Benchmark Results - ScanNet Benchmark

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

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

BFANet ScanNet200	0.360 4	0.553 6	0.293 4	0.193 4	0.483 10	0.096 5	0.266 5	0.000 3	0.000 6	0.000 1	0.298 12	0.255 11	0.661 1	0.810 5	0.810 3	0.194 10	0.785 6	0.000 3	0.000 16	0.161 5	0.000 9	0.494 9	0.382 2	0.574 3	0.258 4	0.000 9	0.372 8	0.000 1	0.000 3	0.043 13	0.436 7	0.000 10	0.000 1	0.239 2	0.000 2	0.901 3	0.105 1	0.689 4	0.025 3	0.128 3	0.614 2	0.436 1	0.493 16	0.000 1	0.000 2	0.526 4	0.546 12	0.109 5	0.651 14	0.953 4	0.753 6	0.101 5	0.143 12	0.897 4	0.000 1	0.431 1	0.469 14	0.000 7	0.522 5	0.337 5	0.661 6	0.459 3	0.409 6	0.666 5	0.102 13	0.508 5	0.757 4	0.000 7	0.060 13	0.970 3	0.497 1	0.000 1	0.376 3	0.511 3	0.262 4	0.688 2	0.921 2	0.617 9	0.321 11	0.590 6	0.491 8	0.556 4	0.000 4	0.000 1	0.481 4	0.093 1	0.043 2	0.284 2	0.000 4	0.875 14	0.135 9	0.669 4	0.124 12	0.394 6	0.849 11	0.298 3	0.000 1	0.476 16	0.088 12	0.042 7	0.000 4	0.000 1	0.254 3	0.653 10	0.741 6	0.215 1	0.573 5	0.852 5	0.266 9	0.654 2	0.056 11	0.835 5	0.000 5	0.492 1	0.000 1	0.000 7	0.000 3	0.612 8	0.000 5	0.000 8	0.000 1	0.616 5	0.469 16	0.460 5	0.698 13	0.516 2	0.000 1	0.378 7	0.563 4	0.476 4	0.863 5	0.574 9	0.330 6	0.000 11	0.282 3	0.000 1	0.760 4	0.710 4	0.233 1	0.000 9	0.641 4	0.814 2	0.000 1	0.585 10	0.053 10	0.000 7	0.000 1	0.629 9	0.000 2	0.000 1	0.678 3	0.528 12	0.534 4	0.129 13	0.596 4	0.973 4	0.264 11	0.772 2	0.526 9	0.139 9	0.707 4	0.000 1	0.000 12	0.764 13	0.591 15	0.848 6	0.000 1	0.827 4	0.338 3	0.806 11	0.000 1	0.568 9	0.151 9	0.358 1	0.659 9	0.510 4
Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis. CVPR 2025
PPT-SpUNet-F.T.	0.332 11	0.556 5	0.270 6	0.123 13	0.519 4	0.091 6	0.349 3	0.000 3	0.000 6	0.000 1	0.339 8	0.383 9	0.498 10	0.833 4	0.807 4	0.241 4	0.584 8	0.000 3	0.755 6	0.124 7	0.000 9	0.608 3	0.330 7	0.530 8	0.314 2	0.000 9	0.374 7	0.000 1	0.000 3	0.197 4	0.459 6	0.000 10	0.000 1	0.117 5	0.000 2	0.876 7	0.095 2	0.682 9	0.000 6	0.086 7	0.518 7	0.433 2	0.930 4	0.000 1	0.000 2	0.563 3	0.542 13	0.077 7	0.715 4	0.858 10	0.756 5	0.008 15	0.171 11	0.874 7	0.000 1	0.039 6	0.550 10	0.000 7	0.545 4	0.256 8	0.657 8	0.453 4	0.351 9	0.449 10	0.213 6	0.392 11	0.611 10	0.000 7	0.037 14	0.946 6	0.138 13	0.000 1	0.000 12	0.063 10	0.308 2	0.537 7	0.796 4	0.673 4	0.323 10	0.392 10	0.400 13	0.509 7	0.000 4	0.000 1	0.649 1	0.000 11	0.023 11	0.000 11	0.000 4	0.914 6	0.002 15	0.506 15	0.163 10	0.359 8	0.872 5	0.000 11	0.000 1	0.623 6	0.112 6	0.001 12	0.000 4	0.000 1	0.021 8	0.753 5	0.565 15	0.150 4	0.579 4	0.806 9	0.267 8	0.616 4	0.042 13	0.783 12	0.000 5	0.374 10	0.000 1	0.000 7	0.000 3	0.620 7	0.000 5	0.000 8	0.000 1	0.572 12	0.634 5	0.350 9	0.792 5	0.000 9	0.000 1	0.376 8	0.535 6	0.378 6	0.855 6	0.672 3	0.074 12	0.000 11	0.185 9	0.000 1	0.727 11	0.660 11	0.076 16	0.000 9	0.432 11	0.646 10	0.000 1	0.594 8	0.006 12	0.000 7	0.000 1	0.658 6	0.000 2	0.000 1	0.661 4	0.549 9	0.300 13	0.291 8	0.045 13	0.942 11	0.304 7	0.600 8	0.572 6	0.135 12	0.695 5	0.000 1	0.008 9	0.793 8	0.942 2	0.899 2	0.000 1	0.816 6	0.181 7	0.897 