Benchmark Results - ScanNet Benchmark

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

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

ALS-MinkowskiNet	0.414 1	0.610 2	0.322 3	0.271 1	0.542 2	0.153 2	0.159 11	0.000 1	0.000 4	0.000 1	0.404 4	0.503 3	0.532 6	0.672 15	0.804 5	0.285 2	0.888 1	0.000 3	0.900 1	0.226 1	0.087 2	0.598 3	0.342 4	0.671 1	0.217 9	0.087 3	0.449 3	0.000 1	0.000 3	0.253 2	0.477 5	1.000 1	0.000 1	0.118 4	0.000 2	0.905 1	0.071 11	0.710 2	0.076 1	0.047 14	0.665 1	0.376 8	0.981 1	0.000 1	0.000 2	0.466 7	0.632 6	0.113 3	0.769 1	0.956 3	0.795 1	0.031 10	0.314 1	0.936 1	0.000 1	0.390 2	0.601 1	0.000 7	0.458 7	0.366 2	0.719 3	0.440 4	0.564 1	0.699 3	0.314 2	0.464 7	0.784 2	0.200 1	0.283 6	0.973 1	0.142 9	0.000 1	0.250 5	0.285 6	0.220 5	0.718 1	0.752 5	0.723 2	0.460 1	0.248 14	0.475 7	0.463 12	0.000 4	0.000 1	0.446 7	0.021 4	0.025 7	0.285 1	0.000 5	0.972 1	0.149 6	0.769 1	0.230 2	0.535 2	0.879 2	0.252 4	0.000 1	0.693 1	0.129 2	0.000 11	0.000 4	0.000 1	0.447 2	0.958 1	0.662 8	0.159 2	0.598 3	0.780 12	0.344 2	0.646 2	0.106 4	0.893 2	0.135 2	0.455 4	0.000 1	0.194 3	0.259 1	0.726 3	0.475 1	0.000 6	0.000 1	0.741 1	0.865 1	0.571 2	0.817 3	0.445 3	0.000 1	0.506 2	0.630 2	0.230 10	0.916 2	0.728 1	0.635 1	1.000 1	0.252 5	0.000 1	0.804 1	0.697 3	0.137 9	0.043 6	0.717 2	0.807 4	0.000 1	0.510 12	0.245 1	0.000 6	0.000 1	0.709 3	0.000 2	0.000 1	0.703 2	0.572 3	0.646 1	0.223 11	0.531 3	0.984 1	0.397 2	0.813 1	0.798 1	0.135 13	0.800 1	0.000 1	0.097 2	0.832 3	0.752 8	0.842 8	0.000 1	0.852 1	0.149 9	0.846 9	0.000 1	0.666 5	0.359 2	0.252 8	0.777 1	0.690 2

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

PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 2	0.520 3	0.109 4	0.108 14	0.000 1	0.337 2	0.000 1	0.310 11	0.394 8	0.494 11	0.753 9	0.848 2	0.256 4	0.717 6	0.000 3	0.842 4	0.192 4	0.065 4	0.449 7	0.346 3	0.546 6	0.190 10	0.000 8	0.384 6	0.000 1	0.000 3	0.218 3	0.505 1	0.791 2	0.000 1	0.136 3	0.000 2	0.903 2	0.073 10	0.687 7	0.000 7	0.168 1	0.551 4	0.387 7	0.941 2	0.000 1	0.000 2	0.397 11	0.654 4	0.000 10	0.714 5	0.759 13	0.752 8	0.118 5	0.264 4	0.926 2	0.000 1	0.048 5	0.575 4	0.000 7	0.597 1	0.366 2	0.755 1	0.469 1	0.474 2	0.798 1	0.140 8	0.617 2	0.692 6	0.000 7	0.592 3	0.971 2	0.188 3	0.000 1	0.133 8	0.593 2	0.349 1	0.650 3	0.717 7	0.699 3	0.455 2	0.790 2	0.523 3	0.636 1	0.301 1	0.000 1	0.622 2	0.000 8	0.017 13	0.259 3	0.000 5	0.921 4	0.337 2	0.733 2	0.210 3	0.514 3	0.860 8	0.407 1	0.000 1	0.688 2	0.109 7	0.000 11	0.000 4	0.000 1	0.151 4	0.671 7	0.782 1	0.115 10	0.641 2	0.903 2	0.349 1	0.616 4	0.088 6	0.832 5	0.000 6	0.480 3	0.000 1	0.428 1	0.000 3	0.497 9	0.000 2	0.000 6	0.000 1	0.662 4	0.690 2	0.612 1	0.828 1	0.575 1	0.000 1	0.404 6	0.644 1	0.325 6	0.887 4	0.728 1	0.009 13	0.134 6	0.026 15	0.000 1	0.761 3	0.731 1	0.172 4	0.077 3	0.528 6	0.727 6	0.000 1	0.603 5	0.220 3	0.022 3	0.000 1	0.740 1	0.000 2	0.000 1	0.661 4	0.586 2	0.566 3	0.436 5	0.531 3	0.978 2	0.457 1	0.708 3	0.583 6	0.141 8	0.748 3	0.000 1	0.026 4	0.822 4	0.871 4	0.879 5	0.000 1	0.851 2	0.405 2	0.914 1	0.000 1	0.682 3	0.000 13	0.281 4	0.738 2	0.463 6
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)
BFANet ScanNet200	0.360 4	0.553 6	0.293 4	0.193 4	0.483 9	0.096 5	0.266 6	0.000 1	0.000 4	0.000 1	0.298 12	0.255 11	0.661 1	0.810 5	0.810 3	0.194 8	0.785 5	0.000 3	0.000 15	0.161 6	0.000 8	0.494 6	0.382 2	0.574 4	0.258 4	0.000 8	0.372 8	0.000 1	0.000 3	0.043 11	0.436 7	0.000 9	0.000 1	0.239 2	0.000 2	0.901 3	0.105 1	0.689 5	0.025 4	0.128 2	0.614 2	0.436 1	0.493 15	0.000 1	0.000 2	0.526 4	0.546 11	0.109 4	0.651 12	0.953 4	0.753 7	0.101 6	0.143 11	0.897 4	0.000 1	0.431 1	0.469 13	0.000 7	0.522 5	0.337 5	0.661 7	0.459 2	0.409 4	0.666 4	0.102 11	0.508 6	0.757 4	0.000 7	0.060 12	0.970 3	0.497 1	0.000 1	0.376 2	0.511 4	0.262 4	0.688 2	0.921 1	0.617 8	0.321 12	0.590 5	0.491 6	0.556 3	0.000 4	0.000 1	0.481 4	0.093 1	0.043 2	0.284 2	0.000 5	0.875 13	0.135 7	0.669 5	0.124 11	0.394 6	0.849 12	0.298 2	0.000 1	0.476 15	0.088 12	0.042 5	0.000 4	0.000 1	0.254 3	0.653 9	0.741 4	0.215 1	0.573 5	0.852 6	0.266 8	0.654 1	0.056 11	0.835 3	0.000 6	0.492 2	0.000 1	0.000 6	0.000 3	0.612 8	0.000 2	0.000 6	0.000 1	0.616 5	0.469 15	0.460 4	0.698 11	0.516 2	0.000 1	0.378 7	0.563 4	0.476 3	0.863 5	0.574 8	0.330 6	0.000 10	0.282 4	0.000 1	0.760 4	0.710 2	0.233 1	0.000 9	0.641 4	0.814 3	0.000 1	0.585 8	0.053 10	0.000 6	0.000 1	0.629 9	0.000 2	0.000 1	0.678 3	0.528 10	0.534 4	0.129 12	0.596 2	0.973 3	0.264 10	0.772 2	0.526 8	0.139 10	0.707 4	0.000 1	0.000 11	0.764 12	0.591 14	0.848 7	0.000 1	0.827 4	0.338 3	0.806 11	0.000 1	0.568 7	0.151 6	0.358 2	0.659 10	0.510 4

