429 7	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 8	0.000 8	0.000 1	0.000 6	0.000 1	0.102 12	0.499 10	0.296 13	0.463 5	0.089 6	0.304 1	0.740 2	0.401 15	0.010 7	0.000 1	0.560 4	0.000 2	0.000 2	0.709 2	0.652 10	0.000 2	0.000 1	0.000 1	0.143 8	0.000 8	0.000 4	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
ALS-MinkowskiNet	0.414 1	0.610 2	0.322 3	0.271 1	0.852 1	0.710 3	0.973 1	0.572 3	0.719 3	0.795 1	0.477 5	0.506 2	0.601 1	0.000 11	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 5	0.632 6	0.449 3	0.817 3	0.475 7	0.723 2	0.798 1	0.376 9	0.832 3	0.693 1	0.031 10	0.564 1	0.510 12	0.000 1	0.893 2	0.905 1	0.672 16	0.314 2	0.000 7	0.718 1	0.153 2	0.542 2	0.397 2	0.726 3	0.752 9	0.252 8	0.226 1	0.916 2	0.800 1	0.047 15	0.807 4	0.769 1	0.709 3	0.630 2	0.769 1	0.217 9	0.000 3	0.285 2	0.598 3	0.846 10	0.535 2	0.956 3	0.000 4	0.137 9	0.784 2	0.464 7	0.463 12	0.230 11	0.000 1	0.598 3	0.662 8	0.000 4	0.087 2	0.000 1	0.135 2	0.900 1	0.780 13	0.703 2	0.741 1	0.571 2	0.149 9	0.697 3	0.646 1	0.000 3	0.076 1	0.000 1	0.025 8	0.000 4	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 6	0.314 1	0.220 6	0.245 1	0.466 7	0.366 2	0.159 2	0.000 5	0.149 7	0.690 2	0.000 1	0.531 3	0.253 2	0.285 7	0.460 1	0.440 4	0.813 1	0.230 2	0.283 6	0.159 11	0.000 1	0.728 1	0.666 5	0.958 1	0.000 1	0.021 4	0.252 4	0.118 4	0.000 6	0.445 3	0.223 12	0.285 1	0.194 3	0.390 2	0.000 2	0.475 2	0.842 8	0.000 1	0.455 4	0.000 1	0.250 5	0.458 8	0.000 1	0.865 1	0.000 1	0.000 1	0.635 1	0.359 2	0.972 1	0.087 3	0.447 2	0.000 1	0.000 6	0.000 1	0.129 2	0.532 7	0.446 8	0.503 3	0.071 12	0.135 13	0.699 3	0.717 2	0.097 2	0.000 1	0.665 1	0.000 2	0.000 2	1.000 1	0.752 6	0.000 2	0.000 1	0.000 1	0.142 9	0.200 1	0.259 1	1.000 1	0.000 1

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

PonderV2 ScanNet200	0.346 5	0.552 7	0.270 8	0.175 6	0.810 8	0.682 9	0.950 4	0.560 6	0.641 11	0.761 4	0.398 11	0.357 10	0.570 7	0.113 2	0.804 5	0.603 6	0.750 7	0.283 4	0.681 7	0.952 6	0.548 5	0.874 4	0.852 11	0.290 10	0.700 2	0.356 10	0.792 4	0.445 9	0.545 12	0.436 11	0.351 12	0.787 9	0.611 9	0.050 9	0.290 13	0.519 11	0.000 1	0.825 7	0.888 4	0.842 3	0.259 5	0.100 2	0.558 6	0.070 13	0.497 8	0.247 13	0.457 12	0.889 3	0.248 9	0.106 11	0.817 11	0.691 6	0.094 7	0.729 6	0.636 7	0.620 12	0.503 11	0.660 12	0.243 6	0.000 3	0.212 8	0.590 4	0.860 8	0.400 5	0.881 8	0.000 4	0.202 2	0.622 10	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 6	0.433 15	0.843 7	0.660 6	0.574 12	0.481 3	0.336 4	0.677 7	0.486 5	0.000 3	0.030 3	0.000 1	0.034 6	0.000 4	0.080 7	0.869 9	0.000 1	0.000 10	0.000 10	0.540 9	0.727 3	0.232 15	0.115 9	0.186 8	0.193 9	0.000 14	0.403 11	0.326 6	0.103 12	0.000 5	0.290 4	0.392 10	0.000 1	0.346 8	0.062 10	0.424 6	0.375 7	0.431 5	0.667 5	0.115 13	0.082 10	0.239 7	0.000 1	0.504 13	0.606 6	0.584 11	0.000 1	0.002 6	0.186 8	0.104 10	0.000 6	0.394 4	0.384 7	0.083 8	0.000 7	0.007 8	0.000 2	0.000 3	0.880 4	0.000 1	0.377 11	0.000 1	0.263 3	0.565 2	0.000 1	0.608 8	0.000 1	0.000 1	0.304 7	0.009 8	0.924 3	0.000 8	0.000 8	0.000 1	0.000 6	0.000 1	0.128 3	0.584 3	0.475 7	0.412 7	0.076 10	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 5	0.125 3	0.000 4	0.441 6	0.000 1
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
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 10	0.265 13	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 12	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 11	0.028 11	0.321 10	0.363 15	0.000 1	0.708 14	0.858 14	0.746 10	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 10	0.077 16	0.720 15	0.610 15	0.103 5	0.629 12	0.437 16	0.626 11	0.446 13	0.702 6	0.190 11	0.005 1	0.058 15	0.322 12	0.702 15	0.244 14	0.768 13	0.000 4	0.134 10	0.552 13	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 6	0.658 10	0.566 14	0.323 14	0.525 16	0.229 12	0.179 8	0.467 16	0.154 15	0.000 3	0.002 5	0.000 1	0.051 1	0.000 4	0.127 2	0.703 11	0.000 1	0.000 10	0.216 1	0.112 15	0.358 11	0.547 1	0.187 7	0.092 15	0.156 16	0.055 10	0.296 14	0.252 10	0.143 5	0.000 5	0.014 13	0.398 9	0.000 1	0.028 15	0.173 7	0.000 14	0.265 15	0.348 14	0.415 15	0.179 6	0.019 15	0.218 8	0.000 1	0.597 7	0.274 16	0.565 12	0.000 1	0.012 5	0.000 11	0.039 15	0.022 3	0.000 9	0.117 14	0.000 11	0.000 7	0.000 11	0.000 2	0.000 3	0.324 15	0.000 1	0.384 9	0.000 1	0.000 12	0.251 16	0.000 1	0.566 11	0.000 1	0.000 1	0.066 12	0.404 1	0.886 12	0.199 2	0.000 8	0.000 1	0.059 3	0.000 1	0.136 1	0.540 6	0.127 16	0.295 10	0.085 7	0.143 7	0.514 6	0.413 14	0.000 12	0.000 1	0.498 9	0.000 2	0.000 2	0.000 11	0.623 11	0.000 2	0.000 1	0.000 1	0.132 14	0.000 8	0.000 4	0.000 10	0.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
OctFormer ScanNet200	0.326 12	0.539 9	0.265 11	0.131 11	0.806 10	0.670 12	0.943 11	0.535 10	0.662 6	0.705 15	0.423 8	0.407 5	0.505 10	0.003 9	0.765 12	0.582 8	0.686 14	0.227 15	0.680 8	0.943 9	0.601 3	0.854 10	0.892 5	0.335 6	0.417 16	0.357 9	0.724 9	0.453 8	0.632 8	0.596 5	0.432 3	0.783 10	0.512 15	0.021 13	0.244 14	0.637 1	0.000 1	0.787 10	0.873 11	0.743 11	0.000 16	0.000 7	0.534 9	0.110 3	0.499 7	0.289 9	0.626 6	0.620 13	0.168 16	0.204 2	0.849 7	0.679 7	0.117 4	0.633 11	0.684 3	0.650 7	0.552 5	0.684 10	0.312 3	0.000 3	0.175 10	0.429 8	0.865 5	0.413 4	0.837 11	0.000 4	0.145 7	0.626 9	0.451 8	0.487 9	0.513 2	0.000 1	0.529 7	0.613 12	0.000 4	0.033 6	0.000 1	0.000 6	0.828 6	0.871 4	0.622 8	0.587 9	0.411 7	0.137 10	0.645 13	0.343 11	0.000 3	0.000 7	0.000 1	0.022 10	0.000 4	0.026 16	0.829 10	0.000 1	0.022 8	0.089 6	0.842 4	0.253 14	0.318 11	0.296 3	0.178 9	0.291 3	0.224 2	0.584 2	0.200 13	0.132 9	0.000 5	0.128 10	0.227 15	0.000 1	0.230 11	0.047 11	0.149 9	0.331 10	0.412 9	0.618 8	0.164 8	0.102 9	0.522 1	0.000 1	0.655 4	0.378 11	0.469 14	0.000 1	0.000 8	0.000 11	0.105 9	0.000 6	0.000 9	0.483 3	0.000 11	0.000 7	0.028 7	0.000 2	0.000 3	0.906 1	0.000 1	0.339 14	0.000 1	0.000 12	0.457 9	0.000 1	0.612 7	0.000 1	0.000 1	0.408 3	0.000 14	0.900 9	0.000 8	0.000 8	0.000 1	0.029 4	0.000 1	0.074 15	0.455 14	0.479 6	0.427 6	0.079 9	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 7	0.000 2	0.000 1	0.000 1	0.138 12	0.000 8	0.000 4	0.000 10	0.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 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 11	0.515 9	0.023 7	0.764 13	0.523 12	0.707 13	0.264 9	0.633 13	0.922 12	0.507 13	0.886 1	0.804 14	0.179 14	0.436 15	0.300 13	0.656 15	0.529 2	0.501 14	0.394 12	0.296 15	0.820 6	0.603 10	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 12	0.063 14	0.463 13	0.254 12	0.332 15	0.649 12	0.220 11	0.100 12	0.729 13	0.613 14	0.071 12	0.582 14	0.628 8	0.702 4	0.424 14	0.749 2	0.137 14	0.000 3	0.142 13	0.360 11	0.863 6	0.305 13	0.877 9	0.000 4	0.173 3	0.606 12	0.337 13	0.478 10	0.154 14	0.000 1	0.253 13	0.664 7	0.000 4	0.000 9	0.000 1	0.000 6	0.626 12	0.782 12	0.302 15	0.602 6	0.185 13	0.282 6	0.651 11	0.317 12	0.000 3	0.000 7	0.000 1	0.022 10	0.000 4	0.154 1	0.876 8	0.000 1	0.014 9	0.063 9	0.029 16	0.553 7	0.467 3	0.084 11	0.124 13	0.157 15	0.049 12	0.373 13	0.252 10	0.097 13	0.000 5	0.219 6	0.542 3	0.000 1	0.392 6	0.172 8	0.000 14	0.339 9	0.417 8	0.533 13	0.093 14	0.115 8	0.195 9	0.000 1	0.516 10	0.288 15	0.741 5	0.000 1	0.001 7	0.233 5	0.056 13	0.000 6	0.159 5	0.334 8	0.077 9	0.000 7	0.000 11	0.000 2	0.000 3	0.749 12	0.000 1	0.411 8	0.000 1	0.008 11	0.452 10	0.000 1	0.595 9	0.000 1	0.000 1	0.220 9	0.006 9	0.894 11	0.006 6	0.000 8	0.000 1	0.000 6	0.000 1	0.112 5	0.504 9	0.404 10	0.551 1	0.093 5	0.129 16	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 9	0.000 8	0.000 4	0.512 5	0.000 1