2	0.000 1	0.679 4	0.223 7	0.264 4	0.691 5	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 12	0.539 9	0.265 9	0.131 11	0.499 6	0.110 3	0.522 2	0.000 3	0.000 6	0.000 1	0.318 10	0.427 6	0.455 14	0.743 11	0.765 12	0.175 11	0.842 3	0.000 3	0.828 4	0.204 3	0.033 6	0.429 11	0.335 5	0.601 2	0.312 3	0.000 9	0.357 9	0.000 1	0.000 3	0.047 10	0.423 8	0.000 10	0.000 1	0.105 8	0.000 2	0.873 9	0.079 8	0.670 12	0.000 6	0.117 4	0.471 13	0.432 3	0.829 10	0.000 1	0.000 2	0.584 2	0.417 16	0.089 6	0.684 9	0.837 11	0.705 15	0.021 11	0.178 10	0.892 5	0.000 1	0.028 7	0.505 12	0.000 7	0.457 8	0.200 13	0.662 4	0.412 9	0.244 14	0.496 8	0.000 16	0.451 7	0.626 8	0.000 7	0.102 10	0.943 9	0.138 13	0.000 1	0.000 12	0.149 7	0.291 3	0.534 8	0.722 6	0.632 6	0.331 9	0.253 14	0.453 10	0.487 11	0.000 4	0.000 1	0.479 5	0.000 11	0.022 12	0.000 11	0.000 4	0.900 10	0.128 10	0.684 3	0.164 9	0.413 4	0.854 10	0.000 11	0.000 1	0.512 15	0.074 14	0.003 11	0.000 4	0.000 1	0.000 10	0.469 14	0.613 12	0.132 8	0.529 7	0.871 3	0.227 15	0.582 7	0.026 16	0.787 11	0.000 5	0.339 14	0.000 1	0.000 7	0.000 3	0.626 6	0.000 5	0.029 7	0.000 1	0.587 8	0.612 7	0.411 7	0.724 9	0.000 9	0.000 1	0.407 5	0.552 5	0.513 3	0.849 9	0.655 4	0.408 4	0.000 11	0.296 2	0.000 1	0.686 14	0.645 13	0.145 8	0.022 7	0.414 13	0.633 11	0.000 1	0.637 2	0.224 2	0.000 7	0.000 1	0.650 7	0.000 2	0.000 1	0.622 8	0.535 11	0.343 11	0.483 3	0.230 12	0.943 10	0.289 9	0.618 7	0.596 4	0.140 8	0.679 7	0.000 1	0.022 6	0.783 10	0.620 11	0.906 1	0.000 1	0.806 8	0.137 10	0.865 4	0.000 1	0.378 12	0.000 14	0.168 14	0.680 7	0.227 13
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
DITR	0.449 1	0.629 1	0.392 1	0.289 1	0.650 1	0.168 1	0.862 1	0.000 3	0.313 2	0.000 1	0.580 1	0.568 1	0.564 3	0.766 7	0.867 1	0.238 5	0.949 1	0.000 3	0.866 2	0.300 1	0.000 9	0.664 1	0.482 1	0.508 11	0.317 1	0.420 1	0.551 1	0.000 1	0.000 3	0.486 1	0.519 1	0.662 3	0.000 1	0.385 1	0.000 2	0.901 3	0.079 8	0.727 1	0.000 6	0.160 2	0.606 3	0.417 4	0.967 2	0.000 1	0.000 2	0.498 5	0.596 10	0.130 2	0.728 3	0.998 1	0.805 1	0.000 16	0.314 1	0.934 2	0.000 1	0.278 4	0.636 1	0.000 7	0.403 11	0.367 1	0.741 2	0.484 1	0.500 2	1.000 1	0.113 11	0.828 1	0.815 1	0.000 7	0.733 1	0.969 4	0.374 2	0.000 1	0.579 1	1.000 1	0.230 5	0.617 4	0.983 1	0.729 1	0.423 3	0.855 1	0.508 5	0.622 2	0.018 3	0.000 1	0.591 3	0.034 4	0.028 9	0.066 10	0.869 1	0.904 7	0.334 2	0.651 5	0.716 1	0.514 2	0.871 6	0.315 2	0.000 1	0.664 3	0.128 3	0.014 10	0.000 4	0.000 1	0.392 2	0.851 2	0.817 1	0.153 3	0.823 1	0.991 1	0.318 3	0.680 1	0.134 2	0.913 1	0.157 1	0.448 4	0.000 1	0.000 7	0.000 3	0.826 1	0.978 1	0.091 5	0.000 1	0.660 4	0.647 3	0.571 2	0.804 4	0.001 8	0.000 1	0.480 2	0.700 1	0.421 5	0.947 1	0.433 14	0.411 3	0.148 6	0.262 4	0.000 1	0.849 1	0.709 5	0.138 10	0.150 1	0.714 2	0.889 1	0.000 1	0.698 1	0.222 3	0.000 7	0.000 1	0.720 2	0.000 2	0.000 1	0.805 1	0.600 1	0.642 2	0.268 9	0.904 1	0.982 2	0.477 1	0.632 6	0.718 2	0.139 9	0.776 2	0.000 1	0.178 1	0.886 1	0.962 1	0.839 8	0.000 1	0.851 2	0.043 12	0.869 3	0.000 1	0.710 1	0.315 5	0.348 2	0.753 2	0.397 8
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation.