PonderV2 ScanNet200	0.346 5	0.552 7	0.270 8	0.175 6	0.497 7	0.070 12	0.239 7	0.000 1	0.000 4	0.000 1	0.232 14	0.412 7	0.584 3	0.842 3	0.804 5	0.212 7	0.540 8	0.000 3	0.433 14	0.106 10	0.000 8	0.590 4	0.290 9	0.548 5	0.243 6	0.000 8	0.356 10	0.000 1	0.000 3	0.062 9	0.398 10	0.441 6	0.000 1	0.104 9	0.000 2	0.888 4	0.076 9	0.682 8	0.030 3	0.094 6	0.491 9	0.351 11	0.869 9	0.000 1	0.063 1	0.403 10	0.700 2	0.000 10	0.660 11	0.881 7	0.761 4	0.050 9	0.186 8	0.852 11	0.000 1	0.007 8	0.570 7	0.100 2	0.565 2	0.326 6	0.641 10	0.431 5	0.290 12	0.621 5	0.259 4	0.408 9	0.622 10	0.125 2	0.082 10	0.950 4	0.179 5	0.000 1	0.263 3	0.424 5	0.193 8	0.558 6	0.880 2	0.545 11	0.375 7	0.727 3	0.445 9	0.499 8	0.000 4	0.000 1	0.475 6	0.002 6	0.034 5	0.083 7	0.000 5	0.924 3	0.290 4	0.636 6	0.115 12	0.400 5	0.874 4	0.186 7	0.000 1	0.611 8	0.128 3	0.113 2	0.000 4	0.000 1	0.000 8	0.584 10	0.636 9	0.103 11	0.385 9	0.843 7	0.283 4	0.603 6	0.080 7	0.825 7	0.000 6	0.377 10	0.000 1	0.000 6	0.000 3	0.457 11	0.000 2	0.000 6	0.000 1	0.574 12	0.608 8	0.481 3	0.792 4	0.394 4	0.000 1	0.357 9	0.503 10	0.261 9	0.817 10	0.504 12	0.304 7	0.472 4	0.115 8	0.000 1	0.750 6	0.677 6	0.202 2	0.000 9	0.509 7	0.729 5	0.000 1	0.519 11	0.000 13	0.000 6	0.000 1	0.620 11	0.000 2	0.000 1	0.660 6	0.560 6	0.486 5	0.384 7	0.346 7	0.952 5	0.247 12	0.667 4	0.436 10	0.269 3	0.691 6	0.000 1	0.010 6	0.787 8	0.889 3	0.880 4	0.000 1	0.810 8	0.336 4	0.860 7	0.000 1	0.606 6	0.009 8	0.248 9	0.681 7	0.392 9
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.
CeCo	0.340 6	0.551 8	0.247 11	0.181 5	0.475 11	0.057 15	0.142 12	0.000 1	0.000 4	0.000 1	0.387 5	0.463 5	0.499 9	0.924 2	0.774 10	0.213 6	0.257 11	0.000 3	0.546 13	0.100 11	0.006 7	0.615 1	0.177 15	0.534 8	0.246 5	0.000 8	0.400 4	0.000 1	0.338 1	0.006 14	0.484 4	0.609 3	0.000 1	0.083 11	0.000 2	0.873 10	0.089 5	0.661 12	0.000 7	0.048 13	0.560 3	0.408 6	0.892 7	0.000 1	0.000 2	0.586 1	0.616 7	0.000 10	0.692 8	0.900 5	0.721 10	0.162 1	0.228 6	0.860 9	0.000 1	0.000 10	0.575 4	0.083 3	0.550 3	0.347 4	0.624 11	0.410 10	0.360 7	0.740 2	0.109 10	0.321 13	0.660 7	0.000 7	0.121 7	0.939 11	0.143 8	0.000 1	0.400 1	0.003 11	0.190 10	0.564 5	0.652 9	0.615 9	0.421 3	0.304 12	0.579 1	0.547 4	0.000 4	0.000 1	0.296 12	0.000 8	0.030 6	0.096 6	0.000 5	0.916 5	0.037 11	0.551 9	0.171 6	0.376 7	0.865 6	0.286 3	0.000 1	0.633 5	0.102 11	0.027 6	0.011 3	0.000 1	0.000 8	0.474 12	0.742 3	0.133 7	0.311 11	0.824 9	0.242 11	0.503 12	0.068 8	0.828 6	0.000 6	0.429 7	0.000 1	0.063 5	0.000 3	0.781 1	0.000 2	0.000 6	0.000 1	0.665 3	0.633 6	0.450 5	0.818 2	0.000 9	0.000 1	0.429 4	0.532 7	0.226 11	0.825 8	0.510 11	0.377 4	0.709 2	0.079 11	0.000 1	0.753 5	0.683 5	0.102 13	0.063 4	0.401 14	0.620 12	0.000 1	0.619 2	0.000 13	0.000 6	0.000 1	0.595 13	0.000 2	0.000 1	0.345 12	0.564 5	0.411 8	0.603 1	0.384 6	0.945 7	0.266 9	0.643 6	0.367 12	0.304 1	0.663 10	0.000 1	0.010 6	0.726 13	0.767 7	0.898 3	0.000 1	0.784 11	0.435 1	0.861 6	0.000 1	0.447 9	0.000 13	0.257 7	0.656 11	0.377 10
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
GSTran	0.339 7	0.536 10	0.273 6	0.169 7	0.491 8	0.071 11	0.365 3	0.000 1	0.000 4	0.000 1	0.178 15	0.246 13	0.458 12	0.754 8	0.788 8	0.316 1	0.834 4	0.000 3	0.872 2	0.202 3	0.079 3	0.318 12	0.286 10	0.538 7	0.156 12	0.004 7	0.310 11	0.000 1	0.000 3	0.009 13	0.397 11	0.297 7	0.000 1	0.093 10	0.000 2	0.876 7	0.060 12	0.690 4	0.000 7	0.086 7	0.517 7	0.358 9	0.667 13	0.000 1	0.000 2	0.473 6	0.670 3	0.000 10	0.731 3	0.896 6	0.765 2	0.061 8	0.256 5	0.889 6	0.000 1	0.000 10	0.480 12	0.000 7	0.412 11	0.279 8	0.690 4	0.366 12	0.373 6	0.466 9	0.357 1	0.514 5	0.648 8	0.024 5	0.615 2	0.949 5	0.183 4	0.000 1	0.162 7	0.564 3	0.196 7	0.535 8	0.413 13	0.638 5	0.410 4	0.682 4	0.445 9	0.470 11	0.289 2	0.000 1	0.358 11	0.000 8	0.022 9	0.161 4	0.008 4	0.877 12	0.495 1	0.461 14	0.161 9	0.348 10	0.853 11	0.199 6	0.000 1	0.643 4	0.109 7	0.014 8	0.000 4	0.000 1	0.000 8	0.681 6	0.705 6	0.079 14	0.441 8	0.872 3	0.282 5	0.593 7	0.096 5	0.786 10	0.021 3	0.495 1	0.000 1	0.118 4	0.000 3	0.487 10	0.000 2	0.002 5	0.000 1	0.589 8	0.563 11	0.144 14	0.682 12	0.109 7	0.000 1	0.235 14	0.455 11	0.368 5	0.659 15	0.609 6	0.000 15	0.060 9	0.033 14	0.000 1	0.746 7	0.648 11	0.084 14	0.000 9	0.803 1	0.832 1	0.000 1	0.614 4	0.000 13	0.497 1	0.000 1	0.597 12	0.000 2	0.000 1	0.621 9	0.506 11	0.459 6	0.252 10	0.228 11	0.913 12	0.369 4	0.665 5	0.598 4	0.139 10	0.666 8	0.000 1	0.097 2	0.841 2	0.698 10	0.857 6	0.000 1	0.811 7	0.129 11	0.784 12	0.000 1	0.386 10	0.012 7	0.317 3	0.696 5	0.425 7