GSTran	0.339 7	0.536 10	0.273 6	0.169 7	0.811 7	0.690 5	0.949 5	0.506 12	0.690 4	0.765 2	0.397 12	0.235 15	0.480 13	0.014 8	0.788 9	0.593 7	0.746 8	0.282 5	0.696 5	0.913 13	0.538 7	0.853 11	0.889 6	0.286 11	0.670 3	0.310 12	0.682 13	0.445 9	0.638 5	0.598 4	0.358 10	0.841 2	0.643 4	0.061 8	0.373 7	0.614 4	0.000 1	0.786 11	0.876 8	0.754 8	0.357 1	0.000 7	0.535 8	0.071 12	0.491 9	0.369 4	0.487 11	0.698 11	0.317 3	0.202 3	0.659 16	0.666 8	0.086 8	0.832 1	0.461 15	0.597 13	0.455 12	0.731 3	0.156 13	0.000 3	0.316 1	0.318 13	0.784 13	0.348 10	0.896 7	0.000 4	0.084 14	0.648 8	0.514 5	0.470 11	0.368 6	0.000 1	0.441 8	0.705 6	0.000 4	0.079 3	0.000 1	0.021 3	0.872 2	0.872 3	0.621 9	0.589 8	0.144 15	0.129 11	0.648 12	0.459 6	0.000 3	0.000 7	0.000 1	0.022 10	0.289 2	0.096 5	0.667 13	0.000 1	0.000 10	0.000 10	0.834 5	0.682 5	0.178 16	0.033 15	0.256 5	0.196 8	0.000 14	0.473 6	0.279 8	0.079 15	0.008 4	0.495 1	0.425 8	0.000 1	0.228 12	0.009 14	0.564 3	0.410 4	0.366 13	0.665 6	0.161 10	0.615 2	0.365 3	0.000 1	0.609 6	0.386 10	0.681 7	0.000 1	0.000 8	0.199 7	0.093 11	0.497 1	0.109 7	0.252 11	0.161 4	0.118 5	0.000 11	0.000 2	0.000 3	0.857 6	0.000 1	0.495 1	0.000 1	0.162 7	0.412 12	0.000 1	0.563 12	0.000 1	0.000 1	0.000 16	0.012 7	0.877 13	0.004 7	0.000 8	0.000 1	0.002 5	0.000 1	0.109 8	0.458 13	0.358 12	0.246 13	0.060 13	0.139 10	0.466 9	0.803 1	0.097 2	0.000 1	0.517 8	0.000 2	0.000 2	0.060 10	0.413 14	0.000 2	0.000 1	0.000 1	0.183 4	0.024 6	0.000 4	0.297 7	0.000 1

PPT-SpUNet-F.T.	0.332 11	0.556 5	0.270 7	0.123 13	0.816 6	0.682 9	0.946 8	0.549 7	0.657 10	0.756 7	0.459 6	0.376 9	0.550 8	0.001 10	0.807 4	0.616 4	0.727 11	0.267 7	0.691 6	0.942 10	0.530 10	0.872 5	0.874 8	0.330 8	0.542 13	0.374 7	0.792 4	0.400 13	0.673 4	0.572 7	0.433 2	0.793 8	0.623 8	0.008 15	0.351 9	0.594 7	0.000 1	0.783 12	0.876 8	0.833 4	0.213 6	0.000 7	0.537 7	0.091 6	0.519 5	0.304 7	0.620 7	0.942 1	0.264 5	0.124 9	0.855 6	0.695 5	0.086 8	0.646 10	0.506 14	0.658 6	0.535 6	0.715 4	0.314 2	0.000 3	0.241 6	0.608 2	0.897 2	0.359 8	0.858 10	0.000 4	0.076 15	0.611 11	0.392 11	0.509 7	0.378 5	0.000 1	0.579 4	0.565 15	0.000 4	0.000 9	0.000 1	0.000 6	0.755 8	0.806 11	0.661 4	0.572 13	0.350 10	0.181 7	0.660 10	0.300 13	0.000 3	0.000 7	0.000 1	0.023 9	0.000 4	0.042 15	0.930 4	0.000 1	0.000 10	0.077 7	0.584 8	0.392 10	0.339 9	0.185 8	0.171 10	0.308 2	0.006 13	0.563 3	0.256 9	0.150 4	0.000 5	0.002 15	0.345 13	0.000 1	0.045 13	0.197 4	0.063 10	0.323 11	0.453 3	0.600 9	0.163 9	0.037 14	0.349 4	0.000 1	0.672 3	0.679 4	0.753 3	0.000 1	0.000 8	0.000 11	0.117 6	0.000 6	0.000 9	0.291 10	0.000 11	0.000 7	0.039 6	0.000 2	0.000 3	0.899 2	0.000 1	0.374 12	0.000 1	0.000 12	0.545 4	0.000 1	0.634 5	0.000 1	0.000 1	0.074 11	0.223 4	0.914 7	0.000 8	0.021 6	0.000 1	0.000 6	0.000 1	0.112 5	0.498 11	0.649 1	0.383 9	0.095 2	0.135 13	0.449 10	0.432 11	0.008 9	0.000 1	0.518 7	0.000 2	0.000 2	0.000 11	0.796 4	0.000 2	0.000 1	0.000 1	0.138 12	0.000 8	0.000 4	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
L3DETR-ScanNet_200	0.336 9	0.533 12	0.279 5	0.155 9	0.801 11	0.689 6	0.946 8	0.539 9	0.660 9	0.759 5	0.380 13	0.333 12	0.583 3	0.000 11	0.788 9	0.529 11	0.740 9	0.261 11	0.679 9	0.940 11	0.525 11	0.860 8	0.883 7	0.226 12	0.613 8	0.397 5	0.720 10	0.512 4	0.565 11	0.620 3	0.417 6	0.775 12	0.629 7	0.158 2	0.298 11	0.579 9	0.000 1	0.835 3	0.883 6	0.927 1	0.114 10	0.079 4	0.511 10	0.073 10	0.508 6	0.312 6	0.629 5	0.861 5	0.192 14	0.098 14	0.908 3	0.636 12	0.032 16	0.563 16	0.514 13	0.664 5	0.505 10	0.697 7	0.225 8	0.000 3	0.264 3	0.411 9	0.860 8	0.321 12	0.960 2	0.058 3	0.109 11	0.776 3	0.526 4	0.557 2	0.303 9	0.000 1	0.339 11	0.712 5	0.000 4	0.014 7	0.000 1	0.000 6	0.638 11	0.856 5	0.641 7	0.579 11	0.107 16	0.119 12	0.661 9	0.416 7	0.000 3	0.000 7	0.000 1	0.007 16	0.000 4	0.067 9	0.910 5	0.000 1	0.000 10	0.000 10	0.463 10	0.448 8	0.294 14	0.324 2	0.293 2	0.211 7	0.108 8	0.448 9	0.068 16	0.141 7	0.000 5	0.330 3	0.699 1	0.000 1	0.256 9	0.192 5	0.000 14	0.355 8	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 8	0.174 9	0.117 6	0.000 6	0.122 6	0.515 2	0.104 6	0.259 2	0.312 3	0.000 2	0.000 3	0.765 11	0.000 1	0.369 13	0.000 1	0.183 6	0.422 11	0.000 1	0.646 3	0.000 1	0.000 1	0.565 2	0.001 13	0.125 16	0.010 5	0.002 7	0.000 1	0.487 1	0.000 1	0.075 14	0.548 4	0.420 9	0.233 14	0.082 8	0.138 12	0.430 11	0.427 12	0.000 12	0.000 1	0.549 6	0.000 2	0.000 2	0.074 9	0.409 15	0.000 2	0.000 1	0.000 1	0.152 7	0.051 4	0.000 4	0.598 4	0.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OA-CNN-L_ScanNet200	0.333 10	0.558 4	0.269 9	0.124 12	0.821 5	0.703 4	0.946 8	0.569 4	0.662 6	0.748 10	0.487 3	0.455 3	0.572 6	0.000 11	0.789 8	0.534 10	0.736 10	0.271 6	0.713 4	0.949 7	0.498 14	0.877 3	0.860 9	0.332 7	0.706 1	0.474 2	0.788 6	0.406 12	0.637 6	0.495 10	0.355 11	0.805 7	0.592 13	0.015 14	0.396 5	0.602 6	0.000 1	0.799 9	0.876 8	0.713 13	0.276 4	0.000 7	0.493 11	0.080 8	0.448 14	0.363 5	0.661 4	0.833 6	0.262 6	0.125 8	0.823 10	0.665 9	0.076 10	0.720 8	0.557 9	0.637 9	0.517 9	0.672 11	0.227 7	0.000 3	0.158 11	0.496 5	0.843 11	0.352 9	0.835 12	0.000 4	0.103 12	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 5	0.806 7	0.836 8	0.621 9	0.591 7	0.373 8	0.314 5	0.668 8	0.398 9	0.003 2	0.000 7	0.000 1	0.016 15	0.024 3	0.043 14	0.906 6	0.000 1	0.052 5	0.000 10	0.384 11	0.330 12	0.342 7	0.100 10	0.223 7	0.183 12	0.112 6	0.476 5	0.313 7	0.130 10	0.196 3	0.112 11	0.370 12	0.000 1	0.234 10	0.071 9	0.160 8	0.403 5	0.398 11	0.492 14	0.197 5	0.076 11	0.272 5	0.000 1	0.200 16	0.560 8	0.735 6	0.000 1	0.000 8	0.000 11	0.110 8	0.002 5	0.021 8	0.412 6	0.000 11	0.000 7	0.000 11	0.000 2	0.000 3	0.794 10	0.000 1	0.445 6	0.000 1	0.022 10	0.509 6	0.000 1	0.517 14	0.000 1	0.000 1	0.001 15	0.245 3	0.915 6	0.024 4	0.089 5	0.000 1	0.262 2	0.000 1	0.103 11	0.524 8	0.392 11	0.515 2	0.013 16	0.251 4	0.411 12	0.662 3	0.001 11	0.000 1	0.473 12	0.000 2	0.000 2	0.150 5	0.699 9	0.000 2	0.000 1	0.000 1	0.166 6	0.000 8	0.024 3	0.000 10	0.000 1