L3DETR-ScanNet_200	0.336 7	0.533 10	0.279 5	0.155 9	0.508 5	0.073 10	0.101 16	0.000 3	0.058 5	0.000 1	0.294 13	0.233 13	0.548 4	0.927 1	0.788 10	0.264 2	0.463 10	0.000 3	0.638 11	0.098 12	0.014 7	0.411 12	0.226 12	0.525 9	0.225 8	0.010 7	0.397 5	0.000 1	0.000 3	0.192 5	0.380 13	0.598 5	0.000 1	0.117 5	0.000 2	0.883 6	0.082 7	0.689 4	0.000 6	0.032 16	0.549 6	0.417 4	0.910 5	0.000 1	0.000 2	0.448 8	0.613 9	0.000 10	0.697 7	0.960 2	0.759 4	0.158 2	0.293 3	0.883 6	0.000 1	0.312 3	0.583 3	0.079 4	0.422 10	0.068 16	0.660 7	0.418 7	0.298 11	0.430 11	0.114 10	0.526 4	0.776 3	0.051 3	0.679 2	0.946 6	0.152 7	0.000 1	0.183 8	0.000 14	0.211 7	0.511 9	0.409 15	0.565 11	0.355 7	0.448 8	0.512 4	0.557 3	0.000 4	0.000 1	0.420 8	0.000 11	0.007 16	0.104 6	0.000 4	0.125 16	0.330 3	0.514 14	0.146 11	0.321 12	0.860 8	0.174 10	0.000 1	0.629 5	0.075 13	0.000 13	0.000 4	0.000 1	0.002 9	0.671 8	0.712 7	0.141 6	0.339 11	0.856 4	0.261 11	0.529 9	0.067 9	0.835 5	0.000 5	0.369 11	0.000 1	0.259 2	0.000 3	0.629 5	0.000 5	0.487 1	0.000 1	0.579 10	0.646 4	0.107 16	0.720 10	0.122 6	0.000 1	0.333 13	0.505 9	0.303 9	0.908 3	0.503 13	0.565 2	0.074 8	0.324 1	0.000 1	0.740 7	0.661 10	0.109 13	0.000 9	0.427 12	0.563 16	0.000 1	0.579 11	0.108 7	0.000 7	0.000 1	0.664 5	0.000 2	0.000 1	0.641 7	0.539 10	0.416 6	0.515 2	0.256 10	0.940 12	0.312 5	0.209 16	0.620 3	0.138 11	0.636 10	0.000 1	0.000 12	0.775 12	0.861 5	0.765 12	0.000 1	0.801 9	0.119 11	0.860 7	0.000 1	0.687 2	0.001 13	0.192 13	0.679 8	0.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
CeCo	0.340 6	0.551 8	0.247 12	0.181 5	0.475 12	0.057 14	0.142 11	0.000 3	0.000 6	0.000 1	0.387 5	0.463 5	0.499 9	0.924 2	0.774 11	0.213 6	0.257 12	0.000 3	0.546 14	0.100 10	0.006 8	0.615 2	0.177 16	0.534 6	0.246 5	0.000 9	0.400 4	0.000 1	0.338 1	0.006 15	0.484 4	0.609 4	0.000 1	0.083 10	0.000 2	0.873 9	0.089 5	0.661 13	0.000 6	0.048 14	0.560 4	0.408 6	0.892 7	0.000 1	0.000 2	0.586 1	0.616 8	0.000 10	0.692 8	0.900 7	0.721 11	0.162 1	0.228 5	0.860 10	0.000 1	0.000 10	0.575 4	0.083 3	0.550 3	0.347 4	0.624 12	0.410 10	0.360 8	0.740 3	0.109 12	0.321 14	0.660 7	0.000 7	0.121 8	0.939 12	0.143 8	0.000 1	0.400 2	0.003 12	0.190 10	0.564 5	0.652 9	0.615 10	0.421 4	0.304 13	0.579 1	0.547 5	0.000 4	0.000 1	0.296 13	0.000 11	0.030 8	0.096 7	0.000 4	0.916 4	0.037 12	0.551 11	0.171 8	0.376 7	0.865 7	0.286 4	0.000 1	0.633 4	0.102 11	0.027 8	0.011 3	0.000 1	0.000 10	0.474 13	0.742 5	0.133 7	0.311 12	0.824 8	0.242 12	0.503 13	0.068 8	0.828 8	0.000 5	0.429 6	0.000 1	0.063 4	0.000 3	0.781 2	0.000 5	0.000 8	0.000 1	0.665 2	0.633 6	0.450 6	0.818 2	0.000 9	0.000 1	0.429 4	0.532 7	0.226 12	0.825 10	0.510 11	0.377 5	0.709 2	0.079 13	0.000 1	0.753 5	0.683 7	0.102 15	0.063 4	0.401 15	0.620 13	0.000 1	0.619 3	0.000 13	0.000 7	0.000 1	0.595 12	0.000 2	0.000 1	0.345 13	0.564 5	0.411 7	0.603 1	0.384 8	0.945 9	0.266 10	0.643 5	0.367 13	0.304 1	0.663 9	0.000 1	0.010 7	0.726 14	0.767 7	0.898 3	0.000 1	0.784 12	0.435 1	0.861 6	0.000 1	0.447 11	0.000 14	0.257 6	0.656 10	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