L3DETR-ScanNet_200	0.336 8	0.533 11	0.279 5	0.155 8	0.508 5	0.073 10	0.101 15	0.000 1	0.058 3	0.000 1	0.294 13	0.233 14	0.548 4	0.927 1	0.788 8	0.264 3	0.463 9	0.000 3	0.638 10	0.098 13	0.014 6	0.411 9	0.226 11	0.525 11	0.225 8	0.010 5	0.397 5	0.000 1	0.000 3	0.192 5	0.380 12	0.598 4	0.000 1	0.117 5	0.000 2	0.883 5	0.082 7	0.689 5	0.000 7	0.032 15	0.549 5	0.417 5	0.910 5	0.000 1	0.000 2	0.448 9	0.613 8	0.000 10	0.697 7	0.960 2	0.759 5	0.158 2	0.293 2	0.883 7	0.000 1	0.312 3	0.583 3	0.079 4	0.422 10	0.068 15	0.660 8	0.418 7	0.298 10	0.430 11	0.114 9	0.526 4	0.776 3	0.051 3	0.679 1	0.946 7	0.152 7	0.000 1	0.183 6	0.000 13	0.211 6	0.511 10	0.409 14	0.565 10	0.355 8	0.448 7	0.512 4	0.557 2	0.000 4	0.000 1	0.420 8	0.000 8	0.007 15	0.104 5	0.000 5	0.125 15	0.330 3	0.514 12	0.146 10	0.321 11	0.860 8	0.174 8	0.000 1	0.629 6	0.075 13	0.000 11	0.000 4	0.000 1	0.002 7	0.671 7	0.712 5	0.141 6	0.339 10	0.856 5	0.261 10	0.529 10	0.067 9	0.835 3	0.000 6	0.369 12	0.000 1	0.259 2	0.000 3	0.629 5	0.000 2	0.487 1	0.000 1	0.579 11	0.646 3	0.107 15	0.720 10	0.122 6	0.000 1	0.333 11	0.505 9	0.303 8	0.908 3	0.503 13	0.565 2	0.074 8	0.324 2	0.000 1	0.740 8	0.661 8	0.109 11	0.000 9	0.427 11	0.563 15	0.000 1	0.579 9	0.108 7	0.000 6	0.000 1	0.664 5	0.000 2	0.000 1	0.641 7	0.539 8	0.416 7	0.515 2	0.256 8	0.940 10	0.312 6	0.209 15	0.620 3	0.138 12	0.636 11	0.000 1	0.000 11	0.775 11	0.861 5	0.765 10	0.000 1	0.801 10	0.119 12	0.860 7	0.000 1	0.687 2	0.001 12	0.192 14	0.679 9	0.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OA-CNN-L_ScanNet200	0.333 9	0.558 4	0.269 9	0.124 11	0.448 13	0.080 8	0.272 5	0.000 1	0.000 4	0.000 1	0.342 7	0.515 2	0.524 7	0.713 13	0.789 7	0.158 10	0.384 10	0.000 3	0.806 6	0.125 7	0.000 8	0.496 5	0.332 6	0.498 14	0.227 7	0.024 4	0.474 2	0.000 1	0.003 2	0.071 8	0.487 3	0.000 9	0.000 1	0.110 7	0.000 2	0.876 7	0.013 15	0.703 3	0.000 7	0.076 9	0.473 11	0.355 10	0.906 6	0.000 1	0.000 2	0.476 5	0.706 1	0.000 10	0.672 10	0.835 11	0.748 9	0.015 14	0.223 7	0.860 9	0.000 1	0.000 10	0.572 6	0.000 7	0.509 6	0.313 7	0.662 5	0.398 11	0.396 5	0.411 12	0.276 3	0.527 3	0.711 5	0.000 7	0.076 11	0.946 7	0.166 6	0.000 1	0.022 9	0.160 7	0.183 11	0.493 11	0.699 8	0.637 6	0.403 5	0.330 11	0.406 11	0.526 5	0.024 3	0.000 1	0.392 10	0.000 8	0.016 14	0.000 10	0.196 3	0.915 6	0.112 10	0.557 8	0.197 4	0.352 9	0.877 3	0.000 10	0.000 1	0.592 12	0.103 10	0.000 11	0.067 1	0.000 1	0.089 5	0.735 5	0.625 10	0.130 9	0.568 6	0.836 8	0.271 6	0.534 9	0.043 13	0.799 8	0.001 5	0.445 6	0.000 1	0.000 6	0.024 2	0.661 4	0.000 2	0.262 2	0.000 1	0.591 7	0.517 13	0.373 8	0.788 6	0.021 8	0.000 1	0.455 3	0.517 8	0.320 7	0.823 9	0.200 15	0.001 14	0.150 5	0.100 9	0.000 1	0.736 9	0.668 7	0.103 12	0.052 5	0.662 3	0.720 7	0.000 1	0.602 6	0.112 6	0.002 5	0.000 1	0.637 8	0.000 2	0.000 1	0.621 9	0.569 4	0.398 9	0.412 6	0.234 9	0.949 6	0.363 5	0.492 13	0.495 9	0.251 4	0.665 9	0.000 1	0.001 10	0.805 6	0.833 6	0.794 9	0.000 1	0.821 5	0.314 5	0.843 10	0.000 1	0.560 8	0.245 3	0.262 6	0.713 4	0.370 11