DITR	0.409 2	0.616 1	0.351 1	0.215 3	0.831 3	0.791 1	0.947 6	0.619 1	0.730 2	0.762 3	0.494 2	0.571 1	0.597 2	0.000 11	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 10	0.000 1	0.913 1	0.878 7	0.687 14	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 6	0.938 1	0.752 2	0.118 3	0.827 2	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 6	0.814 1	0.656 1	0.524 6	0.491 3	0.000 1	0.743 1	0.752 2	0.000 4	0.000 9	0.000 1	0.399 1	0.865 3	0.953 1	0.833 1	0.694 2	0.444 6	0.000 15	0.688 4	0.609 2	0.000 3	0.053 2	0.000 1	0.022 10	0.000 4	0.053 13	0.940 3	0.000 1	0.186 1	0.093 5	0.854 3	0.877 1	0.534 2	0.404 1	0.270 3	0.191 10	0.198 4	0.461 8	0.375 1	0.152 3	0.921 1	0.132 9	0.235 14	0.000 1	0.617 1	0.330 1	0.896 1	0.399 6	0.431 5	0.597 10	0.759 1	0.554 4	0.400 2	0.000 1	0.559 9	0.699 1	0.852 2	0.000 1	0.000 8	0.091 10	0.385 1	0.000 6	0.000 9	0.478 4	0.077 9	0.000 7	0.140 4	0.000 2	0.000 3	0.670 13	0.000 1	0.452 5	0.000 1	0.263 3	0.361 13	0.000 1	0.643 4	0.000 1	0.000 1	0.357 5	0.005 10	0.928 2	0.362 1	0.496 1	0.000 1	0.000 6	0.000 1	0.072 16	0.585 2	0.587 3	0.476 4	0.037 15	0.191 5	0.410 13	0.629 6	0.118 1	0.000 1	0.479 11	0.000 2	0.000 2	0.107 8	0.839 3	0.000 2	0.000 1	0.000 1	0.139 11	0.036 5	0.000 4	0.247 9	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 15	0.816 15	0.770 16	0.186 13	0.634 5	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 13	0.000 1	0.484 16	0.832 16	0.766 7	0.052 14	0.000 7	0.413 15	0.059 15	0.418 15	0.222 15	0.318 16	0.609 14	0.206 13	0.112 10	0.743 12	0.625 13	0.076 10	0.579 15	0.548 11	0.590 15	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 4	0.142 8	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 6	0.553 13	0.557 15	0.373 12	0.545 15	0.164 14	0.014 14	0.547 15	0.174 14	0.000 3	0.002 5	0.000 1	0.037 4	0.000 4	0.063 10	0.664 14	0.000 1	0.000 10	0.130 2	0.170 13	0.152 16	0.335 10	0.079 12	0.110 14	0.175 13	0.098 9	0.175 16	0.166 14	0.045 16	0.207 2	0.014 13	0.465 5	0.000 1	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 10	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 2	0.000 9	0.105 15	0.000 11	0.000 7	0.000 11	0.000 2	0.000 3	0.398 14	0.000 1	0.308 16	0.000 1	0.000 12	0.319 14	0.000 1	0.543 13	0.000 1	0.000 1	0.062 13	0.004 11	0.862 15	0.000 8	0.000 8	0.000 1	0.000 6	0.000 1	0.123 4	0.316 15	0.225 14	0.250 12	0.094 4	0.180 6	0.332 14	0.441 10	0.000 12	0.000 1	0.310 16	0.000 2	0.000 2	0.000 11	0.592 12	0.000 2	0.000 1	0.000 1	0.203 2	0.000 8	0.000 4	0.000 10	0.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34D	0.253 15	0.463 15	0.154 16	0.102 15	0.771 15	0.650 15	0.932 14	0.483 15	0.571 15	0.710 14	0.331 15	0.250 14	0.492 12	0.044 4	0.703 15	0.419 16	0.606 16	0.227 15	0.621 15	0.865 16	0.531 9	0.771 16	0.813 13	0.291 9	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 12	0.027 12	0.293 12	0.310 16	0.000 1	0.757 13	0.858 14	0.737 12	0.150 8	0.164 1	0.368 16	0.084 7	0.381 16	0.142 16	0.357 14	0.720 10	0.214 12	0.092 15	0.724 14	0.596 16	0.056 13	0.655 9	0.525 12	0.581 16	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 4	0.159 5	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 4	0.663 9	0.466 16	0.265 16	0.582 10	0.337 11	0.016 13	0.559 14	0.084 16	0.000 3	0.000 7	0.000 1	0.036 5	0.000 4	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 14	0.068 16	0.159 14	0.117 5	0.284 15	0.111 15	0.094 14	0.000 5	0.000 16	0.197 16	0.000 1	0.044 14	0.013 13	0.002 13	0.228 16	0.307 16	0.588 11	0.025 16	0.545 5	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 6	0.000 9	0.097 16	0.000 11	0.000 7	0.005 9	0.000 2	0.000 3	0.096 16	0.000 1	0.334 15	0.000 1	0.000 12	0.274 15	0.000 1	0.513 15	0.000 1	0.000 1	0.280 8	0.194 5	0.897 10	0.000 8	0.000 8	0.000 1	0.000 6	0.000 1	0.108 10	0.279 16	0.189 15	0.141 16	0.059 14	0.272 2	0.307 15	0.445 9	0.003 10	0.000 1	0.353 15	0.000 2	0.026 1	0.000 11	0.581 13	0.001 1	0.000 1	0.000 1	0.093 16	0.002 7	0.000 4	0.000 10	0.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IMFSegNet	0.337 8	0.535 11	0.266 10	0.169 8	0.810 8	0.715 2	0.947 6	0.545 8	0.675 5	0.759 5	0.418 9	0.406 6	0.493 11	0.000 11	0.803 7	0.571 9	0.755 5	0.264 9	0.644 12	0.953 5	0.366 16	0.840 13	0.831 12	0.357 3	0.564 9	0.317 11	0.699 11	0.422 11	0.633 7	0.509 9	0.424 5	0.824 4	0.637 5	0.001 16	0.382 6	0.383 14	0.000 1	0.810 8	0.884 5	0.678 15	0.311 3	0.000 7	0.469 13	0.072 11	0.527 3	0.300 8	0.576 9	0.769 7	0.177 15	0.181 5	0.829 8	0.642 11	0.095 6	0.791 5	0.655 6	0.645 8	0.524 8	0.692 8	0.208 10	0.000 3	0.264 3	0.398 10	0.867 4	0.343 11	0.915 5	0.000 4	0.036 16	0.551 14	0.442 9	0.432 13	0.568 1	0.000 1	0.359 10	0.660 9	0.000 4	0.040 5	0.000 1	0.000 6	0.853 4	0.821 10	0.469 11	0.546 14	0.361 9	0.000 15	0.683 5	0.373 10	0.000 3	0.000 7	0.000 1	0.038 3	0.000 4	0.054 12	0.472 16	0.000 1	0.026 7	0.000 10	0.868 2	0.708 4	0.341 8	0.263 5	0.170 11	0.263 4	0.109 7	0.426 10	0.228 12	0.143 5	0.000 5	0.251 5	0.442 7	0.000 1	0.447 5	0.187 6	0.544 4	0.311 13	0.396 12	0.728 3	0.211 3	0.066 12	0.147 12	0.000 1	0.505 12	0.378 11	0.743 4	0.000 1	0.000 8	0.204 6	0.118 4	0.000 6	0.000 9	0.297 9	0.122 5	0.133 4	0.004 10	0.032 1	0.667 1	0.839 9	0.000 1	0.380 10	0.000 1	0.051 9	0.492 7	0.000 1	0.572 10	0.000 1	0.000 1	0.196 10	0.004 11	0.910 8	0.000 8	0.000 8	0.000 1	0.000 6	0.000 1	0.112 5	0.544 5	0.497 4	0.152 15	0.095 2	0.132 15	0.277 16	0.634 5	0.031 4	0.000 1	0.592 3	0.000 2	0.000 2	0.119 7	0.786 5	0.000 2	0.000 1	0.000 1	0.126 15	0.177 2	0.094 2	0.274 8	0.000 1

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

Method	avg	chair	table	door	couch	cabinet	shelf	desk	office chair	bed	pillow	sink	picture	window	toilet	bookshelf	monitor	curtain	book	armchair	coffee table	box	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	ottoman	bench	board	mirror	copier	basket	sofa chair	file cabinet	fan	laptop	shower	paper	person	paper towel dispenser	oven	blinds	rack	plate	blackboard	piano	suitcase	rail	radiator	recycling bin	wardrobe	soap dispenser	bucket	clock	stand	light	laundry basket	pipe	clothes dryer	guitar	toilet paper holder	seat	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	fireplace	soap dish	kitchen counter	mini fridge	fire extinguisher	ball	hat	shower curtain rod	water cooler	paper cutter	tray	shower door	pillar	ledge	toilet seat cover dispenser	cart	tissue box	light switch	crate	power outlet	decoration	projector	closet door	vacuum cleaner	candle	stuffed animal	headphones	dish rack	broom	guitar case	range hood	dustpan	water bottle	vent	mailbox	bowl	paper bag	alarm clock	music stand	projector screen	laundry detergent	bathroom counter	bathroom vanity	laundry hamper	bathroom stall door	trash bin	dumbbell	stair rail	tube	bathroom cabinet	cd case	closet rod	coffee kettle	shower head	keyboard piano	case of water bottles	coat rack	storage organizer	folded chair	fire alarm	power strip	calendar	poster	potted plant	luggage	mattress

LGround Inst.	0.246 3	0.413 3	0.170 3	0.130 3	0.754 3	0.541 3	0.682 4	0.903 1	0.264 4	0.164 2	0.234 3	0.000 3	0.681 4	0.452 3	0.464 5	0.541 4	0.399 3	1.000 1	0.637 2	0.772 3	0.588 4	0.190 2	0.589 5	0.081 3	0.857 3	0.426 3	0.373 3	0.318 3	0.135 2	0.690 2	0.653 4	0.000 3	0.159 4	0.500 2	0.000 3	0.581 3	0.387 4	1.000 1	0.046 3	0.000 3	0.402 3	0.003 5	0.455 5	0.196 4	0.571 2	1.000 1	0.270 3	0.003 5	0.530 5	0.748 3	0.000 2	0.744 4	0.575 3	0.511 3	0.112 3	0.815 2	0.067 3	0.000 3	0.400 3	0.167 3	0.667 3	0.241 2	1.000 1	0.000 1	0.208 4	0.660 3	0.125 4	0.317 2	0.000 5	0.000 2	0.100 2	0.561 4	0.000 3	0.000 2	0.000 1	0.000 2	1.000 1	0.500 1	0.344 2	0.568 4	0.167 3	0.000 3	0.706 3	0.068 3	0.000 2	0.000 1	0.000 3	0.063 3	0.000 1	0.000 3	0.056 4	0.000 3	0.000 3	0.500 2	0.000 2	0.143 5	0.017 4	0.125 3	0.097 2	0.164 3	0.000 1	0.582 4	0.400 1	0.000 2	0.000 1	0.000 4	0.083 4	0.000 1	0.000 3	0.000 3	0.000 1	0.025 3	0.156 3	0.533 3	0.250 2	0.200 2	0.500 1	0.000 1	1.000 1	0.333 4	1.000 1	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.333 2	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.400 3	0.000 1	0.364 2	0.000 1	0.000 3	0.500 3	0.000 1	0.511 4	0.000 1	0.000 1	0.286 3	0.333 2	0.000 5	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.034 3	0.111 5	0.000 3	0.333 4	0.031 5	0.000 4	0.750 1	0.125 2	0.000 2	0.000 1	0.151 2	0.000 1	0.000 1	0.000 2	0.500 2	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.
CSC-Pretrain Inst.	0.209 4	0.361 5	0.157 4	0.085 4	0.700 5	0.248 5	0.634 5	0.776 5	0.322 2	0.135 5	0.103 5	0.000 3	0.524 5	0.364 5	0.618 2	0.592 3	0.381 5	0.997 3	0.589 4	0.747 4	0.340 5	0.109 5	0.768 2	0.059 5	0.702 5	0.448 2	0.188 5	0.149 5	0.091 5	0.636 3	0.573 5	0.000 3	0.246 3	0.500 2	0.000 3	0.450 5	0.405 3	0.667 4	0.006 5	0.000 3	0.356 4	0.007 3	0.506 2	0.420 3	0.340 3	0.667 5	0.294 2	0.004 4	0.571 4	0.748 2	0.000 2	1.000 1	0.573 4	0.502 4	0.094 4	0.807 3	0.000 4	0.000 3	0.400 3	0.000 5	0.278 5	0.228 3	1.000 1	0.000 1	0.115 5	0.432 4	0.198 3	0.050 5	0.125 2	0.000 2	0.000 5	0.573 3	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.125 4	0.312 4	0.610 3	0.221 1	0.000 3	0.667 4	0.050 4	0.000 2	0.000 1	0.000 3	0.032 5	0.000 1	0.000 3	0.083 3	0.000 3	0.000 3	0.000 4	0.000 2	0.220 4	0.000 5	0.125 3	0.000 5	0.111 5	0.000 1	0.667 2	0.200 3	0.000 2	0.000 1	0.000 4	0.110 3	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.053 5	0.500 4	0.000 5	0.000 4	0.500 1	0.000 1	0.500 4	0.333 4	0.500 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	0.600 2	0.000 1	0.364 2	0.000 1	0.000 3	0.750 1	0.000 1	0.833 1	0.000 1	0.000 1	0.143 5	0.000 5	0.396 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.021 5	0.221 4	0.000 3	0.093 5	0.055 4	0.451 2	0.677 2	0.125 2	0.000 2	0.000 1	0.028 4	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.050 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
Minkowski 34D Inst.	0.203 5	0.369 4	0.134 5	0.078 5	0.706 4	0.382 4	0.693 3	0.845 3	0.221 5	0.150 4	0.158 4	0.000 3	0.746 2	0.369 4	0.545 4	0.595 2	0.387 4	0.997 3	0.413 5	0.720 5	0.636 2	0.165 3	0.732 3	0.070 4	0.851 4	0.402 4	0.251 4	0.313 4	0.123 4	0.583 4	0.696 3	0.000 3	0.051 5	0.500 2	0.000 3	0.500 4	0.372 5	0.667 4	0.009 4	0.000 3	0.307 5	0.003 4	0.479 4	0.107 5	0.226 5	0.903 4	0.109 5	0.031 3	0.981 3	0.726 5	0.000 2	0.522 5	0.669 2	0.282 5	0.052 5	0.778 5	0.000 4	0.000 3	0.400 3	0.074 4	0.333 4	0.218 4	1.000 1	0.000 1	0.250 3	0.406 5	0.118 5	0.317 2	0.100 3	0.000 2	0.191 1	0.596 2	0.000 3	0.000 2	0.000 1	0.000 2	0.000 3	0.500 1	0.178 5	0.701 2	0.000 5	0.000 3	0.522 5	0.018 5	0.000 2	0.000 1	0.000 3	0.060 4	0.000 1	0.000 3	0.033 5	0.000 3	0.000 3	0.000 4	0.000 2	0.281 3	0.100 2	0.000 5	0.090 4	0.133 4	0.000 1	0.422 5	0.050 4	0.000 2	0.000 1	0.200 3	0.000 5	0.000 1	0.000 3	0.000 3	0.000 1	0.000 4	0.123 4	0.677 2	0.021 4	0.000 4	0.500 1	0.000 1	0.500 4	0.442 2	0.125 5	0.000 1	0.000 3	0.000 3	0.000 3	0.000 2	0.000 3	0.056 4	0.000 3	0.000 3	0.000 3	0.000 3	0.000 1	0.200 5	0.000 1	0.143 5	0.000 1	0.000 3	0.250 5	0.000 1	0.511 4	0.000 1	0.000 1	0.286 3	0.083 4	0.396 2	0.000 1	0.000 3	0.000 1	0.000 2	0.000 1	0.025 4	0.300 2	0.000 3	0.371 3	0.070 2	0.000 4	0.385 3	0.000 5	0.000 2	0.000 1	0.000 5	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	0.000 2	0.200 2	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	0.320 2	0.501 2	0.264 2	0.164 2	0.841 1	0.679 1	0.716 2	0.879 2	0.280 3	0.192 1	0.634 1	0.231 1	0.733 3	0.459 2	0.565 3	0.498 5	0.560 2	1.000 1	0.686 1	0.890 2	0.708 1	0.123 4	0.820 1	0.152 2	0.967 1	0.456 1	0.458 2	0.387 2	0.194 1	0.435 5	0.906 1	0.077 1	0.396 2	0.509 1	0.217 2	0.715 1	0.619 2	1.000 1	0.099 2	0.792 1	0.513 2	0.062 2	0.506 3	0.549 1	0.605 1	1.000 1	0.123 4	0.106 1	1.000 1	0.744 4	0.000 2	1.000 1	0.504 5	0.525 2	0.185 2	0.790 4	0.101 2	0.008 2	0.587 2	0.356 1	0.817 1	0.083 5	1.000 1	0.000 1	0.621 1	0.842 1	0.415 1	0.268 4	0.083 4	0.000 2	0.098 3	0.881 1	0.125 2	0.000 2	0.000 1	0.000 2	0.000 3	0.125 4	0.332 3	0.448 5	0.202 2	0.196 1	0.798 1	0.264 2	0.000 2	0.000 1	0.017 2	0.233 2	0.000 1	0.063 1	0.333 2	0.038 1	0.111 1	0.250 3	0.000 2	0.516 1	0.208 1	0.470 2	0.094 3	0.218 1	0.000 1	0.667 2	0.033 5	0.000 2	0.000 1	0.400 1	0.156 2	0.000 1	0.267 1	0.226 1	0.000 1	0.104 2	0.159 2	0.299 5	0.095 3	0.458 1	0.500 1	0.000 1	1.000 1	0.472 1	0.792 3	0.000 1	0.022 1	0.061 2	0.250 1	0.008 1	0.250 2	0.333 2	0.143 2	0.396 2	0.049 2	0.012 1	0.000 1	0.283 4	0.000 1	0.241 4	0.000 1	0.101 2	0.331 4	0.000 1	0.629 3	0.000 1	0.000 1	0.857 2	0.222 3	0.677 1	0.000 1	0.003 2	0.000 1	0.000 2	0.000 1	0.076 2	0.252 3	0.400 1	0.431 2	0.061 3	0.328 3	0.331 4	0.500 1	0.000 2	0.000 1	0.167 1	0.000 1	0.000 1	0.000 2	0.500 2	0.000 1	0.000 2	1.000 1	0.542 1	0.000 2	0.063 1	0.000 2	0.000 1
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Mask3D Scannet200	0.388 1	0.542 1	0.357 1	0.237 1	0.808 2	0.676 2	0.741 1	0.832 4	0.496 1	0.151 3	0.628 2	0.021 2	0.955 1	0.578 1	0.753 1	0.612 1	0.591 1	0.822 5	0.609 3	0.926 1	0.614 3	0.291 1	0.725 4	0.163 1	0.890 2	0.380 5	0.615 1	0.517 1	0.130 3	0.806 1	0.857 2	0.024 2	0.511 1	0.412 5	0.226 1	0.597 2	0.756 1	1.000 1	0.111 1	0.792 1	0.736 1	0.091 1	0.610 1	0.527 2	0.323 4	1.000 1	0.504 1	0.063 2	1.000 1	0.853 1	0.010 1	0.974 3	0.839 1	0.667 1	0.301 1	0.883 1	0.266 1	0.039 1	0.640 1	0.311 2	0.739 2	0.463 1	1.000 1	0.000 1	0.287 2	0.715 2	0.313 2	0.600 1	1.000 1	0.027 1	0.076 4	0.502 5	0.500 1	0.409 1	0.000 1	0.194 1	0.125 2	0.500 1	0.491 1	0.748 1	0.050 4	0.042 2	0.776 2	0.352 1	0.008 1	0.000 1	0.033 1	0.254 1	0.000 1	0.005 2	0.552 1	0.008 2	0.020 2	0.750 1	0.500 1	0.409 2	0.065 3	0.511 1	0.107 1	0.178 2	0.000 1	1.000 1	0.400 1	0.016 1	0.000 1	0.400 1	0.571 1	0.000 1	0.060 2	0.044 2	0.000 1	0.514 1	0.278 1	1.000 1	0.258 1	0.017 3	0.125 5	0.000 1	0.792 3	0.399 3	1.000 1	0.000 1	0.013 2	0.265 1	0.018 2	0.000 2	1.000 1	0.335 1	0.381 1	0.500 1	0.250 1	0.004 2	0.000 1	0.727 1	0.000 1	0.497 1	0.000 1	0.188 1	0.677 2	0.000 1	0.708 2	0.000 1	0.000 1	0.945 1	0.391 1	0.123 4	0.000 1	0.028 1	0.000 1	1.000 1	0.000 1	0.099 1	0.451 1	0.400 1	0.668 1	0.573 1	0.606 1	0.077 5	0.003 4	0.004 1	0.000 1	0.042 3	0.000 1	0.000 1	1.000 1	1.000 1	0.000 1	0.042 1	0.000 2	0.200 2	0.302 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 12	0.856 14	0.555 15	0.943 1	0.660 24	0.735 2	0.979 1	0.606 7	0.492 1	0.792 4	0.934 3	0.841 2	0.819 5	0.716 8	0.947 10	0.906 1	0.822 1