PTv3 ScanNet200	0.393 3	0.592 3	0.330 2	0.216 3	0.520 3	0.109 4	0.108 15	0.000 3	0.337 1	0.000 1	0.310 11	0.394 8	0.494 11	0.753 9	0.848 2	0.256 3	0.717 7	0.000 3	0.842 3	0.192 4	0.065 3	0.449 10	0.346 3	0.546 5	0.190 12	0.000 9	0.384 6	0.000 1	0.000 3	0.218 3	0.505 2	0.791 2	0.000 1	0.136 3	0.000 2	0.903 2	0.073 11	0.687 6	0.000 6	0.168 1	0.551 5	0.387 7	0.941 3	0.000 1	0.000 2	0.397 12	0.654 3	0.000 10	0.714 5	0.759 14	0.752 7	0.118 4	0.264 4	0.926 3	0.000 1	0.048 5	0.575 4	0.000 7	0.597 1	0.366 2	0.755 1	0.469 2	0.474 3	0.798 2	0.140 9	0.617 2	0.692 6	0.000 7	0.592 3	0.971 2	0.188 4	0.000 1	0.133 9	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 11	0.017 14	0.259 3	0.000 4	0.921 3	0.337 1	0.733 2	0.210 3	0.514 2	0.860 8	0.407 1	0.000 1	0.688 2	0.109 8	0.000 13	0.000 4	0.000 1	0.151 4	0.671 8	0.782 2	0.115 12	0.641 2	0.903 2	0.349 1	0.616 4	0.088 6	0.832 7	0.000 5	0.480 2	0.000 1	0.428 1	0.000 3	0.497 9	0.000 5	0.000 8	0.000 1	0.662 3	0.690 2	0.612 1	0.828 1	0.575 1	0.000 1	0.404 6	0.644 2	0.325 7	0.887 4	0.728 1	0.009 15	0.134 7	0.026 16	0.000 1	0.761 3	0.731 3	0.172 6	0.077 3	0.528 7	0.727 7	0.000 1	0.603 5	0.220 4	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 4	0.531 5	0.978 3	0.457 2	0.708 3	0.583 5	0.141 7	0.748 3	0.000 1	0.026 5	0.822 3	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 14	0.281 3	0.738 3	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)
ALS-MinkowskiNet	0.414 2	0.610 2	0.322 3	0.271 2	0.542 2	0.153 2	0.159 10	0.000 3	0.000 6	0.000 1	0.404 4	0.503 4	0.532 6	0.672 16	0.804 5	0.285 1	0.888 2	0.000 3	0.900 1	0.226 2	0.087 2	0.598 4	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 12	0.710 2	0.076 1	0.047 15	0.665 1	0.376 8	0.981 1	0.000 1	0.000 2	0.466 7	0.632 7	0.113 4	0.769 1	0.956 3	0.795 2	0.031 8	0.314 1	0.936 1	0.000 1	0.390 2	0.601 2	0.000 7	0.458 7	0.366 2	0.719 3	0.440 5	0.564 1	0.699 4	0.314 1	0.464 6	0.784 2	0.200 1	0.283 5	0.973 1	0.142 9	0.000 1	0.250 7	0.285 5	0.220 6	0.718 1	0.752 5	0.723 2	0.460 1	0.248 15	0.475 9	0.463 13	0.000 4	0.000 1	0.446 7	0.021 5	0.025 10	0.285 1	0.000 4	0.972 1	0.149 8	0.769 1	0.230 2	0.535 1	0.879 2	0.252 7	0.000 1	0.693 1	0.129 2	0.000 13	0.000 4	0.000 1	0.447 1	0.958 1	0.662 9	0.159 2	0.598 3	0.780 11	0.344 2	0.646 3	0.106 5	0.893 2	0.135 2	0.455 3	0.000 1	0.194 3	0.259 1	0.726 3	0.475 4	0.000 8	0.000 1	0.741 1	0.865 1	0.571 2	0.817 3	0.445 3	0.000 1	0.506 1	0.630 3	0.230 11	0.916 2	0.728 1	0.635 1	1.000 1	0.252 5	0.000 1	0.804 2	0.697 6	0.137 11	0.043 6	0.717 1	0.807 3	0.000 1	0.510 13	0.245 1	0.000 7	0.000 1	0.709 3	0.000 2	0.000 1	0.703 2	0.572 3	0.646 1	0.223 10	0.531 5	0.984 1	0.397 3	0.813 1	0.798 1	0.135 12	0.800 1	0.000 1	0.097 2	0.832 2	0.752 8	0.842 7	0.000 1	0.852 1	0.149 9	0.846 9	0.000 1	0.666 5	0.359 4	0.252 7	0.777 1	0.690 2
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. arxiv
IMFSegNet	0.334 8	0.532 12	0.251 10	0.179 6	0.486 9	0.041 15	0.139 12	0.003 1	0.283 3	0.000 1	0.274 14	0.191 14	0.457 13	0.704 14	0.795 7	0.197 9	0.830 5	0.000 3	0.710 8	0.055 15	0.064 4	0.518 6	0.305 9	0.458 16	0.216 11	0.027 5	0.284 12	0.000 1	0.000 3	0.044 11	0.406 9	0.561 6	0.000 1	0.080 11	0.000 2	0.873 9	0.021 14	0.683 8	0.000 6	0.076 8	0.494 10	0.363 9	0.648 15	0.000 1	0.000 2	0.425 9	0.649 4	0.000 10	0.668 12	0.908 6	0.740 10	0.010 13	0.206 7	0.862 9	0.000 1	0.000 10	0.560 8	0.000 7	0.359 12	0.237 11	0.631 11	0.408 11	0.411 4	0.322 14	0.246 4	0.439 9	0.599 12	0.047 4	0.213 6	0.940 10	0.139 11	0.000 1	0.369 5	0.124 9	0.188 11	0.495 10	0.624 10	0.626 7	0.320 13	0.595 4	0.495 7	0.496 10	0.000 4	0.000 1	0.340 11	0.014 6	0.032 6	0.135 5	0.000 4	0.903 8	0.277 6	0.612 8	0.196 6	0.344 11	0.848 13	0.260 5	0.000 1	0.574 12	0.073 15	0.062 4	0.000 4	0.000 1	0.091 5	0.839 3	0.776 3	0.123 11	0.392 8	0.756 12	0.274 5	0.518 11	0.029 15	0.842 3	0.000 5	0.357 12	0.000 1	0.035 6	0.000 3	0.444 11	0.793 2	0.245 4	0.000 1	0.512 15	0.512 14	0.159 14	0.713 12	0.000 9	0.000 1	0.336 12	0.484 11	0.569 2	0.852 8	0.615 6	0.120 11	0.068 10	0.228 7	0.000 1	0.733 9	0.773 1	0.190 4	0.000 9	0.608 5	0.792 4	0.000 1	0.597 7	0.000 13	0.025 2	0.000 1	0.573 16	0.000 2	0.000 1	0.508 10	0.555 7	0.363 9	0.139 11	0.610 2	0.947 8	0.305 6	0.594 9	0.527 8	0.009 16	0.633 12	0.000 1	0.060 3	0.820 5	0.604 14	0.799 9	0.000 1	0.799 10	0.034 14	0.784 12	0.000 1	0.618 6	0.424 1	0.134 15	0.646 12	0.214 14