PPT-SpUNet-F.T.	0.332 10	0.556 5	0.270 7	0.123 12	0.519 4	0.091 6	0.349 4	0.000 1	0.000 4	0.000 1	0.339 8	0.383 9	0.498 10	0.833 4	0.807 4	0.241 5	0.584 7	0.000 3	0.755 7	0.124 8	0.000 8	0.608 2	0.330 7	0.530 10	0.314 2	0.000 8	0.374 7	0.000 1	0.000 3	0.197 4	0.459 6	0.000 9	0.000 1	0.117 5	0.000 2	0.876 7	0.095 2	0.682 8	0.000 7	0.086 7	0.518 6	0.433 2	0.930 4	0.000 1	0.000 2	0.563 3	0.542 12	0.077 7	0.715 4	0.858 9	0.756 6	0.008 15	0.171 10	0.874 8	0.000 1	0.039 6	0.550 8	0.000 7	0.545 4	0.256 9	0.657 9	0.453 3	0.351 8	0.449 10	0.213 5	0.392 10	0.611 11	0.000 7	0.037 13	0.946 7	0.138 12	0.000 1	0.000 11	0.063 9	0.308 2	0.537 7	0.796 4	0.673 4	0.323 11	0.392 9	0.400 12	0.509 7	0.000 4	0.000 1	0.649 1	0.000 8	0.023 8	0.000 10	0.000 5	0.914 7	0.002 14	0.506 13	0.163 8	0.359 8	0.872 5	0.000 10	0.000 1	0.623 7	0.112 5	0.001 10	0.000 4	0.000 1	0.021 6	0.753 3	0.565 14	0.150 4	0.579 4	0.806 10	0.267 7	0.616 4	0.042 14	0.783 11	0.000 6	0.374 11	0.000 1	0.000 6	0.000 3	0.620 7	0.000 2	0.000 6	0.000 1	0.572 13	0.634 5	0.350 9	0.792 4	0.000 9	0.000 1	0.376 8	0.535 6	0.378 4	0.855 6	0.672 3	0.074 10	0.000 10	0.185 7	0.000 1	0.727 10	0.660 9	0.076 15	0.000 9	0.432 10	0.646 9	0.000 1	0.594 7	0.006 12	0.000 6	0.000 1	0.658 6	0.000 2	0.000 1	0.661 4	0.549 7	0.300 12	0.291 9	0.045 12	0.942 9	0.304 7	0.600 8	0.572 7	0.135 13	0.695 5	0.000 1	0.008 8	0.793 7	0.942 1	0.899 2	0.000 1	0.816 6	0.181 7	0.897 2	0.000 1	0.679 4	0.223 4	0.264 5	0.691 6	0.345 12
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 ScanNet200	0.326 11	0.539 9	0.265 10	0.131 10	0.499 6	0.110 3	0.522 1	0.000 1	0.000 4	0.000 1	0.318 10	0.427 6	0.455 13	0.743 11	0.765 11	0.175 9	0.842 3	0.000 3	0.828 5	0.204 2	0.033 5	0.429 8	0.335 5	0.601 3	0.312 3	0.000 8	0.357 9	0.000 1	0.000 3	0.047 10	0.423 8	0.000 9	0.000 1	0.105 8	0.000 2	0.873 10	0.079 8	0.670 11	0.000 7	0.117 4	0.471 12	0.432 3	0.829 10	0.000 1	0.000 2	0.584 2	0.417 15	0.089 6	0.684 9	0.837 10	0.705 14	0.021 13	0.178 9	0.892 5	0.000 1	0.028 7	0.505 10	0.000 7	0.457 8	0.200 12	0.662 5	0.412 9	0.244 13	0.496 7	0.000 15	0.451 8	0.626 9	0.000 7	0.102 9	0.943 10	0.138 12	0.000 1	0.000 11	0.149 8	0.291 3	0.534 9	0.722 6	0.632 7	0.331 10	0.253 13	0.453 8	0.487 9	0.000 4	0.000 1	0.479 5	0.000 8	0.022 9	0.000 10	0.000 5	0.900 8	0.128 9	0.684 3	0.164 7	0.413 4	0.854 10	0.000 10	0.000 1	0.512 14	0.074 14	0.003 9	0.000 4	0.000 1	0.000 8	0.469 13	0.613 11	0.132 8	0.529 7	0.871 4	0.227 14	0.582 8	0.026 15	0.787 9	0.000 6	0.339 13	0.000 1	0.000 6	0.000 3	0.626 6	0.000 2	0.029 4	0.000 1	0.587 9	0.612 7	0.411 7	0.724 9	0.000 9	0.000 1	0.407 5	0.552 5	0.513 1	0.849 7	0.655 4	0.408 3	0.000 10	0.296 3	0.000 1	0.686 13	0.645 12	0.145 7	0.022 7	0.414 12	0.633 10	0.000 1	0.637 1	0.224 2	0.000 6	0.000 1	0.650 7	0.000 2	0.000 1	0.622 8	0.535 9	0.343 10	0.483 3	0.230 10	0.943 8	0.289 8	0.618 7	0.596 5	0.140 9	0.679 7	0.000 1	0.022 5	0.783 9	0.620 12	0.906 1	0.000 1	0.806 9	0.137 10	0.865 4	0.000 1	0.378 11	0.000 13	0.168 15	0.680 8	0.227 14
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
AWCS	0.305 12	0.508 12	0.225 12	0.142 9	0.463 12	0.063 13	0.195 9	0.000 1	0.000 4	0.000 1	0.467 3	0.551 1	0.504 8	0.773 6	0.764 12	0.142 12	0.029 15	0.000 3	0.626 11	0.100 11	0.000 8	0.360 10	0.179 13	0.507 13	0.137 13	0.006 6	0.300 12	0.000 1	0.000 3	0.172 7	0.364 13	0.512 5	0.000 1	0.056 12	0.000 2	0.865 12	0.093 4	0.634 15	0.000 7	0.071 11	0.396 13	0.296 14	0.876 8	0.000 1	0.000 2	0.373 12	0.436 14	0.063 9	0.749 2	0.877 8	0.721 10	0.131 4	0.124 12	0.804 13	0.000 1	0.000 10	0.515 9	0.010 6	0.452 9	0.252 10	0.578 12	0.417 8	0.179 15	0.484 8	0.171 6	0.337 12	0.606 12	0.000 7	0.115 8	0.937 12	0.142 9	0.000 1	0.008 10	0.000 13	0.157 14	0.484 12	0.402 15	0.501 13	0.339 9	0.553 6	0.529 2	0.478 10	0.000 4	0.000 1	0.404 9	0.001 7	0.022 9	0.077 8	0.000 5	0.894 10	0.219 5	0.628 7	0.093 13	0.305 12	0.886 1	0.233 5	0.000 1	0.603 9	0.112 5	0.023 7	0.000 4	0.000 1	0.000 8	0.741 4	0.664 7	0.097 12	0.253 12	0.782 11	0.264 9	0.523 11	0.154 1	0.707 14	0.000 6	0.411 8	0.000 1	0.000 6	0.000 3	0.332 14	0.000 2	0.000 6	0.000 1	0.602 6	0.595 9	0.185 12	0.656 14	0.159 5	0.000 1	0.355 10	0.424 13	0.154 13	0.729 12	0.516 10	0.220 9	0.620 3	0.084 10	0.000 1	0.707 12	0.651 10	0.173 3	0.014 8	0.381 15	0.582 13	0.000 1	0.619 2	0.049 11	0.000 6	0.000 1	0.702 4	0.000 2	0.000 1	0.302 14	0.489 13	0.317 11	0.334 8	0.392 5	0.922 11	0.254 11	0.533 12	0.394 11	0.129 15	0.613 13	0.000 1	0.000 11	0.820 5	0.649 11	0.749 11	0.000 1	0.782 12	0.282 6	0.863 5	0.000 1	0.288 14	0.006 9	0.220 11	0.633 12	0.542 3