PTv3 ScanNet	0.794 2	0.941 3	0.813 20	0.851 9	0.782 6	0.890 3	0.597 1	0.916 5	0.696 9	0.713 5	0.979 1	0.635 2	0.384 3	0.793 3	0.907 10	0.821 5	0.790 33	0.696 13	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 39	0.852 2	0.889 1	0.774 9	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 9	0.824 2	0.749 1	0.948 9	0.887 7	0.771 11

PonderV2	0.785 4	0.978 1	0.800 29	0.833 26	0.788 4	0.853 19	0.545 19	0.910 8	0.713 2	0.705 6	0.979 1	0.596 9	0.390 2	0.769 15	0.832 44	0.821 5	0.792 32	0.730 2	0.975 1	0.897 5	0.785 6
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Mix3D	0.781 5	0.964 2	0.855 1	0.843 18	0.781 7	0.858 13	0.575 7	0.831 36	0.685 15	0.714 4	0.979 1	0.594 10	0.310 29	0.801 2	0.892 18	0.841 2	0.819 5	0.723 5	0.940 15	0.887 7	0.725 27
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 21	0.818 15	0.836 23	0.790 3	0.875 5	0.576 6	0.905 9	0.704 6	0.739 1	0.969 11	0.611 3	0.349 11	0.756 25	0.958 1	0.702 48	0.805 16	0.708 9	0.916 35	0.898 4	0.801 3

TTT-KD	0.773 7	0.646 94	0.818 15	0.809 38	0.774 9	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 11	0.912 8	0.838 4	0.823 3	0.694 14	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 11	0.840 33	0.564 11	0.900 11	0.686 14	0.677 14	0.961 17	0.537 34	0.348 12	0.769 15	0.903 12	0.785 13	0.815 8	0.676 25	0.939 16	0.880 13	0.772 10

PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 35	0.829 28	0.751 25	0.854 17	0.540 23	0.903 10	0.630 37	0.672 17	0.963 15	0.565 24	0.357 9	0.788 5	0.900 14	0.737 28	0.802 17	0.685 19	0.950 7	0.887 7	0.780 7
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OctFormer	0.766 9	0.925 7	0.808 25	0.849 11	0.786 5	0.846 29	0.566 10	0.876 18	0.690 11	0.674 16	0.960 19	0.576 20	0.226 70	0.753 27	0.904 11	0.777 15	0.815 8	0.722 6	0.923 30	0.877 16	0.776 9
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net	0.764 11	0.924 8	0.819 13	0.840 20	0.757 20	0.853 19	0.580 4	0.848 29	0.709 4	0.643 27	0.958 23	0.587 15	0.295 36	0.753 27	0.884 22	0.758 22	0.815 8	0.725 4	0.927 26	0.867 25	0.743 18

OccuSeg+Semantic	0.764 11	0.758 60	0.796 33	0.839 21	0.746 28	0.907 1	0.562 12	0.850 28	0.680 17	0.672 17	0.978 5	0.610 4	0.335 20	0.777 9	0.819 48	0.847 1	0.830 1	0.691 16	0.972 2	0.885 10	0.727 25

O-CNN	0.762 13	0.924 8	0.823 8	0.844 17	0.770 12	0.852 21	0.577 5	0.847 31	0.711 3	0.640 31	0.958 23	0.592 11	0.217 76	0.762 20	0.888 19	0.758 22	0.813 12	0.726 3	0.932 24	0.868 24	0.744 17
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 77	0.789 40	0.843 18	0.762 16	0.856 14	0.562 12	0.920 4	0.657 27	0.658 21	0.958 23	0.589 13	0.337 17	0.782 6	0.879 23	0.787 11	0.779 38	0.678 21	0.926 28	0.880 13	0.799 4

DTC	0.757 15	0.843 27	0.820 11	0.847 14	0.791 2	0.862 11	0.511 36	0.870 20	0.707 5	0.652 23	0.954 38	0.604 8	0.279 47	0.760 21	0.942 2	0.734 29	0.766 47	0.701 12	0.884 57	0.874 22	0.736 19

OA-CNN-L_ScanNet20	0.756 16	0.783 46	0.826 6	0.858 5	0.776 8	0.837 36	0.548 18	0.896 14	0.649 29	0.675 15	0.962 16	0.586 16	0.335 20	0.771 14	0.802 52	0.770 18	0.787 35	0.691 16	0.936 19	0.880 13	0.761 13

PNE	0.755 17	0.786 44	0.835 5	0.834 25	0.758 18	0.849 24	0.570 9	0.836 35	0.648 30	0.668 19	0.978 5	0.581 19	0.367 7	0.683 38	0.856 32	0.804 7	0.801 21	0.678 21	0.961 5	0.889 6	0.716 32
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer	0.755 17	0.927 6	0.822 9	0.836 23	0.801 1	0.849 24	0.516 33	0.864 25	0.651 28	0.680 13	0.958 23	0.584 18	0.282 44	0.759 23	0.855 34	0.728 31	0.802 17	0.678 21	0.880 62	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
PointTransformerV2	0.752 19	0.742 68	0.809 24	0.872 2	0.758 18	0.860 12	0.552 16	0.891 16	0.610 44	0.687 8	0.960 19	0.559 28	0.304 32	0.766 18	0.926 6	0.767 19	0.797 25	0.644 36	0.942 13	0.876 19	0.722 29
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net	0.752 19	0.906 13	0.793 37	0.802 44	0.689 43	0.825 49	0.556 14	0.867 21	0.681 16	0.602 47	0.960 19	0.555 30	0.365 8	0.779 8	0.859 29	0.747 25	0.795 29	0.717 7	0.917 34	0.856 33	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
BPNet	0.749 21	0.909 11	0.818 15	0.811 36	0.752 23	0.839 35	0.485 50	0.842 32	0.673 19	0.644 26	0.957 28	0.528 40	0.305 31	0.773 12	0.859 29	0.788 10	0.818 7	0.693 15	0.916 35	0.856 33	0.723 28
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 42	0.790 38	0.807 40	0.750 27	0.856 14	0.524 29	0.881 17	0.588 56	0.642 30	0.977 9	0.591 12	0.274 50	0.781 7	0.929 4	0.804 7	0.796 26	0.642 37	0.947 10	0.885 10	0.715 33
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP	0.748 23	0.623 97	0.804 27	0.859 4	0.745 29	0.824 51	0.501 40	0.912 7	0.690 11	0.685 10	0.956 29	0.567 23	0.320 26	0.768 17	0.918 7	0.720 36	0.802 17	0.676 25	0.921 32	0.881 12	0.779 8

StratifiedFormer	0.747 24	0.901 14	0.803 28	0.845 16	0.757 20	0.846 29	0.512 35	0.825 39	0.696 9	0.645 25	0.956 29	0.576 20	0.262 61	0.744 32	0.861 28	0.742 26	0.770 45	0.705 10	0.899 47	0.860 30	0.734 20
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
VMNet	0.746 25	0.870 19	0.838 3	0.858 5	0.729 34	0.850 23	0.501 40	0.874 19	0.587 57	0.658 21	0.956 29	0.564 25	0.299 34	0.765 19	0.900 14	0.716 39	0.812 13	0.631 42	0.939 16	0.858 31	0.709 34
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion	0.746 25	0.771 54	0.819 13	0.848 13	0.702 40	0.865 10	0.397 88	0.899 12	0.699 7	0.664 20	0.948 58	0.588 14	0.330 22	0.746 31	0.851 38	0.764 20	0.796 26	0.704 11	0.935 20	0.866 26	0.728 23
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
DiffSeg3D2	0.745 27	0.725 77	0.814 19	0.837 22	0.751 25	0.831 43	0.514 34	0.896 14	0.674 18	0.684 11	0.960 19	0.564 25	0.303 33	0.773 12	0.820 47	0.713 42	0.798 24	0.690 18	0.923 30	0.875 20	0.757 14