GSTran	0.334 9	0.533 11	0.250 11	0.179 7	0.487 8	0.041 15	0.139 12	0.003 1	0.273 4	0.000 1	0.273 15	0.189 15	0.465 12	0.704 14	0.794 8	0.198 8	0.831 4	0.000 3	0.712 7	0.055 15	0.063 5	0.518 6	0.306 8	0.459 15	0.217 9	0.028 4	0.282 13	0.000 1	0.000 3	0.044 11	0.405 10	0.558 7	0.000 1	0.080 11	0.000 2	0.873 9	0.020 15	0.684 7	0.000 6	0.075 11	0.496 9	0.363 9	0.651 14	0.000 1	0.000 2	0.425 9	0.648 5	0.000 10	0.669 11	0.914 5	0.741 9	0.009 14	0.200 8	0.864 8	0.000 1	0.000 10	0.560 8	0.000 7	0.357 13	0.233 12	0.633 10	0.408 11	0.411 4	0.320 15	0.242 5	0.440 8	0.598 13	0.047 4	0.205 7	0.940 10	0.139 11	0.000 1	0.372 4	0.138 8	0.191 9	0.495 10	0.618 12	0.624 8	0.321 11	0.595 4	0.496 6	0.499 8	0.000 4	0.000 1	0.340 11	0.014 6	0.032 6	0.136 4	0.000 4	0.903 8	0.279 5	0.601 9	0.198 4	0.345 10	0.849 11	0.260 5	0.000 1	0.573 13	0.072 16	0.060 5	0.000 4	0.000 1	0.089 6	0.838 4	0.775 4	0.125 10	0.381 10	0.752 13	0.274 5	0.517 12	0.032 14	0.841 4	0.000 5	0.354 13	0.000 1	0.047 5	0.000 3	0.439 12	0.787 3	0.252 3	0.000 1	0.512 15	0.507 15	0.158 15	0.717 11	0.000 9	0.000 1	0.337 11	0.483 12	0.570 1	0.853 7	0.614 7	0.121 10	0.070 9	0.229 6	0.000 1	0.732 10	0.773 1	0.193 3	0.000 9	0.606 6	0.791 5	0.000 1	0.593 9	0.000 13	0.010 5	0.000 1	0.574 15	0.000 2	0.000 1	0.507 11	0.554 8	0.361 10	0.136 12	0.608 3	0.948 7	0.304 7	0.593 10	0.533 7	0.011 15	0.634 11	0.000 1	0.060 3	0.821 4	0.613 12	0.797 10	0.000 1	0.799 10	0.036 13	0.782 13	0.000 1	0.609 7	0.423 2	0.133 16	0.647 11	0.213 15

OA-CNN-L_ScanNet200	0.333 10	0.558 4	0.269 8	0.124 12	0.448 14	0.080 8	0.272 4	0.000 3	0.000 6	0.000 1	0.342 7	0.515 3	0.524 7	0.713 13	0.789 9	0.158 12	0.384 11	0.000 3	0.806 5	0.125 6	0.000 9	0.496 8	0.332 6	0.498 13	0.227 7	0.024 6	0.474 2	0.000 1	0.003 2	0.071 8	0.487 3	0.000 10	0.000 1	0.110 7	0.000 2	0.876 7	0.013 16	0.703 3	0.000 6	0.076 8	0.473 12	0.355 11	0.906 6	0.000 1	0.000 2	0.476 6	0.706 1	0.000 10	0.672 10	0.835 12	0.748 8	0.015 12	0.223 6	0.860 10	0.000 1	0.000 10	0.572 6	0.000 7	0.509 6	0.313 7	0.662 4	0.398 13	0.396 7	0.411 12	0.276 2	0.527 3	0.711 5	0.000 7	0.076 12	0.946 6	0.166 6	0.000 1	0.022 10	0.160 6	0.183 12	0.493 12	0.699 8	0.637 5	0.403 5	0.330 12	0.406 12	0.526 6	0.024 2	0.000 1	0.392 10	0.000 11	0.016 15	0.000 11	0.196 3	0.915 5	0.112 11	0.557 10	0.197 5	0.352 9	0.877 3	0.000 11	0.000 1	0.592 11	0.103 10	0.000 13	0.067 1	0.000 1	0.089 6	0.735 7	0.625 11	0.130 9	0.568 6	0.836 7	0.271 7	0.534 8	0.043 12	0.799 10	0.001 4	0.445 5	0.000 1	0.000 7	0.024 2	0.661 4	0.000 5	0.262 2	0.000 1	0.591 7	0.517 12	0.373 8	0.788 7	0.021 7	0.000 1	0.455 3	0.517 8	0.320 8	0.823 11	0.200 16	0.001 16	0.150 5	0.100 11	0.000 1	0.736 8	0.668 9	0.103 14	0.052 5	0.662 3	0.720 8	0.000 1	0.602 6	0.112 6	0.002 6	0.000 1	0.637 8	0.000 2	0.000 1	0.621 9	0.569 4	0.398 8	0.412 5	0.234 11	0.949 6	0.363 4	0.492 14	0.495 10	0.251 4	0.665 8	0.000 1	0.001 11	0.805 7	0.833 6	0.794 11	0.000 1	0.821 5	0.314 5	0.843 10	0.000 1	0.560 10	0.245 6	0.262 5	0.713 4	0.370 11