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

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

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

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

ScanNet Benchmark

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

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

PTv3-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 6	0.908 6	0.743 2	0.923 5	0.573 6	0.714 22	0.695 16	0.734 8	0.747 2	0.725 9	0.809 1	1.000 1	0.814 7	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 5	0.852 13	0.718 5	0.917 7	0.574 4	0.677 28	0.748 10	0.729 12	0.715 6	0.795 2	0.809 1	1.000 1	0.831 3	0.854 9	0.787 10	1.000 1	0.638 5

EV3D	0.811 3	1.000 1	0.968 7	0.852 13	0.717 6	0.921 6	0.574 5	0.677 28	0.748 10	0.730 11	0.703 11	0.795 2	0.809 1	1.000 1	0.831 3	0.854 9	0.778 14	1.000 1	0.638 6

SIM3D	0.803 4	1.000 1	0.967 8	0.863 12	0.692 16	0.924 4	0.552 9	0.732 21	0.667 20	0.732 10	0.662 14	0.796 1	0.789 9	1.000 1	0.803 8	0.864 6	0.766 19	1.000 1	0.643 4

OneFormer3D	0.801 5	1.000 1	0.973 4	0.909 5	0.698 13	0.928 2	0.582 3	0.668 33	0.685 17	0.780 2	0.687 12	0.698 17	0.702 14	1.000 1	0.794 10	0.900 2	0.784 12	0.986 50	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 11	0.915 8	0.532 11	0.733 20	0.757 9	0.733 9	0.708 8	0.698 16	0.648 33	0.981 36	0.890 1	0.830 18	0.796 7	0.997 37	0.644 3

UniPerception	0.800 6	1.000 1	0.930 10	0.872 10	0.727 4	0.862 22	0.454 17	0.764 13	0.820 1	0.746 6	0.706 9	0.750 5	0.772 10	0.926 43	0.764 16	0.818 26	0.826 1	0.997 37	0.660 2

InsSSM	0.799 8	1.000 1	0.915 12	0.710 39	0.729 3	0.925 3	0.664 1	0.670 31	0.770 6	0.766 3	0.739 3	0.737 6	0.700 15	1.000 1	0.792 11	0.829 20	0.815 4	0.997 37	0.625 9

TST3D	0.795 9	1.000 1	0.929 11	0.918 4	0.709 9	0.884 17	0.596 2	0.704 25	0.769 7	0.734 7	0.644 19	0.699 15	0.751 12	1.000 1	0.794 9	0.876 5	0.757 21	0.997 37	0.550 30
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 3	0.837 18	0.626 24	0.897 10	0.543 10	0.759 15	0.800 5	0.766 4	0.659 15	0.769 4	0.697 18	1.000 1	0.791 12	0.707 46	0.791 9	1.000 1	0.610 15

ExtMask3D	0.789 11	1.000 1	0.988 1	0.756 32	0.706 10	0.912 9	0.429 18	0.647 38	0.806 4	0.755 5	0.673 13	0.689 18	0.772 11	1.000 1	0.789 13	0.852 11	0.811 5	1.000 1	0.617 12