Retro-FPN	0.744 28	0.842 28	0.800 29	0.767 58	0.740 30	0.836 38	0.541 21	0.914 6	0.672 20	0.626 35	0.958 23	0.552 31	0.272 52	0.777 9	0.886 21	0.696 49	0.801 21	0.674 28	0.941 14	0.858 31	0.717 30
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 98	0.799 32	0.849 11	0.730 33	0.822 53	0.493 47	0.897 13	0.664 21	0.681 12	0.955 32	0.562 27	0.378 4	0.760 21	0.903 12	0.738 27	0.801 21	0.673 29	0.907 39	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
LRPNet	0.742 30	0.816 36	0.806 26	0.807 40	0.752 23	0.828 47	0.575 7	0.839 34	0.699 7	0.637 32	0.954 38	0.520 43	0.320 26	0.755 26	0.834 42	0.760 21	0.772 42	0.676 25	0.915 37	0.862 28	0.717 30

SAT	0.742 30	0.860 22	0.765 52	0.819 31	0.769 14	0.848 26	0.533 25	0.829 37	0.663 22	0.631 34	0.955 32	0.586 16	0.274 50	0.753 27	0.896 16	0.729 30	0.760 53	0.666 31	0.921 32	0.855 35	0.733 21

LargeKernel3D	0.739 32	0.909 11	0.820 11	0.806 42	0.740 30	0.852 21	0.545 19	0.826 38	0.594 55	0.643 27	0.955 32	0.541 33	0.263 60	0.723 36	0.858 31	0.775 17	0.767 46	0.678 21	0.933 22	0.848 40	0.694 39
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
MinkowskiNet	0.736 33	0.859 23	0.818 15	0.832 27	0.709 38	0.840 33	0.521 31	0.853 27	0.660 24	0.643 27	0.951 48	0.544 32	0.286 42	0.731 34	0.893 17	0.675 58	0.772 42	0.683 20	0.874 69	0.852 38	0.727 25
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
RPN	0.736 33	0.776 50	0.790 38	0.851 9	0.754 22	0.854 17	0.491 49	0.866 23	0.596 54	0.686 9	0.955 32	0.536 35	0.342 15	0.624 53	0.869 25	0.787 11	0.802 17	0.628 43	0.927 26	0.875 20	0.704 36

IPCA	0.731 35	0.890 15	0.837 4	0.864 3	0.726 35	0.873 6	0.530 28	0.824 40	0.489 90	0.647 24	0.978 5	0.609 5	0.336 18	0.624 53	0.733 61	0.758 22	0.776 40	0.570 68	0.949 8	0.877 16	0.728 23

PointTransformer++	0.725 36	0.727 76	0.811 23	0.819 31	0.765 15	0.841 32	0.502 39	0.814 45	0.621 40	0.623 37	0.955 32	0.556 29	0.284 43	0.620 55	0.866 26	0.781 14	0.757 57	0.648 34	0.932 24	0.862 28	0.709 34

SparseConvNet	0.725 36	0.647 93	0.821 10	0.846 15	0.721 36	0.869 7	0.533 25	0.754 61	0.603 50	0.614 39	0.955 32	0.572 22	0.325 24	0.710 37	0.870 24	0.724 34	0.823 3	0.628 43	0.934 21	0.865 27	0.683 42

MatchingNet	0.724 38	0.812 38	0.812 21	0.810 37	0.735 32	0.834 40	0.495 46	0.860 26	0.572 64	0.602 47	0.954 38	0.512 45	0.280 46	0.757 24	0.845 40	0.725 33	0.780 37	0.606 53	0.937 18	0.851 39	0.700 38

INS-Conv-semantic	0.717 39	0.751 63	0.759 55	0.812 35	0.704 39	0.868 8	0.537 24	0.842 32	0.609 46	0.608 43	0.953 42	0.534 37	0.293 37	0.616 56	0.864 27	0.719 38	0.793 30	0.640 38	0.933 22	0.845 44	0.663 48

PointMetaBase	0.714 40	0.835 29	0.785 41	0.821 29	0.684 45	0.846 29	0.531 27	0.865 24	0.614 41	0.596 51	0.953 42	0.500 48	0.246 66	0.674 39	0.888 19	0.692 50	0.764 49	0.624 45	0.849 84	0.844 45	0.675 44

contrastBoundary	0.705 41	0.769 57	0.775 46	0.809 38	0.687 44	0.820 56	0.439 76	0.812 46	0.661 23	0.591 53	0.945 66	0.515 44	0.171 94	0.633 50	0.856 32	0.720 36	0.796 26	0.668 30	0.889 54	0.847 41	0.689 40
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 42	0.774 52	0.800 29	0.793 49	0.760 17	0.847 28	0.471 54	0.802 49	0.463 97	0.634 33	0.968 13	0.491 51	0.271 54	0.726 35	0.910 9	0.706 44	0.815 8	0.551 80	0.878 63	0.833 46	0.570 80

RFCR	0.702 43	0.889 16	0.745 66	0.813 34	0.672 48	0.818 60	0.493 47	0.815 44	0.623 38	0.610 41	0.947 60	0.470 60	0.249 65	0.594 59	0.848 39	0.705 45	0.779 38	0.646 35	0.892 52	0.823 52	0.611 63
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click	0.701 44	0.825 33	0.796 33	0.723 65	0.716 37	0.832 42	0.433 78	0.816 42	0.634 35	0.609 42	0.969 11	0.418 86	0.344 14	0.559 71	0.833 43	0.715 40	0.808 15	0.560 74	0.902 44	0.847 41	0.680 43

JSENet	0.699 45	0.881 18	0.762 53	0.821 29	0.667 49	0.800 72	0.522 30	0.792 52	0.613 42	0.607 44	0.935 86	0.492 50	0.205 81	0.576 64	0.853 36	0.691 52	0.758 55	0.652 33	0.872 72	0.828 49	0.649 52
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click	0.693 46	0.743 67	0.794 35	0.655 88	0.684 45	0.822 53	0.497 45	0.719 71	0.622 39	0.617 38	0.977 9	0.447 73	0.339 16	0.750 30	0.664 77	0.703 47	0.790 33	0.596 58	0.946 12	0.855 35	0.647 53
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-II	0.692 47	0.732 72	0.772 47	0.786 50	0.677 47	0.866 9	0.517 32	0.848 29	0.509 83	0.626 35	0.952 46	0.536 35	0.225 72	0.545 77	0.704 68	0.689 55	0.810 14	0.564 73	0.903 43	0.854 37	0.729 22
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 48	0.884 17	0.754 59	0.795 47	0.647 56	0.818 60	0.422 80	0.802 49	0.612 43	0.604 45	0.945 66	0.462 63	0.189 89	0.563 70	0.853 36	0.726 32	0.765 48	0.632 41	0.904 41	0.821 55	0.606 67
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 49	0.704 83	0.741 70	0.754 62	0.656 51	0.829 45	0.501 40	0.741 66	0.609 46	0.548 61	0.950 52	0.522 42	0.371 5	0.633 50	0.756 56	0.715 40	0.771 44	0.623 46	0.861 80	0.814 58	0.658 49
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 50	0.866 20	0.748 63	0.819 31	0.645 58	0.794 75	0.450 66	0.802 49	0.587 57	0.604 45	0.945 66	0.464 62	0.201 84	0.554 73	0.840 41	0.723 35	0.732 67	0.602 56	0.907 39	0.822 54	0.603 70

KP-FCNN	0.684 51	0.847 26	0.758 57	0.784 52	0.647 56	0.814 63	0.473 53	0.772 55	0.605 48	0.594 52	0.935 86	0.450 71	0.181 92	0.587 60	0.805 51	0.690 53	0.785 36	0.614 49	0.882 59	0.819 56	0.632 59
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++	0.684 51	0.728 75	0.757 58	0.776 55	0.690 41	0.804 70	0.464 59	0.816 42	0.577 63	0.587 54	0.945 66	0.508 47	0.276 49	0.671 40	0.710 66	0.663 63	0.750 61	0.589 63	0.881 60	0.832 48	0.653 51

DGNet	0.684 51	0.712 82	0.784 42	0.782 54	0.658 50	0.835 39	0.499 44	0.823 41	0.641 32	0.597 50	0.950 52	0.487 53	0.281 45	0.575 65	0.619 81	0.647 71	0.764 49	0.620 48	0.871 75	0.846 43	0.688 41

PointContrast_LA_SEM	0.683 54	0.757 61	0.784 42	0.786 50	0.639 60	0.824 51	0.408 83	0.775 54	0.604 49	0.541 63	0.934 90	0.532 38	0.269 56	0.552 74	0.777 54	0.645 74	0.793 30	0.640 38	0.913 38	0.824 51	0.671 45

Superpoint Network	0.683 54	0.851 25	0.728 74	0.800 46	0.653 53	0.806 68	0.468 56	0.804 47	0.572 64	0.602 47	0.946 63	0.453 70	0.239 69	0.519 82	0.822 45	0.689 55	0.762 52	0.595 60	0.895 50	0.827 50	0.630 60

VI-PointConv	0.676 56	0.770 56	0.754 59	0.783 53	0.621 64	0.814 63	0.552 16	0.758 59	0.571 66	0.557 59	0.954 38	0.529 39	0.268 58	0.530 80	0.682 72	0.675 58	0.719 70	0.603 55	0.888 55	0.833 46	0.665 47
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D	0.673 57	0.789 43	0.748 63	0.763 60	0.635 62	0.814 63	0.407 85	0.747 63	0.581 61	0.573 56	0.950 52	0.484 54	0.271 54	0.607 57	0.754 57	0.649 68	0.774 41	0.596 58	0.883 58	0.823 52	0.606 67

SALANet	0.670 58	0.816 36	0.770 50	0.768 57	0.652 54	0.807 67	0.451 63	0.747 63	0.659 26	0.545 62	0.924 96	0.473 59	0.149 104	0.571 67	0.811 50	0.635 77	0.746 62	0.623 46	0.892 52	0.794 71	0.570 80

O3DSeg	0.668 59	0.822 34	0.771 49	0.496 108	0.651 55	0.833 41	0.541 21	0.761 58	0.555 72	0.611 40	0.966 14	0.489 52	0.370 6	0.388 102	0.580 84	0.776 16	0.751 59	0.570 68	0.956 6	0.817 57	0.646 54

PointASNL	0.666 60	0.703 84	0.781 44	0.751 64	0.655 52	0.830 44	0.471 54	0.769 56	0.474 93	0.537 65	0.951 48	0.475 58	0.279 47	0.635 48	0.698 71	0.675 58	0.751 59	0.553 79	0.816 91	0.806 62	0.703 37
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConv	0.666 60	0.781 47	0.759 55	0.699 73	0.644 59	0.822 53	0.475 52	0.779 53	0.564 69	0.504 79	0.953 42	0.428 80	0.203 83	0.586 62	0.754 57	0.661 64	0.753 58	0.588 64	0.902 44	0.813 60	0.642 55
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++	0.663 62	0.746 65	0.708 77	0.722 66	0.638 61	0.820 56	0.451 63	0.566 99	0.599 52	0.541 63	0.950 52	0.510 46	0.313 28	0.648 45	0.819 48	0.616 82	0.682 85	0.590 62	0.869 76	0.810 61	0.656 50
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net	0.658 63	0.778 48	0.702 80	0.806 42	0.619 65	0.813 66	0.468 56	0.693 79	0.494 86	0.524 71	0.941 78	0.449 72	0.298 35	0.510 84	0.821 46	0.675 58	0.727 69	0.568 71	0.826 89	0.803 65	0.637 57
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MVF-GNN	0.658 63	0.558 105	0.751 61	0.655 88	0.690 41	0.722 97	0.453 62	0.867 21	0.579 62	0.576 55	0.893 108	0.523 41	0.293 37	0.733 33	0.571 86	0.692 50	0.659 92	0.606 53	0.875 66	0.804 64	0.668 46

HPGCNN	0.656 65	0.698 86	0.743 68	0.650 90	0.564 82	0.820 56	0.505 38	0.758 59	0.631 36	0.479 83	0.945 66	0.480 56	0.226 70	0.572 66	0.774 55	0.690 53	0.735 65	0.614 49	0.853 83	0.776 86	0.597 73
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 66	0.752 62	0.734 72	0.664 86	0.583 77	0.815 62	0.399 87	0.754 61	0.639 33	0.535 67	0.942 76	0.470 60	0.309 30	0.665 41	0.539 88	0.650 67	0.708 75	0.635 40	0.857 82	0.793 73	0.642 55
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 67	0.778 48	0.731 73	0.699 73	0.577 78	0.829 45	0.446 68	0.736 67	0.477 92	0.523 73	0.945 66	0.454 67	0.269 56	0.484 92	0.749 60	0.618 80	0.738 63	0.599 57	0.827 88	0.792 76	0.621 62