PonderV2 ScanNet200	0.346 5	0.552 7	0.270 7	0.175 8	0.497 7	0.070 11	0.239 6	0.000 3	0.000 6	0.000 1	0.232 16	0.412 7	0.584 2	0.842 3	0.804 5	0.212 7	0.540 9	0.000 3	0.433 15	0.106 9	0.000 9	0.590 5	0.290 11	0.548 4	0.243 6	0.000 9	0.356 10	0.000 1	0.000 3	0.062 9	0.398 12	0.441 9	0.000 1	0.104 9	0.000 2	0.888 5	0.076 10	0.682 9	0.030 2	0.094 6	0.491 11	0.351 12	0.869 9	0.000 1	0.063 1	0.403 11	0.700 2	0.000 10	0.660 13	0.881 8	0.761 3	0.050 7	0.186 9	0.852 12	0.000 1	0.007 8	0.570 7	0.100 2	0.565 2	0.326 6	0.641 9	0.431 6	0.290 13	0.621 6	0.259 3	0.408 10	0.622 9	0.125 2	0.082 11	0.950 5	0.179 5	0.000 1	0.263 6	0.424 4	0.193 8	0.558 6	0.880 3	0.545 12	0.375 6	0.727 3	0.445 11	0.499 8	0.000 4	0.000 1	0.475 6	0.002 9	0.034 5	0.083 8	0.000 4	0.924 2	0.290 4	0.636 6	0.115 13	0.400 5	0.874 4	0.186 9	0.000 1	0.611 7	0.128 3	0.113 2	0.000 4	0.000 1	0.000 10	0.584 11	0.636 10	0.103 13	0.385 9	0.843 6	0.283 4	0.603 6	0.080 7	0.825 9	0.000 5	0.377 9	0.000 1	0.000 7	0.000 3	0.457 10	0.000 5	0.000 8	0.000 1	0.574 11	0.608 8	0.481 4	0.792 5	0.394 4	0.000 1	0.357 9	0.503 10	0.261 10	0.817 12	0.504 12	0.304 7	0.472 4	0.115 10	0.000 1	0.750 6	0.677 8	0.202 2	0.000 9	0.509 8	0.729 6	0.000 1	0.519 12	0.000 13	0.000 7	0.000 1	0.620 11	0.000 2	0.000 1	0.660 6	0.560 6	0.486 5	0.384 6	0.346 9	0.952 5	0.247 13	0.667 4	0.436 11	0.269 3	0.691 6	0.000 1	0.010 7	0.787 9	0.889 3	0.880 4	0.000 1	0.810 7	0.336 4	0.860 7	0.000 1	0.606 8	0.009 10	0.248 8	0.681 6	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.
LGround	0.272 14	0.485 14	0.184 14	0.106 14	0.476 11	0.077 9	0.218 7	0.000 3	0.000 6	0.000 1	0.547 2	0.295 10	0.540 5	0.746 10	0.745 14	0.058 15	0.112 15	0.005 1	0.658 10	0.077 14	0.000 9	0.322 14	0.178 15	0.512 10	0.190 12	0.199 2	0.277 14	0.000 1	0.000 3	0.173 6	0.399 11	0.000 10	0.000 1	0.039 15	0.000 2	0.858 14	0.085 6	0.676 11	0.002 4	0.103 5	0.498 8	0.323 13	0.703 11	0.000 1	0.000 2	0.296 14	0.549 11	0.216 1	0.702 6	0.768 13	0.718 13	0.028 9	0.092 15	0.786 15	0.000 1	0.000 10	0.453 15	0.022 5	0.251 16	0.252 9	0.572 14	0.348 14	0.321 10	0.514 7	0.063 14	0.279 15	0.552 14	0.000 7	0.019 15	0.932 14	0.132 15	0.000 1	0.000 12	0.000 14	0.156 16	0.457 14	0.623 11	0.518 13	0.265 15	0.358 11	0.381 14	0.395 14	0.000 4	0.000 1	0.127 16	0.012 8	0.051 1	0.000 11	0.000 4	0.886 13	0.014 13	0.437 16	0.179 7	0.244 14	0.826 14	0.000 11	0.000 1	0.599 9	0.136 1	0.085 3	0.000 4	0.000 1	0.000 10	0.565 12	0.612 13	0.143 5	0.207 14	0.566 14	0.232 14	0.446 14	0.127 3	0.708 14	0.000 5	0.384 8	0.000 1	0.000 7	0.000 3	0.402 13	0.000 5	0.059 6	0.000 1	0.525 14	0.566 10	0.229 11	0.659 14	0.000 9	0.000 1	0.265 14	0.446 13	0.147 15	0.720 16	0.597 8	0.066 13	0.000 11	0.187 8	0.000 1	0.726 12	0.467 16	0.134 12	0.000 9	0.413 14	0.629 12	0.000 1	0.363 15	0.055 9	0.022 3	0.000 1	0.626 10	0.000 2	0.000 1	0.323 14	0.479 16	0.154 15	0.117 14	0.028 15	0.901 14	0.243 14	0.415 15	0.295 16	0.143 6	0.610 15	0.000 1	0.000 12	0.777 11	0.397 16	0.324 15	0.000 1	0.778 14	0.179 8	0.702 15	0.000 1	0.274 16	0.404 3	0.233 9	0.622 14	0.398 7
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34D	0.253 15	0.463 15	0.154 16	0.102 15	0.381 16	0.084 7	0.134 14	0.000 3	0.000 6	0.000 1	0.386 6	0.141 16	0.279 16	0.737 12	0.703 15	0.014 16	0.164 14	0.000 3	0.663 9	0.092 13	0.000 9	0.224 15	0.291 10	0.531 7	0.056 16	0.000 9	0.242 15	0.000 1	0.000 3	0.013 14	0.331 15	0.000 10	0.000 1	0.035 16	0.001 1	0.858 14	0.059 13	0.650 15	0.000 6	0.056 13	0.353 15	0.299 14	0.670 12	0.000 1	0.000 2	0.284 15	0.484 14	0.071 8	0.594 15	0.720 15	0.710 14	0.027 10	0.068 16	0.813 13	0.000 1	0.005 9	0.492 13	0.164 1	0.274 15	0.111 15	0.571 15	0.307 16	0.293 12	0.307 16	0.150 8	0.163 16	0.531 15	0.002 6	0.545 4	0.932 14	