Queryformer	0.787 12	1.000 1	0.933 9	0.601 48	0.754 1	0.886 15	0.558 8	0.661 35	0.767 8	0.665 17	0.716 5	0.639 23	0.808 5	1.000 1	0.844 2	0.897 4	0.804 6	1.000 1	0.624 10

MAFT	0.786 13	1.000 1	0.894 17	0.807 22	0.694 15	0.893 13	0.486 13	0.674 30	0.740 12	0.786 1	0.704 10	0.727 8	0.739 13	1.000 1	0.707 22	0.849 13	0.756 22	1.000 1	0.685 1

Mask3D	0.780 14	1.000 1	0.786 41	0.716 37	0.696 14	0.885 16	0.500 12	0.714 22	0.810 3	0.672 16	0.715 6	0.679 19	0.809 1	1.000 1	0.831 3	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 15	0.903 54	0.903 14	0.806 23	0.609 30	0.886 14	0.568 7	0.815 6	0.705 15	0.711 13	0.655 16	0.652 22	0.685 21	1.000 1	0.789 14	0.809 27	0.776 16	1.000 1	0.583 23
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++	0.769 16	1.000 1	0.803 34	0.937 1	0.684 17	0.865 19	0.213 33	0.870 2	0.664 21	0.571 23	0.758 1	0.702 13	0.807 6	1.000 1	0.653 29	0.902 1	0.792 8	1.000 1	0.626 8

SoftGroup	0.761 17	1.000 1	0.808 30	0.845 15	0.716 7	0.862 21	0.243 30	0.824 4	0.655 23	0.620 18	0.734 4	0.699 14	0.791 8	0.981 36	0.716 20	0.844 14	0.769 17	1.000 1	0.594 21
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 18	1.000 1	0.904 13	0.731 35	0.678 18	0.895 11	0.458 15	0.644 40	0.670 19	0.710 14	0.620 24	0.732 7	0.650 23	1.000 1	0.756 17	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 19	1.000 1	0.774 42	0.867 11	0.621 26	0.934 1	0.404 19	0.706 24	0.812 2	0.605 21	0.633 22	0.626 24	0.690 20	1.000 1	0.640 31	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 20	1.000 1	0.818 26	0.837 19	0.713 8	0.844 24	0.457 16	0.647 38	0.711 14	0.614 19	0.617 26	0.657 21	0.650 23	1.000 1	0.692 23	0.822 22	0.765 20	1.000 1	0.595 20
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 21	1.000 1	0.788 39	0.724 36	0.642 23	0.859 23	0.248 29	0.787 11	0.618 26	0.596 22	0.653 18	0.722 11	0.583 45	1.000 1	0.766 15	0.861 7	0.825 2	1.000 1	0.504 36

IPCA-Inst	0.731 22	1.000 1	0.788 40	0.884 9	0.698 12	0.788 40	0.252 28	0.760 14	0.646 24	0.511 31	0.637 21	0.665 20	0.804 7	1.000 1	0.644 30	0.778 31	0.747 24	1.000 1	0.561 27

TopoSeg	0.725 23	1.000 1	0.806 33	0.933 2	0.668 20	0.758 44	0.272 27	0.734 19	0.630 25	0.549 27	0.654 17	0.606 25	0.697 19	0.966 40	0.612 35	0.839 15	0.754 23	1.000 1	0.573 24

DKNet	0.718 24	1.000 1	0.814 27	0.782 26	0.619 27	0.872 18	0.224 31	0.751 17	0.569 30	0.677 15	0.585 30	0.724 10	0.633 35	0.981 36	0.515 45	0.819 24	0.736 25	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 25	1.000 1	0.850 19	0.924 3	0.648 21	0.747 47	0.162 35	0.862 3	0.572 29	0.520 29	0.624 23	0.549 28	0.649 32	1.000 1	0.560 40	0.706 47	0.768 18	1.000 1	0.591 22

HAIS	0.699 26	1.000 1	0.849 20	0.820 20	0.675 19	0.808 34	0.279 25	0.757 16	0.465 36	0.517 30	0.596 28	0.559 27	0.600 39	1.000 1	0.654 28	0.767 33	0.676 29	0.994 46	0.560 28
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet	0.698 27	1.000 1	0.697 58	0.888 8	0.556 37	0.803 35	0.387 20	0.626 42	0.417 41	0.556 26	0.585 31	0.702 12	0.600 39	1.000 1	0.824 6	0.720 45	0.692 27	1.000 1	0.509 35
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup	0.694 28	1.000 1	0.799 36	0.811 21	0.622 25	0.817 29	0.376 21	0.805 9	0.590 28	0.487 35	0.568 34	0.525 32	0.650 23	0.835 53	0.600 36	0.829 19	0.655 32	1.000 1	0.526 32

SphereSeg	0.680 29	1.000 1	0.856 18	0.744 33	0.618 28	0.893 12	0.151 36	0.651 37	0.713 13	0.537 28	0.579 33	0.430 42	0.651 22	1.000 1	0.389 56	0.744 40	0.697 26	0.991 48	0.601 19

DANCENET	0.680 29	1.000 1	0.807 31	0.733 34	0.600 31	0.768 43	0.375 22	0.543 50	0.538 31	0.610 20	0.599 27	0.498 33	0.632 37	0.981 36	0.739 19	0.856 8	0.633 38	0.882 61	0.454 45

Box2Mask	0.677 31	1.000 1	0.847 21	0.771 28	0.509 46	0.816 30	0.277 26	0.558 49	0.482 33	0.562 25	0.640 20	0.448 38	0.700 15	1.000 1	0.666 24	0.852 12	0.578 45	0.997 37	0.488 40
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 32	1.000 1	0.758 50	0.682 41	0.576 35	0.842 25	0.477 14	0.504 56	0.524 32	0.567 24	0.585 32	0.451 37	0.557 47	1.000 1	0.751 18	0.797 28	0.563 48	1.000 1	0.467 44
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group	0.664 33	1.000 1	0.822 25	0.764 31	0.616 29	0.815 31	0.139 40	0.694 27	0.597 27	0.459 39	0.566 35	0.599 26	0.600 39	0.516 63	0.715 21	0.819 25	0.635 36	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 34	1.000 1	0.760 48	0.667 43	0.581 33	0.863 20	0.323 23	0.655 36	0.477 34	0.473 37	0.549 37	0.432 41	0.650 23	1.000 1	0.655 27	0.738 41	0.585 44	0.944 53	0.472 43