PointConv-SFPN	0.641 68	0.776 50	0.703 79	0.721 67	0.557 85	0.826 48	0.451 63	0.672 84	0.563 70	0.483 82	0.943 75	0.425 83	0.162 99	0.644 46	0.726 62	0.659 65	0.709 74	0.572 67	0.875 66	0.786 81	0.559 86

MVPNet	0.641 68	0.831 30	0.715 75	0.671 83	0.590 73	0.781 81	0.394 89	0.679 81	0.642 31	0.553 60	0.937 83	0.462 63	0.256 62	0.649 44	0.406 102	0.626 78	0.691 82	0.666 31	0.877 64	0.792 76	0.608 66
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet	0.640 70	0.717 81	0.701 81	0.692 76	0.576 79	0.801 71	0.467 58	0.716 72	0.563 70	0.459 89	0.953 42	0.429 79	0.169 96	0.581 63	0.854 35	0.605 83	0.710 72	0.550 81	0.894 51	0.793 73	0.575 78

FPConv	0.639 71	0.785 45	0.760 54	0.713 71	0.603 68	0.798 73	0.392 90	0.534 104	0.603 50	0.524 71	0.948 58	0.457 65	0.250 64	0.538 78	0.723 64	0.598 87	0.696 80	0.614 49	0.872 72	0.799 66	0.567 83
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net	0.638 72	0.797 41	0.769 51	0.641 96	0.590 73	0.820 56	0.461 60	0.537 103	0.637 34	0.536 66	0.947 60	0.388 93	0.206 80	0.656 42	0.668 75	0.647 71	0.732 67	0.585 65	0.868 77	0.793 73	0.473 106

PointSPNet	0.637 73	0.734 71	0.692 88	0.714 70	0.576 79	0.797 74	0.446 68	0.743 65	0.598 53	0.437 94	0.942 76	0.403 89	0.150 103	0.626 52	0.800 53	0.649 68	0.697 79	0.557 77	0.846 85	0.777 85	0.563 84

SConv	0.636 74	0.830 31	0.697 84	0.752 63	0.572 81	0.780 83	0.445 70	0.716 72	0.529 76	0.530 68	0.951 48	0.446 74	0.170 95	0.507 87	0.666 76	0.636 76	0.682 85	0.541 87	0.886 56	0.799 66	0.594 74

Supervoxel-CNN	0.635 75	0.656 91	0.711 76	0.719 68	0.613 66	0.757 92	0.444 73	0.765 57	0.534 75	0.566 57	0.928 94	0.478 57	0.272 52	0.636 47	0.531 90	0.664 62	0.645 96	0.508 94	0.864 79	0.792 76	0.611 63

joint point-based	0.634 76	0.614 99	0.778 45	0.667 85	0.633 63	0.825 49	0.420 81	0.804 47	0.467 95	0.561 58	0.951 48	0.494 49	0.291 39	0.566 68	0.458 97	0.579 93	0.764 49	0.559 76	0.838 86	0.814 58	0.598 72
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL	0.632 77	0.731 73	0.688 91	0.675 80	0.591 72	0.784 80	0.444 73	0.565 100	0.610 44	0.492 80	0.949 56	0.456 66	0.254 63	0.587 60	0.706 67	0.599 86	0.665 91	0.612 52	0.868 77	0.791 79	0.579 77

3DSM_DMMF	0.631 78	0.626 96	0.745 66	0.801 45	0.607 67	0.751 93	0.506 37	0.729 70	0.565 68	0.491 81	0.866 111	0.434 75	0.197 87	0.595 58	0.630 80	0.709 43	0.705 77	0.560 74	0.875 66	0.740 96	0.491 101

PointNet2-SFPN	0.631 78	0.771 54	0.692 88	0.672 81	0.524 90	0.837 36	0.440 75	0.706 77	0.538 74	0.446 91	0.944 72	0.421 85	0.219 75	0.552 74	0.751 59	0.591 89	0.737 64	0.543 86	0.901 46	0.768 88	0.557 87

APCF-Net	0.631 78	0.742 68	0.687 93	0.672 81	0.557 85	0.792 78	0.408 83	0.665 85	0.545 73	0.508 76	0.952 46	0.428 80	0.186 90	0.634 49	0.702 69	0.620 79	0.706 76	0.555 78	0.873 70	0.798 68	0.581 76
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv	0.630 81	0.604 101	0.741 70	0.766 59	0.590 73	0.747 94	0.501 40	0.734 68	0.503 85	0.527 69	0.919 100	0.454 67	0.323 25	0.550 76	0.420 101	0.678 57	0.688 83	0.544 84	0.896 49	0.795 70	0.627 61
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 82	0.800 40	0.625 104	0.719 68	0.545 87	0.806 68	0.445 70	0.597 93	0.448 100	0.519 74	0.938 82	0.481 55	0.328 23	0.489 91	0.499 95	0.657 66	0.759 54	0.592 61	0.881 60	0.797 69	0.634 58

SegGroup_sem	0.627 83	0.818 35	0.747 65	0.701 72	0.602 69	0.764 89	0.385 94	0.629 90	0.490 88	0.508 76	0.931 93	0.409 88	0.201 84	0.564 69	0.725 63	0.618 80	0.692 81	0.539 88	0.873 70	0.794 71	0.548 90
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv	0.625 84	0.830 31	0.694 86	0.757 61	0.563 83	0.772 87	0.448 67	0.647 88	0.520 79	0.509 75	0.949 56	0.431 78	0.191 88	0.496 89	0.614 82	0.647 71	0.672 89	0.535 90	0.876 65	0.783 82	0.571 79

dtc_net	0.625 84	0.703 84	0.751 61	0.794 48	0.535 88	0.848 26	0.480 51	0.676 83	0.528 77	0.469 86	0.944 72	0.454 67	0.004 117	0.464 94	0.636 79	0.704 46	0.758 55	0.548 83	0.924 29	0.787 80	0.492 100

HPEIN	0.618 86	0.729 74	0.668 94	0.647 92	0.597 71	0.766 88	0.414 82	0.680 80	0.520 79	0.525 70	0.946 63	0.432 76	0.215 77	0.493 90	0.599 83	0.638 75	0.617 101	0.570 68	0.897 48	0.806 62	0.605 69
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCN	0.610 87	0.858 24	0.772 47	0.489 109	0.532 89	0.792 78	0.404 86	0.643 89	0.570 67	0.507 78	0.935 86	0.414 87	0.046 114	0.510 84	0.702 69	0.602 85	0.705 77	0.549 82	0.859 81	0.773 87	0.534 93
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 88	0.760 59	0.667 95	0.649 91	0.521 91	0.793 76	0.457 61	0.648 87	0.528 77	0.434 96	0.947 60	0.401 90	0.153 102	0.454 95	0.721 65	0.648 70	0.717 71	0.536 89	0.904 41	0.765 89	0.485 102
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer	0.600 89	0.634 95	0.743 68	0.697 75	0.601 70	0.781 81	0.437 77	0.585 96	0.493 87	0.446 91	0.933 91	0.394 91	0.011 116	0.654 43	0.661 78	0.603 84	0.733 66	0.526 91	0.832 87	0.761 91	0.480 103

LAP-D	0.594 90	0.720 79	0.692 88	0.637 97	0.456 101	0.773 86	0.391 92	0.730 69	0.587 57	0.445 93	0.940 80	0.381 94	0.288 40	0.434 98	0.453 99	0.591 89	0.649 94	0.581 66	0.777 95	0.749 95	0.610 65

DPC	0.592 91	0.720 79	0.700 82	0.602 101	0.480 97	0.762 91	0.380 95	0.713 75	0.585 60	0.437 94	0.940 80	0.369 96	0.288 40	0.434 98	0.509 94	0.590 91	0.639 99	0.567 72	0.772 97	0.755 93	0.592 75
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet	0.589 92	0.766 58	0.659 99	0.683 78	0.470 100	0.740 96	0.387 93	0.620 92	0.490 88	0.476 84	0.922 98	0.355 99	0.245 67	0.511 83	0.511 93	0.571 94	0.643 97	0.493 98	0.872 72	0.762 90	0.600 71

ROSMRF	0.580 93	0.772 53	0.707 78	0.681 79	0.563 83	0.764 89	0.362 97	0.515 105	0.465 96	0.465 88	0.936 85	0.427 82	0.207 79	0.438 96	0.577 85	0.536 97	0.675 88	0.486 99	0.723 103	0.779 83	0.524 96

SD-DETR	0.576 94	0.746 65	0.609 108	0.445 113	0.517 92	0.643 108	0.366 96	0.714 74	0.456 98	0.468 87	0.870 110	0.432 76	0.264 59	0.558 72	0.674 73	0.586 92	0.688 83	0.482 100	0.739 101	0.733 98	0.537 92

SQN_0.1%	0.569 95	0.676 88	0.696 85	0.657 87	0.497 93	0.779 84	0.424 79	0.548 101	0.515 81	0.376 101	0.902 107	0.422 84	0.357 9	0.379 103	0.456 98	0.596 88	0.659 92	0.544 84	0.685 106	0.665 109	0.556 88

TextureNet	0.566 96	0.672 90	0.664 96	0.671 83	0.494 95	0.719 98	0.445 70	0.678 82	0.411 106	0.396 99	0.935 86	0.356 98	0.225 72	0.412 100	0.535 89	0.565 95	0.636 100	0.464 102	0.794 94	0.680 106	0.568 82
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet	0.562 97	0.648 92	0.700 82	0.770 56	0.586 76	0.687 102	0.333 101	0.650 86	0.514 82	0.475 85	0.906 104	0.359 97	0.223 74	0.340 105	0.442 100	0.422 108	0.668 90	0.501 95	0.708 104	0.779 83	0.534 93

Pointnet++ & Feature	0.557 98	0.735 70	0.661 98	0.686 77	0.491 96	0.744 95	0.392 90	0.539 102	0.451 99	0.375 102	0.946 63	0.376 95	0.205 81	0.403 101	0.356 105	0.553 96	0.643 97	0.497 96	0.824 90	0.756 92	0.515 97

GMLPs	0.538 99	0.495 110	0.693 87	0.647 92	0.471 99	0.793 76	0.300 104	0.477 106	0.505 84	0.358 104	0.903 106	0.327 102	0.081 111	0.472 93	0.529 91	0.448 106	0.710 72	0.509 92	0.746 99	0.737 97	0.554 89

PanopticFusion-label	0.529 100	0.491 111	0.688 91	0.604 100	0.386 106	0.632 109	0.225 114	0.705 78	0.434 103	0.293 110	0.815 112	0.348 100	0.241 68	0.499 88	0.669 74	0.507 99	0.649 94	0.442 108	0.796 93	0.602 113	0.561 85
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak	0.516 101	0.676 88	0.591 111	0.609 98	0.442 102	0.774 85	0.335 100	0.597 93	0.422 105	0.357 105	0.932 92	0.341 101	0.094 110	0.298 107	0.528 92	0.473 104	0.676 87	0.495 97	0.602 112	0.721 101	0.349 113

Online SegFusion	0.515 102	0.607 100	0.644 102	0.579 103	0.434 103	0.630 110	0.353 98	0.628 91	0.440 101	0.410 97	0.762 116	0.307 104	0.167 97	0.520 81	0.403 103	0.516 98	0.565 104	0.447 106	0.678 107	0.701 103	0.514 98
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF	0.501 103	0.558 105	0.608 109	0.424 115	0.478 98	0.690 101	0.246 110	0.586 95	0.468 94	0.450 90	0.911 102	0.394 91	0.160 100	0.438 96	0.212 112	0.432 107	0.541 110	0.475 101	0.742 100	0.727 99	0.477 104

PCNN	0.498 104	0.559 104	0.644 102	0.560 105	0.420 105	0.711 100	0.229 112	0.414 107	0.436 102	0.352 106	0.941 78	0.324 103	0.155 101	0.238 112	0.387 104	0.493 100	0.529 111	0.509 92	0.813 92	0.751 94	0.504 99

Weakly-Openseg v3	0.489 105	0.749 64	0.664 96	0.646 94	0.496 94	0.559 114	0.122 117	0.577 97	0.257 117	0.364 103	0.805 113	0.198 115	0.096 109	0.510 84	0.496 96	0.361 112	0.563 105	0.359 115	0.777 95	0.644 110	0.532 95