0.093 16	0.000 1	0.000 12	0.002 13	0.159 14	0.368 16	0.581 14	0.440 16	0.228 16	0.406 9	0.282 16	0.294 15	0.000 4	0.000 1	0.189 15	0.060 2	0.036 4	0.000 11	0.000 4	0.897 11	0.000 16	0.525 13	0.025 16	0.205 16	0.771 16	0.000 11	0.000 1	0.593 10	0.108 9	0.044 6	0.000 4	0.000 1	0.000 10	0.282 16	0.589 14	0.094 15	0.169 15	0.466 16	0.227 15	0.419 16	0.125 4	0.757 13	0.002 3	0.334 15	0.000 1	0.000 7	0.000 3	0.357 14	0.000 5	0.000 8	0.000 1	0.582 9	0.513 13	0.337 10	0.612 16	0.000 9	0.000 1	0.250 15	0.352 16	0.136 16	0.724 15	0.655 4	0.280 8	0.000 11	0.046 15	0.000 1	0.606 16	0.559 14	0.159 7	0.102 2	0.445 9	0.655 9	0.000 1	0.310 16	0.117 5	0.000 7	0.000 1	0.581 14	0.026 1	0.000 1	0.265 16	0.483 15	0.084 16	0.097 16	0.044 14	0.865 16	0.142 16	0.588 11	0.351 14	0.272 2	0.596 16	0.000 1	0.003 10	0.622 15	0.720 9	0.096 16	0.000 1	0.771 15	0.016 15	0.772 14	0.000 1	0.302 14	0.194 8	0.214 11	0.621 15	0.197 16
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
AWCS	0.305 13	0.508 13	0.225 13	0.142 10	0.463 13	0.063 12	0.195 8	0.000 3	0.000 6	0.000 1	0.467 3	0.551 2	0.504 8	0.773 6	0.764 13	0.142 13	0.029 16	0.000 3	0.626 12	0.100 10	0.000 9	0.360 13	0.179 14	0.507 12	0.137 14	0.006 8	0.300 11	0.000 1	0.000 3	0.172 7	0.364 14	0.512 8	0.000 1	0.056 13	0.000 2	0.865 13	0.093 4	0.634 16	0.000 6	0.071 12	0.396 14	0.296 15	0.876 8	0.000 1	0.000 2	0.373 13	0.436 15	0.063 9	0.749 2	0.877 9	0.721 11	0.131 3	0.124 13	0.804 14	0.000 1	0.000 10	0.515 11	0.010 6	0.452 9	0.252 9	0.578 13	0.417 8	0.179 16	0.484 9	0.171 7	0.337 13	0.606 11	0.000 7	0.115 9	0.937 13	0.142 9	0.000 1	0.008 11	0.000 14	0.157 15	0.484 13	0.402 16	0.501 14	0.339 8	0.553 7	0.529 2	0.478 12	0.000 4	0.000 1	0.404 9	0.001 10	0.022 12	0.077 9	0.000 4	0.894 12	0.219 7	0.628 7	0.093 14	0.305 13	0.886 1	0.233 8	0.000 1	0.603 8	0.112 6	0.023 9	0.000 4	0.000 1	0.000 10	0.741 6	0.664 8	0.097 14	0.253 13	0.782 10	0.264 10	0.523 10	0.154 1	0.707 15	0.000 5	0.411 7	0.000 1	0.000 7	0.000 3	0.332 15	0.000 5	0.000 8	0.000 1	0.602 6	0.595 9	0.185 12	0.656 15	0.159 5	0.000 1	0.355 10	0.424 14	0.154 14	0.729 14	0.516 10	0.220 9	0.620 3	0.084 12	0.000 1	0.707 13	0.651 12	0.173 5	0.014 8	0.381 16	0.582 14	0.000 1	0.619 3	0.049 11	0.000 7	0.000 1	0.702 4	0.000 2	0.000 1	0.302 15	0.489 14	0.317 12	0.334 7	0.392 7	0.922 13	0.254 12	0.533 13	0.394 12	0.129 14	0.613 14	0.000 1	0.000 12	0.820 5	0.649 10	0.749 13	0.000 1	0.782 13	0.282 6	0.863 5	0.000 1	0.288 15	0.006 11	0.220 10	0.633 13	0.542 3
: Long-Tailed 3D Semantic Segmentation with Adaptive Weight Constraint and Sampling. ICRA 2024
CSC-Pretrain	0.249 16	0.455 16	0.171 15	0.079 16	0.418 15	0.059 13	0.186 9	0.000 3	0.000 6	0.000 1	0.335 9	0.250 12	0.316 15	0.766 7	0.697 16	0.142 13	0.170 13	0.003 2	0.553 13	0.112 8	0.097 1	0.201 16	0.186 13	0.476 14	0.081 15	0.000 9	0.216 16	0.000 1	0.000 3	0.001 16	0.314 16	0.000 10	0.000 1	0.055 14	0.000 2	0.832 16	0.094 3	0.659 14	0.002 4	0.076 8	0.310 16	0.293 16	0.664 13	0.000 1	0.000 2	0.175 16	0.634 6	0.130 2	0.552 16	0.686 16	0.700 16	0.076 6	0.110 14	0.770 16	0.000 1	0.000 10	0.430 16	0.000 7	0.319 14	0.166 14	0.542 16	0.327 15	0.205 15	0.332 13	0.052 15	0.375 12	0.444 16	0.000 7	0.012 16	0.930 16	0.203 3	0.000 1	0.000 12	0.046 11	0.175 13	0.413 15	0.592 13	0.471 15	0.299 14	0.152 16	0.340 15	0.247 16	0.000 4	0.000 1	0.225 14	0.058 3	0.037 3	0.000 11	0.207 2	0.862 15	0.014 13	0.548 12	0.033 15	0.233 15	0.816 15	0.000 11	0.000 1	0.542 14	0.123 5	0.121 1	0.019 2	0.000 1	0.000 10	0.463 15	0.454 16	0.045 16	0.128 16	0.557 15	0.235 