CSC-Pretrained	0.648 35	1.000 1	0.810 28	0.768 29	0.523 44	0.813 32	0.143 39	0.819 5	0.389 44	0.422 48	0.511 41	0.443 39	0.650 23	1.000 1	0.624 33	0.732 42	0.634 37	1.000 1	0.375 52

PE	0.645 36	1.000 1	0.773 44	0.798 25	0.538 39	0.786 41	0.088 48	0.799 10	0.350 48	0.435 46	0.547 38	0.545 29	0.646 34	0.933 42	0.562 39	0.761 36	0.556 53	0.997 37	0.501 38
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN	0.643 37	1.000 1	0.758 49	0.582 54	0.539 38	0.826 28	0.046 53	0.765 12	0.372 46	0.436 45	0.588 29	0.539 31	0.650 23	1.000 1	0.577 37	0.750 38	0.653 34	0.997 37	0.495 39
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3D	0.641 38	1.000 1	0.841 22	0.893 7	0.531 41	0.802 36	0.115 45	0.588 47	0.448 38	0.438 43	0.537 40	0.430 43	0.550 48	0.857 45	0.534 43	0.764 35	0.657 31	0.987 49	0.568 25
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN	0.638 39	1.000 1	0.895 16	0.800 24	0.480 50	0.676 52	0.144 38	0.737 18	0.354 47	0.447 40	0.400 54	0.365 49	0.700 15	1.000 1	0.569 38	0.836 16	0.599 40	1.000 1	0.473 42

PointGroup	0.636 40	1.000 1	0.765 45	0.624 45	0.505 48	0.797 37	0.116 44	0.696 26	0.384 45	0.441 41	0.559 36	0.476 35	0.596 42	1.000 1	0.666 24	0.756 37	0.556 52	0.997 37	0.513 34
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 41	0.667 56	0.797 38	0.714 38	0.562 36	0.774 42	0.146 37	0.810 8	0.429 40	0.476 36	0.546 39	0.399 45	0.633 35	1.000 1	0.632 32	0.722 44	0.609 39	1.000 1	0.514 33
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 42	1.000 1	0.829 24	0.606 47	0.646 22	0.836 26	0.068 49	0.511 54	0.462 37	0.507 32	0.619 25	0.389 47	0.610 38	1.000 1	0.432 51	0.828 21	0.673 30	0.788 65	0.552 29

DENet	0.629 43	1.000 1	0.797 37	0.608 46	0.589 32	0.627 56	0.219 32	0.882 1	0.310 50	0.402 53	0.383 56	0.396 46	0.650 23	1.000 1	0.663 26	0.543 64	0.691 28	1.000 1	0.568 26

3D-MPA	0.611 44	1.000 1	0.833 23	0.765 30	0.526 43	0.756 45	0.136 42	0.588 47	0.470 35	0.438 44	0.432 50	0.358 51	0.650 23	0.857 45	0.429 52	0.765 34	0.557 51	1.000 1	0.430 47
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 45	1.000 1	0.801 35	0.599 49	0.535 40	0.728 49	0.286 24	0.436 60	0.679 18	0.491 33	0.433 48	0.256 53	0.404 60	0.857 45	0.620 34	0.724 43	0.510 58	1.000 1	0.539 31

AOIA	0.601 46	1.000 1	0.761 47	0.687 40	0.485 49	0.828 27	0.008 60	0.663 34	0.405 43	0.405 52	0.425 51	0.490 34	0.596 42	0.714 56	0.553 42	0.779 29	0.597 41	0.992 47	0.424 49

PCJC	0.578 47	1.000 1	0.810 29	0.583 53	0.449 53	0.813 33	0.042 54	0.603 45	0.341 49	0.490 34	0.465 45	0.410 44	0.650 23	0.835 53	0.264 62	0.694 51	0.561 49	0.889 58	0.504 37

SSEN	0.575 48	1.000 1	0.761 46	0.473 56	0.477 51	0.795 38	0.066 50	0.529 52	0.658 22	0.460 38	0.461 46	0.380 48	0.331 62	0.859 44	0.401 55	0.692 53	0.653 33	1.000 1	0.348 54
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 49	0.528 66	0.708 57	0.626 44	0.580 34	0.745 48	0.063 51	0.627 41	0.240 54	0.400 54	0.497 42	0.464 36	0.515 49	1.000 1	0.475 47	0.745 39	0.571 46	1.000 1	0.429 48

NeuralBF	0.555 50	0.667 56	0.896 15	0.843 17	0.517 45	0.751 46	0.029 55	0.519 53	0.414 42	0.439 42	0.465 44	0.000 72	0.484 51	0.857 45	0.287 60	0.693 52	0.651 35	1.000 1	0.485 41
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 51	1.000 1	0.807 32	0.588 52	0.327 58	0.647 54	0.004 62	0.815 7	0.180 57	0.418 49	0.364 58	0.182 56	0.445 54	1.000 1	0.442 50	0.688 54	0.571 47	1.000 1	0.396 50
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance	0.539 52	1.000 1	0.621 61	0.300 59	0.530 42	0.698 50	0.127 43	0.533 51	0.222 55	0.430 47	0.400 53	0.365 49	0.574 46	0.938 41	0.472 48	0.659 56	0.543 54	0.944 53	0.347 55

One_Thing_One_Click	0.529 53	0.667 56	0.718 53	0.777 27	0.399 54	0.683 51	0.000 65	0.669 32	0.138 60	0.391 55	0.374 57	0.539 30	0.360 61	0.641 60	0.556 41	0.774 32	0.593 42	0.997 37	0.251 60
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 54	1.000 1	0.538 66	0.282 60	0.468 52	0.790 39	0.173 34	0.345 62	0.429 39	0.413 51	0.484 43	0.176 57	0.595 44	0.591 61	0.522 44	0.668 55	0.476 59	0.986 51	0.327 56

Occipital-SCS	0.512 55	1.000 1	0.716 54	0.509 55	0.506 47	0.611 57	0.092 47	0.602 46	0.177 58	0.346 58	0.383 55	0.165 58	0.442 55	0.850 52	0.386 57	0.618 60	0.543 55	0.889 58	0.389 51