3DMV	0.484 106	0.484 112	0.538 113	0.643 95	0.424 104	0.606 113	0.310 102	0.574 98	0.433 104	0.378 100	0.796 114	0.301 105	0.214 78	0.537 79	0.208 113	0.472 105	0.507 114	0.413 111	0.693 105	0.602 113	0.539 91
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 107	0.577 103	0.611 107	0.356 117	0.321 114	0.715 99	0.299 106	0.376 111	0.328 113	0.319 108	0.944 72	0.285 107	0.164 98	0.216 115	0.229 110	0.484 102	0.545 109	0.456 104	0.755 98	0.709 102	0.475 105
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 108	0.679 87	0.604 110	0.578 104	0.380 107	0.682 103	0.291 107	0.106 117	0.483 91	0.258 115	0.920 99	0.258 111	0.025 115	0.231 114	0.325 106	0.480 103	0.560 107	0.463 103	0.725 102	0.666 108	0.231 117
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 109	0.474 113	0.623 105	0.463 111	0.366 109	0.651 106	0.310 102	0.389 110	0.349 111	0.330 107	0.937 83	0.271 109	0.126 106	0.285 108	0.224 111	0.350 114	0.577 103	0.445 107	0.625 110	0.723 100	0.394 109
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET2	0.442 110	0.548 107	0.548 112	0.597 102	0.363 110	0.628 111	0.300 104	0.292 112	0.374 108	0.307 109	0.881 109	0.268 110	0.186 90	0.238 112	0.204 114	0.407 109	0.506 115	0.449 105	0.667 108	0.620 112	0.462 107

SurfaceConvPF	0.442 110	0.505 109	0.622 106	0.380 116	0.342 112	0.654 105	0.227 113	0.397 109	0.367 109	0.276 112	0.924 96	0.240 112	0.198 86	0.359 104	0.262 108	0.366 110	0.581 102	0.435 109	0.640 109	0.668 107	0.398 108
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 112	0.437 115	0.646 101	0.474 110	0.369 108	0.645 107	0.353 98	0.258 114	0.282 115	0.279 111	0.918 101	0.298 106	0.147 105	0.283 109	0.294 107	0.487 101	0.562 106	0.427 110	0.619 111	0.633 111	0.352 112
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 113	0.525 108	0.647 100	0.522 106	0.324 113	0.488 117	0.077 118	0.712 76	0.353 110	0.401 98	0.636 118	0.281 108	0.176 93	0.340 105	0.565 87	0.175 118	0.551 108	0.398 112	0.370 118	0.602 113	0.361 111

SPLAT Net	0.393 114	0.472 114	0.511 114	0.606 99	0.311 115	0.656 104	0.245 111	0.405 108	0.328 113	0.197 116	0.927 95	0.227 114	0.000 119	0.001 119	0.249 109	0.271 117	0.510 112	0.383 114	0.593 113	0.699 104	0.267 115
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF	0.383 115	0.297 117	0.491 115	0.432 114	0.358 111	0.612 112	0.274 108	0.116 116	0.411 106	0.265 113	0.904 105	0.229 113	0.079 112	0.250 110	0.185 115	0.320 115	0.510 112	0.385 113	0.548 114	0.597 116	0.394 109

PointNet++	0.339 116	0.584 102	0.478 116	0.458 112	0.256 117	0.360 118	0.250 109	0.247 115	0.278 116	0.261 114	0.677 117	0.183 116	0.117 107	0.212 116	0.145 117	0.364 111	0.346 118	0.232 118	0.548 114	0.523 117	0.252 116
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNet	0.308 117	0.353 116	0.290 118	0.278 118	0.166 118	0.553 115	0.169 116	0.286 113	0.147 118	0.148 118	0.908 103	0.182 117	0.064 113	0.023 118	0.018 119	0.354 113	0.363 116	0.345 116	0.546 116	0.685 105	0.278 114

ScanNet	0.306 118	0.203 118	0.366 117	0.501 107	0.311 115	0.524 116	0.211 115	0.002 119	0.342 112	0.189 117	0.786 115	0.145 118	0.102 108	0.245 111	0.152 116	0.318 116	0.348 117	0.300 117	0.460 117	0.437 118	0.182 118
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 119	0.000 119	0.041 119	0.172 119	0.030 119	0.062 119	0.001 119	0.035 118	0.004 119	0.051 119	0.143 119	0.019 119	0.003 118	0.041 117	0.050 118	0.003 119	0.054 119	0.018 119	0.005 119	0.264 119	0.082 119

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

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

PointRel	0.816 1	1.000 1	0.971 7	0.908 6	0.743 2	0.923 6	0.573 6	0.714 22	0.695 17	0.734 8	0.747 2	0.725 10	0.809 1	1.000 1	0.814 8	0.899 3	0.820 3	1.000 1	0.610 16
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
Spherical Mask(CtoF)	0.812 2	1.000 1	0.973 6	0.852 13	0.718 5	0.917 8	0.574 4	0.677 29	0.748 10	0.729 12	0.715 6	0.795 2	0.809 1	1.000 1	0.831 4	0.854 9	0.787 10	1.000 1	0.638 5

EV3D	0.811 3	1.000 1	0.968 8	0.852 13	0.717 6	0.921 7	0.574 5	0.677 29	0.748 10	0.730 11	0.703 11	0.795 2	0.809 1	1.000 1	0.831 4	0.854 9	0.778 14	1.000 1	0.638 6

SIM3D	0.803 4	1.000 1	0.967 9	0.863 12	0.692 17	0.924 5	0.552 10	0.732 21	0.667 21	0.732 10	0.662 15	0.796 1	0.789 9	1.000 1	0.803 9	0.864 6	0.766 19	1.000 1	0.643 4

OneFormer3D	0.801 5	1.000 1	0.973 5	0.909 5	0.698 14	0.928 3	0.582 3	0.668 34	0.685 18	0.780 2	0.687 13	0.698 18	0.702 14	1.000 1	0.794 11	0.900 2	0.784 12	0.986 51	0.635 7
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
Competitor-SPFormer	0.800 6	1.000 1	0.986 2	0.845 15	0.705 12	0.915 9	0.532 12	0.733 20	0.757 9	0.733 9	0.708 8	0.698 17	0.648 34	0.981 37	0.890 1	0.830 18	0.796 7	0.997 38	0.644 3

UniPerception	0.800 6	1.000 1	0.930 11	0.872 10	0.727 4	0.862 23	0.454 18	0.764 13	0.820 1	0.746 6	0.706 9	0.750 5	0.772 10	0.926 44	0.764 17	0.818 26	0.826 1	0.997 38	0.660 2

InsSSM	0.799 8	1.000 1	0.915 13	0.710 40	0.729 3	0.925 4	0.664 1	0.670 32	0.770 6	0.766 3	0.739 3	0.737 6	0.700 15	1.000 1	0.792 12	0.829 20	0.815 4	0.997 38	0.625 9
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
TST3D	0.795 9	1.000 1	0.929 12	0.918 4	0.709 9	0.884 18	0.596 2	0.704 25	0.769 7	0.734 7	0.644 20	0.699 16	0.751 12	1.000 1	0.794 10	0.876 5	0.757 22	0.997 38	0.550 31
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
MG-Former	0.791 10	1.000 1	0.980 4	0.837 18	0.626 25	0.897 11	0.543 11	0.759 15	0.800 5	0.766 4	0.659 16	0.769 4	0.697 18	1.000 1	0.791 13	0.707 47	0.791 9	1.000 1	0.610 15

ExtMask3D	0.789 11	1.000 1	0.988 1	0.756 33	0.706 11	0.912 10	0.429 19	0.647 39	0.806 4	0.755 5	0.673 14	0.689 19	0.772 11	1.000 1	0.789 14	0.852 11	0.811 5	1.000 1	0.617 12

Queryformer	0.787 12	1.000 1	0.933 10	0.601 49	0.754 1	0.886 16	0.558 9	0.661 36	0.767 8	0.665 18	0.716 5	0.639 24	0.808 5	1.000 1	0.844 3	0.897 4	0.804 6	1.000 1	0.624 10

MAFT	0.786 13	1.000 1	0.894 18	0.807 22	0.694 16	0.893 14	0.486 14	0.674 31	0.740 12	0.786 1	0.704 10	0.727 9	0.739 13	1.000 1	0.707 23	0.849 13	0.756 23	1.000 1	0.685 1

KmaxOneFormerNet	0.783 14	0.903 54	0.981 3	0.794 26	0.706 10	0.931 2	0.561 8	0.701 26	0.706 15	0.727 13	0.697 12	0.731 8	0.689 21	1.000 1	0.856 2	0.750 38	0.761 21	1.000 1	0.599 20

Mask3D	0.780 15	1.000 1	0.786 42	0.716 38	0.696 15	0.885 17	0.500 13	0.714 22	0.810 3	0.672 17	0.715 6	0.679 20	0.809 1	1.000 1	0.831 4	0.833 17	0.787 10	1.000 1	0.602 18
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormer	0.770 16	0.903 54	0.903 15	0.806 23	0.609 31	0.886 15	0.568 7	0.815 6	0.705 16	0.711 14	0.655 17	0.652 23	0.685 22	1.000 1	0.789 15	0.809 27	0.776 16	1.000 1	0.583 24
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++	0.769 17	1.000 1	0.803 35	0.937 1	0.684 18	0.865 20	0.213 34	0.870 2	0.664 22	0.571 24	0.758 1	0.702 14	0.807 6	1.000 1	0.653 30	0.902 1	0.792 8	1.000 1	0.626 8

SoftGroup	0.761 18	1.000 1	0.808 31	0.845 15	0.716 7	0.862 22	0.243 31	0.824 4	0.655 24	0.620 19	0.734 4	0.699 15	0.791 8	0.981 37	0.716 21	0.844 14	0.769 17	1.000 1	0.594 22
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNet	0.757 19	1.000 1	0.904 14	0.731 36	0.678 19	0.895 12	0.458 16	0.644 41	0.670 20	0.710 15	0.620 25	0.732 7	0.650 24	1.000 1	0.756 18	0.778 30	0.779 13	1.000 1	0.614 13
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
TD3D	0.751 20	1.000 1	0.774 43	0.867 11	0.621 27	0.934 1	0.404 20	0.706 24	0.812 2	0.605 22	0.633 23	0.626 25	0.690 20	1.000 1	0.640 32	0.820 23	0.777 15	1.000 1	0.612 14
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNet	0.747 21	1.000 1	0.818 27	0.837 19	0.713 8	0.844 25	0.457 17	0.647 39	0.711 14	0.614 20	0.617 27	0.657 22	0.650 24	1.000 1	0.692 24	0.822 22	0.765 20	1.000 1	0.595 21
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut	0.732 22	1.000 1	0.788 40	0.724 37	0.642 24	0.859 24	0.248 30	0.787 11	0.618 27	0.596 23	0.653 19	0.722 12	0.583 46	1.000 1	0.766 16	0.861 7	0.825 2	1.000 1	0.504 37

IPCA-Inst	0.731 23	1.000 1	0.788 41	0.884 9	0.698 13	0.788 41	0.252 29	0.760 14	0.646 25	0.511 32	0.637 22	0.665 21	0.804 7	1.000 1	0.644 31	0.778 31	0.747 25	1.000 1	0.561 28

TopoSeg	0.725 24	1.000 1	0.806 34	0.933 2	0.668 21	0.758 45	0.272 28	0.734 19	0.630 26	0.549 28	0.654 18	0.606 26	0.697 19	0.966 41	0.612 36	0.839 15	0.754 24	1.000 1	0.573 25

DKNet	0.718 25	1.000 1	0.814 28	0.782 27	0.619 28	0.872 19	0.224 32	0.751 17	0.569 31	0.677 16	0.585 31	0.724 11	0.633 36	0.981 37	0.515 46	0.819 24	0.736 26	1.000 1	0.617 11
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC	0.707 26	1.000 1	0.850 20	0.924 3	0.648 22	0.747 48	0.162 36	0.862 3	0.572 30	0.520 30	0.624 24	0.549 29	0.649 33	1.000 1	0.560 41	0.706 48	0.768 18	1.000 1	0.591 23

HAIS	0.699 27	1.000 1	0.849 21	0.820 20	0.675 20	0.808 35	0.279 26	0.757 16	0.465 37	0.517 31	0.596 29	0.559 28	0.600 40	1.000 1	0.654 29	0.767 33	0.676 30	0.994 47	0.560 29
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet	0.698 28	1.000 1	0.697 59	0.888 8	0.556 38	0.803 36	0.387 21	0.626 43	0.417 42	0.556 27	0.585 32	0.702 13	0.600 40	1.000 1	0.824 7	0.720 46	0.692 28	1.000 1	0.509 36
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup	0.694 29	1.000 1	0.799 37	0.811 21	0.622 26	0.817 30	0.376 22	0.805 9	0.590 29	0.487 36	0.568 35	0.525 33	0.650 24	0.835 54	0.600 37	0.829 19	0.655 33	1.000 1	0.526 33

SphereSeg	0.680 30	1.000 1	0.856 19	0.744 34	0.618 29	0.893 13	0.151 37	0.651 38	0.713 13	0.537 29	0.579 34	0.430 43	0.651 23	1.000 1	0.389 57	0.744 41	0.697 27	0.991 49	0.601 19

DANCENET	0.680 30	1.000 1	0.807 32	0.733 35	0.600 32	0.768 44	0.375 23	0.543 51	0.538 32	0.610 21	0.599 28	0.498 34	0.632 38	0.981 37	0.739 20	0.856 8	0.633 39	0.882 62	0.454 46

Box2Mask	0.677 32	1.000 1	0.847 22	0.771 29	0.509 47	0.816 31	0.277 27	0.558 50	0.482 34	0.562 26	0.640 21	0.448 39	0.700 15	1.000 1	0.666 25	0.852 12	0.578 46	0.997 38	0.488 41
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance	0.672 33	1.000 1	0.758 51	0.682 42	0.576 36	0.842 26	0.477 15	0.504 57	0.524 33	0.567 25	0.585 33	0.451 38	0.557 48	1.000 1	0.751 19	0.797 28	0.563 49	1.000 1	0.467 45
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group	0.664 34	1.000 1	0.822 26	0.764 32	0.616 30	0.815 32	0.139 41	0.694 28	0.597 28	0.459 40	0.566 36	0.599 27	0.600 40	0.516 64	0.715 22	0.819 25	0.635 37	1.000 1	0.603 17
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance	0.657 35	1.000 1	0.760 49	0.667 44	0.581 34	0.863 21	0.323 24	0.655 37	0.477 35	0.473 38	0.549 38	0.432 42	0.650 24	1.000 1	0.655 28	0.738 42	0.585 45	0.944 54	0.472 44

CSC-Pretrained	0.648 36	1.000 1	0.810 29	0.768 30	0.523 45	0.813 33	0.143 40	0.819 5	0.389 45	0.422 49	0.511 42	0.443 40	0.650 24	1.000 1	0.624 34	0.732 43	0.634 38	1.000 1	0.375 53

PE	0.645 37	1.000 1	0.773 45	0.798 25	0.538 40	0.786 42	0.088 49	0.799 10	0.350 49	0.435 47	0.547 39	0.545 30	0.646 35	0.933 43	0.562 40	0.761 36	0.556 54	0.997 38	0.501 39
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 38	1.000 1	0.758 50	0.582 55	0.539 39	0.826 29	0.046 54	0.765 12	0.372 47	0.436 46	0.588 30	0.539 32	0.650 24	1.000 1	0.577 38	0.750 39	0.653 35	0.997 38	0.495 40
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3D	0.641 39	1.000 1	0.841 23	0.893 7	0.531 42	0.802 37	0.115 46	0.588 48	0.448 39	0.438 44	0.537 41	0.430 44	0.550 49	0.857 46	0.534 44	0.764 35	0.657 32	0.987 50	0.568 26
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN	0.638 40	1.000 1	0.895 17	0.800 24	0.480 51	0.676 53	0.144 39	0.737 18	0.354 48	0.447 41	0.400 55	0.365 50	0.700 15	1.000 1	0.569 39	0.836 16	0.599 41	1.000 1	0.473 43

PointGroup	0.636 41	1.000 1	0.765 46	0.624 46	0.505 49	0.797 38	0.116 45	0.696 27	0.384 46	0.441 42	0.559 37	0.476 36	0.596 43	1.000 1	0.666 25	0.756 37	0.556 53	0.997 38	0.513 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]
DD-UNet+Group	0.635 42	0.667 57	0.797 39	0.714 39	0.562 37	0.774 43	0.146 38	0.810 8	0.429 41	0.476 37	0.546 40	0.399 46	0.633 36	1.000 1	0.632 33	0.722 45	0.609 40	1.000 1	0.514 34
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
Mask3D_evaluation	0.631 43	1.000 1	0.829 25	0.606 48	0.646 23	0.836 27	0.068 50	0.511 55	0.462 38	0.507 33	0.619 26	0.389 48	0.610 39	1.000 1	0.432 52	0.828 21	0.673 31	0.788 66	0.552 30

DENet	0.629 44	1.000 1	0.797 38	0.608 47	0.589 33	0.627 57	0.219 33	0.882 1	0.310 51	0.402 54	0.383 57	0.396 47	0.650 24	1.000 1	0.663 27	0.543 65	0.691 29	1.000 1	0.568 27

3D-MPA	0.611 45	1.000 1	0.833 24	0.765 31	0.526 44	0.756 46	0.136 43	0.588 48	0.470 36	0.438 45	0.432 51	0.358 52	0.650 24	0.857 46	0.429 53	0.765 34	0.557 52	1.000 1	0.430 48
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS	0.605 46	1.000 1	0.801 36	0.599 50	0.535 41	0.728 50	0.286 25	0.436 61	0.679 19	0.491 34	0.433 49	0.256 54	0.404 61	0.857 46	0.620 35	0.724 44	0.510 59	1.000 1	0.539 32

AOIA	0.601 47	1.000 1	0.761 48	0.687 41	0.485 50	0.828 28	0.008 61	0.663 35	0.405 44	0.405 53	0.425 52	0.490 35	0.596 43	0.714 57	0.553 43	0.779 29	0.597 42	0.992 48	0.424 50

PCJC	0.578 48	1.000 1	0.810 30	0.583 54	0.449 54	0.813 34	0.042 55	0.603 46	0.341 50	0.490 35	0.465 46	0.410 45	0.650 24	0.835 54	0.264 63	0.694 52	0.561 50	0.889 59	0.504 38

SSEN	0.575 49	1.000 1	0.761 47	0.473 57	0.477 52	0.795 39	0.066 51	0.529 53	0.658 23	0.460 39	0.461 47	0.380 49	0.331 63	0.859 45	0.401 56	0.692 54	0.653 34	1.000 1	0.348 55
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg	0.567 50	0.528 67	0.708 58	0.626 45	0.580 35	0.745 49	0.063 52	0.627 42	0.240 55	0.400 55	0.497 43	0.464 37	0.515 50	1.000 1	0.475 48	0.745 40	0.571 47	1.000 1	0.429 49

NeuralBF	0.555 51	0.667 57	0.896 16	0.843 17	0.517 46	0.751 47	0.029 56	0.519 54	0.414 43	0.439 43	0.465 45	0.000 73	0.484 52	0.857 46	0.287 61	0.693 53	0.651 36	1.000 1	0.485 42
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML	0.549 52	1.000 1	0.807 33	0.588 53	0.327 59	0.647 55	0.004 63	0.815 7	0.180 58	0.418 50	0.364 59	0.182 57	0.445 55	1.000 1	0.442 51	0.688 55	0.571 48	1.000 1	0.396 51
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance	0.539 53	1.000 1	0.621 62	0.300 60	0.530 43	0.698 51	0.127 44	0.533 52	0.222 56	0.430 48	0.400 54	0.365 50	0.574 47	0.938 42	0.472 49	0.659 57	0.543 55	0.944 54	0.347 56

One_Thing_One_Click	0.529 54	0.667 57	0.718 54	0.777 28	0.399 55	0.683 52	0.000 66	0.669 33	0.138 61	0.391 56	0.374 58	0.539 31	0.360 62	0.641 61	0.556 42	0.774 32	0.593 43	0.997 38	0.251 61
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN	0.515 55	1.000 1	0.538 67	0.282 61	0.468 53	0.790 40	0.173 35	0.345 63	0.429 40	0.413 52	0.484 44	0.176 58	0.595 45	0.591 62	0.522 45	0.668 56	0.476 60	0.986 52	0.327 57

Occipital-SCS	0.512 56	1.000 1	0.716 55	0.509 56	0.506 48	0.611 58	0.092 48	0.602 47	0.177 59	0.346 59	0.383 56	0.165 59	0.442 56	0.850 53	0.386 58	0.618 61	0.543 56	0.889 59	0.389 52

3D-BoNet	0.488 57	1.000 1	0.672 61	0.590 52	0.301 61	0.484 68	0.098 47	0.620 44	0.306 52	0.341 60	0.259 63	0.125 61	0.434 58	0.796 56	0.402 55	0.499 67	0.513 58	0.909 58	0.439 47
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst	0.478 58	0.667 57	0.712 57	0.595 51	0.259 64	0.550 64	0.000 66	0.613 45	0.175 60	0.250 65	0.434 48	0.437 41	0.411 60	0.857 46	0.485 47	0.591 64	0.267 70	0.944 54	0.359 54
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS	0.470 59	0.667 57	0.685 60	0.677 43	0.372 57	0.562 62	0.000 66	0.482 58	0.244 54	0.316 62	0.298 60	0.052 68	0.442 57	0.857 46	0.267 62	0.702 49	0.559 51	1.000 1	0.287 59

SALoss-ResNet	0.459 60	1.000 1	0.737 53	0.159 71	0.259 63	0.587 60	0.138 42	0.475 59	0.217 57	0.416 51	0.408 53	0.128 60	0.315 64	0.714 57	0.411 54	0.536 66	0.590 44	0.873 63	0.304 58
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.447 61	0.528 67	0.555 65	0.381 58	0.382 56	0.633 56	0.002 64	0.509 56	0.260 53	0.361 58	0.432 50	0.327 53	0.451 54	0.571 63	0.367 59	0.639 59	0.386 61	0.980 53	0.276 60
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_ins	0.445 62	0.667 57	0.773 44	0.185 68	0.317 60	0.656 54	0.000 66	0.407 62	0.134 62	0.381 57	0.267 62	0.217 56	0.476 53	0.714 57	0.452 50	0.629 60	0.514 57	1.000 1	0.222 64
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SIS	0.382 63	1.000 1	0.432 70	0.245 63	0.190 65	0.577 61	0.013 60	0.263 65	0.033 68	0.320 61	0.240 64	0.075 64	0.422 59	0.857 46	0.117 68	0.699 50	0.271 69	0.883 61	0.235 63
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.323 64	0.667 57	0.542 66	0.264 62	0.157 68	0.550 63	0.000 66	0.205 68	0.009 70	0.270 64	0.218 65	0.075 64	0.500 51	0.688 60	0.007 74	0.698 51	0.301 66	0.459 71	0.200 65
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone	0.319 65	0.667 57	0.715 56	0.233 64	0.189 66	0.479 69	0.008 61	0.218 66	0.067 67	0.201 67	0.173 66	0.107 62	0.123 69	0.438 65	0.150 65	0.615 62	0.355 62	0.916 57	0.093 73

R-PointNet	0.306 66	0.500 69	0.405 71	0.311 59	0.348 58	0.589 59	0.054 53	0.068 71	0.126 63	0.283 63	0.290 61	0.028 69	0.219 67	0.214 68	0.331 60	0.396 71	0.275 67	0.821 65	0.245 62

Region-18class	0.284 67	0.250 73	0.751 52	0.228 66	0.270 62	0.521 65	0.000 66	0.468 60	0.008 72	0.205 66	0.127 67	0.000 73	0.068 71	0.070 72	0.262 64	0.652 58	0.323 64	0.740 67	0.173 66

SemRegionNet-20cls	0.250 68	0.333 70	0.613 63	0.229 65	0.163 67	0.493 66	0.000 66	0.304 64	0.107 64	0.147 70	0.100 69	0.052 67	0.231 65	0.119 70	0.039 70	0.445 69	0.325 63	0.654 68	0.141 69

3D-BEVIS	0.248 69	0.667 57	0.566 64	0.076 72	0.035 74	0.394 72	0.027 58	0.035 73	0.098 65	0.099 72	0.030 73	0.025 70	0.098 70	0.375 67	0.126 67	0.604 63	0.181 72	0.854 64	0.171 67
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
tmp	0.248 69	0.667 57	0.437 69	0.188 67	0.153 69	0.491 67	0.000 66	0.208 67	0.094 66	0.153 69	0.099 70	0.057 66	0.217 68	0.119 70	0.039 70	0.466 68	0.302 65	0.640 69	0.140 70

Sem_Recon_ins	0.227 71	0.764 56	0.486 68	0.069 73	0.098 71	0.426 71	0.017 59	0.067 72	0.015 69	0.172 68	0.100 68	0.096 63	0.054 73	0.183 69	0.135 66	0.366 72	0.260 71	0.614 70	0.168 68

ASIS	0.199 72	0.333 70	0.253 73	0.167 70	0.140 70	0.438 70	0.000 66	0.177 69	0.008 71	0.121 71	0.069 71	0.004 72	0.231 66	0.429 66	0.036 72	0.445 70	0.273 68	0.333 73	0.119 72

Sgpn_scannet	0.143 73	0.208 74	0.390 72	0.169 69	0.065 72	0.275 73	0.029 57	0.069 70	0.000 73	0.087 73	0.043 72	0.014 71	0.027 74	0.000 73	0.112 69	0.351 73	0.168 73	0.438 72	0.138 71

MaskRCNN 2d->3d Proj	0.058 74	0.333 70	0.002 74	0.000 74	0.053 73	0.002 74	0.002 65	0.021 74	0.000 73	0.045 74	0.024 74	0.238 55	0.065 72	0.000 73	0.014 73	0.107 74	0.020 74	0.110 74	0.006 74

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