3D-BoNet	0.488 56	1.000 1	0.672 60	0.590 51	0.301 60	0.484 67	0.098 46	0.620 43	0.306 51	0.341 59	0.259 62	0.125 60	0.434 57	0.796 55	0.402 54	0.499 66	0.513 57	0.909 57	0.439 46
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 57	0.667 56	0.712 56	0.595 50	0.259 63	0.550 63	0.000 65	0.613 44	0.175 59	0.250 64	0.434 47	0.437 40	0.411 59	0.857 45	0.485 46	0.591 63	0.267 69	0.944 53	0.359 53
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 58	0.667 56	0.685 59	0.677 42	0.372 56	0.562 61	0.000 65	0.482 57	0.244 53	0.316 61	0.298 59	0.052 67	0.442 56	0.857 45	0.267 61	0.702 48	0.559 50	1.000 1	0.287 58

SALoss-ResNet	0.459 59	1.000 1	0.737 52	0.159 70	0.259 62	0.587 59	0.138 41	0.475 58	0.217 56	0.416 50	0.408 52	0.128 59	0.315 63	0.714 56	0.411 53	0.536 65	0.590 43	0.873 62	0.304 57
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASC	0.447 60	0.528 66	0.555 64	0.381 57	0.382 55	0.633 55	0.002 63	0.509 55	0.260 52	0.361 57	0.432 49	0.327 52	0.451 53	0.571 62	0.367 58	0.639 58	0.386 60	0.980 52	0.276 59
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_ins	0.445 61	0.667 56	0.773 43	0.185 67	0.317 59	0.656 53	0.000 65	0.407 61	0.134 61	0.381 56	0.267 61	0.217 55	0.476 52	0.714 56	0.452 49	0.629 59	0.514 56	1.000 1	0.222 63
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 62	1.000 1	0.432 69	0.245 62	0.190 64	0.577 60	0.013 59	0.263 64	0.033 67	0.320 60	0.240 63	0.075 63	0.422 58	0.857 45	0.117 67	0.699 49	0.271 68	0.883 60	0.235 62
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3D	0.323 63	0.667 56	0.542 65	0.264 61	0.157 67	0.550 62	0.000 65	0.205 67	0.009 69	0.270 63	0.218 64	0.075 63	0.500 50	0.688 59	0.007 73	0.698 50	0.301 65	0.459 70	0.200 64
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone	0.319 64	0.667 56	0.715 55	0.233 63	0.189 65	0.479 68	0.008 60	0.218 65	0.067 66	0.201 66	0.173 65	0.107 61	0.123 68	0.438 64	0.150 64	0.615 61	0.355 61	0.916 56	0.093 72

R-PointNet	0.306 65	0.500 68	0.405 70	0.311 58	0.348 57	0.589 58	0.054 52	0.068 70	0.126 62	0.283 62	0.290 60	0.028 68	0.219 66	0.214 67	0.331 59	0.396 70	0.275 66	0.821 64	0.245 61

Region-18class	0.284 66	0.250 72	0.751 51	0.228 65	0.270 61	0.521 64	0.000 65	0.468 59	0.008 71	0.205 65	0.127 66	0.000 72	0.068 70	0.070 71	0.262 63	0.652 57	0.323 63	0.740 66	0.173 65

SemRegionNet-20cls	0.250 67	0.333 69	0.613 62	0.229 64	0.163 66	0.493 65	0.000 65	0.304 63	0.107 63	0.147 69	0.100 68	0.052 66	0.231 64	0.119 69	0.039 69	0.445 68	0.325 62	0.654 67	0.141 68

3D-BEVIS	0.248 68	0.667 56	0.566 63	0.076 71	0.035 73	0.394 71	0.027 57	0.035 72	0.098 64	0.099 71	0.030 72	0.025 69	0.098 69	0.375 66	0.126 66	0.604 62	0.181 71	0.854 63	0.171 66
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
tmp	0.248 68	0.667 56	0.437 68	0.188 66	0.153 68	0.491 66	0.000 65	0.208 66	0.094 65	0.153 68	0.099 69	0.057 65	0.217 67	0.119 69	0.039 69	0.466 67	0.302 64	0.640 68	0.140 69

Sem_Recon_ins	0.227 70	0.764 55	0.486 67	0.069 72	0.098 70	0.426 70	0.017 58	0.067 71	0.015 68	0.172 67	0.100 67	0.096 62	0.054 72	0.183 68	0.135 65	0.366 71	0.260 70	0.614 69	0.168 67

ASIS	0.199 71	0.333 69	0.253 72	0.167 69	0.140 69	0.438 69	0.000 65	0.177 68	0.008 70	0.121 70	0.069 70	0.004 71	0.231 65	0.429 65	0.036 71	0.445 69	0.273 67	0.333 72	0.119 71

Sgpn_scannet	0.143 72	0.208 73	0.390 71	0.169 68	0.065 71	0.275 72	0.029 56	0.069 69	0.000 72	0.087 72	0.043 71	0.014 70	0.027 73	0.000 72	0.112 68	0.351 72	0.168 72	0.438 71	0.138 70

MaskRCNN 2d->3d Proj	0.058 73	0.333 69	0.002 73	0.000 73	0.053 72	0.002 73	0.002 64	0.021 73	0.000 72	0.045 73	0.024 73	0.238 54	0.065 71	0.000 72	0.014 72	0.107 73	0.020 73	0.110 73	0.006 73

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EMSANet (Instance)	0.380 1	0.549 3	0.651 1	0.147 1	0.397 3	0.399 1	0.167 2	0.437 3	0.319 2	0.210 1	0.301 1	0.235 1	0.463 2	0.245 2	0.372 3	0.511 1	0.296 2	0.876 1	0.268 1
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
FKNet	0.368 2	0.588 1	0.618 2	0.099 3	0.466 1	0.395 2	0.108 3	0.548 1	0.157 3	0.175 2	0.268 2	0.096 4	0.439 3	0.343 1	0.420 2	0.500 3	0.317 1	0.855 3	0.234 2

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

MaskRCNN_ScanNet	0.227 4	0.228 4	0.381 4	0.013 4	0.237 4	0.339 4	0.089 4	0.339 4	0.150 4	0.134 4	0.143 4	0.179 2	0.255 4	0.053 4	0.331 4	0.244 4	0.154 4	0.687 4	0.127 4
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17

Presenting the ScanNet200 Benchmark

ScanNet200 Benchmark

ScanNet200 3D Semantic Label Benchmark

ScanNet200 3D Semantic Instance Benchmark

ScanNet Benchmark

3D Semantic Label Benchmark

3D Semantic Instance Benchmark

2D Semantic Label Benchmark

2D Semantic Instance Benchmark

Scene Type Classification Benchmark

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

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

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