13	0.441 15	0.063 10	0.484 16	0.000 5	0.308 16	0.000 1	0.000 7	0.000 3	0.318 16	0.000 5	0.000 8	0.000 1	0.545 13	0.543 11	0.164 13	0.734 8	0.000 9	0.000 1	0.215 16	0.371 15	0.198 13	0.743 13	0.205 15	0.062 14	0.000 11	0.079 13	0.000 1	0.683 15	0.547 15	0.142 9	0.000 9	0.441 10	0.579 15	0.000 1	0.464 14	0.098 8	0.041 1	0.000 1	0.590 13	0.000 2	0.000 1	0.373 12	0.494 13	0.174 14	0.105 15	0.001 16	0.895 15	0.222 15	0.537 12	0.307 15	0.180 5	0.625 13	0.000 1	0.000 12	0.591 16	0.609 13	0.398 14	0.000 1	0.766 16	0.014 16	0.638 16	0.000 1	0.377 13	0.004 12	0.206 12	0.609 16	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 25%	head ap 25%	common ap 25%	tail ap 25%	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

TD3D Scannet200	0.379 2	0.603 2	0.306 2	0.190 2	0.635 2	0.073 2	0.500 1	0.000 1	0.000 1	0.000 1	0.495 3	0.735 2	0.275 5	1.000 1	0.979 2	0.590 2	0.000 4	0.021 2	0.000 3	0.146 3	0.000 2	0.356 2	0.173 5	0.795 1	0.226 2	0.000 1	0.173 2	0.000 1	0.000 2	0.226 2	0.390 2	0.000 2	0.000 1	0.250 1	0.000 1	0.706 2	0.061 3	0.885 1	0.093 2	0.186 2	0.259 4	0.200 1	0.667 1	0.000 2	0.000 1	0.667 2	0.825 1	0.250 4	0.834 4	1.000 1	0.958 1	0.553 1	0.111 3	0.748 1	0.220 2	0.051 2	0.866 2	0.792 1	0.390 5	0.045 5	0.800 2	0.302 5	0.517 1	0.533 3	0.113 2	0.427 1	0.843 2	0.000 2	0.458 1	0.600 1	0.000 1	0.101 2	0.000 1	0.259 1	0.717 2	0.500 2	0.615 2	0.520 2	0.526 2	0.457 1	0.270 4	0.000 1	0.000 1	0.400 2	0.088 2	0.294 2	0.181 2	0.000 1	1.000 1	0.400 1	0.710 5	0.103 3	0.477 5	0.905 2	0.061 2	0.000 1	0.906 2	0.102 2	0.232 1	0.125 2	0.000 2	0.003 2	0.792 3	1.000 1	0.000 2	0.102 3	0.125 4	0.559 5	0.523 3	0.075 2	0.715 1	0.000 2	0.424 5	0.000 1	0.396 2	0.250 1	0.638 1	0.000 1	0.000 2	0.000 1	0.622 5	0.833 2	0.221 1	0.970 1	0.250 2	0.038 1	0.260 2	0.415 1	0.125 2	1.000 1	1.000 1	0.857 2	0.000 2	0.908 1	0.012 1	0.869 3	0.836 1	0.635 1	0.111 1	0.625 1	1.000 1	0.020 2	0.510 1	0.003 3	0.009 2	1.000 1	0.778 1	0.000 1	0.000 1	0.370 3	0.755 1	0.288 2	0.333 3	0.274 2	1.000 1	0.557 1	0.731 2	0.456 2	0.433 3	0.769 5	0.000 1	0.000 2	0.621 4	1.000 1	0.458 4	0.000 1	0.196 2	0.817 1	0.000 1	0.472 1	0.222 3	0.205 5	0.689 2	0.274 3
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
LGround Inst.	0.314 3	0.529 3	0.225 3	0.155 3	0.578 5	0.010 3	0.500 1	0.000 1	0.000 1	0.000 1	0.515 2	0.556 3	0.696 1	1.000 1	0.927 3	0.400 3	0.083 3	0.000 3	1.000 1	0.252 1	0.000 2	0.167 3	0.350 2	0.731 2	0.067 3	0.000 1	0.123 4	0.000 1	0.000 2	0.036 3	0.372 3	0.000 2	0.000 1	0.250 1	0.000 1	0.569 4	0.031 5	0.810 3	0.000 3	0.000 4	0.630 1	0.183 2	0.278 3	0.000 2	0.000 1	0.582 4	0.589 5	0.500 2	0.863 3	1.000 1	0.940 2	0.000 4	0.144 1	0.716 3	0.000 3	0.000 3	0.484 3	0.000 3	0.500 3	0.400 3	0.798 3	0.500 2	0.278 4	0.750 1	0.093 3	0.166 4	0.783 3	0.000 2	0.200 2	0.400 2	0.000 1	0.000 3	0.000 1	0.219 2	0.539 3	0.500 2	0.578 3	0.413 3	0.181 5	0.457 2	0.375 2	0.000 1	0.000 1	0.050 5	0.000 4	0.077 4	0.000 3	0.000 1	0.500 5	0.000 5	0.743 3	0.250 2	0.488 4	0.846 3	0.000 3	0.000 1	0.800 3	0.069 3	0.000 3	0.000 3	0.000 2	0.000 3	1.000 1	0.607 4	0.000 2	0.200 1	0.500 1	0.694 2	0.528 2	0.063 3	0.659 2	0.000 2	0.594 2	0.000 1	0.000 3	0.000 2	0.571 2	0.000 1	0.000 2	0.000 1	0.716 4	0.647 5	0.221 2	0.857 4	0.000 3	0.000 3	0.217 3	0.346 3	0.071 5	0.530 5	1.000 1	0.429 3	0.000 2	0.286 3	0.000 3	0.826 5	0.706 3	0.208 4	0.000 3	0.250 4	0.744 5	0.000 3	0.500 2	0.042 1	0.000 3	0.000 2	0.746 3	0.000 1	0.000 1	0.517 1	0.625 3	0.085 5	0.333 3	0.000 4	1.000 1	0.378 4	0.533 5	0.376 4	0.042 5	0.814 3	0.000 1	0.000 2	0.765 3	1.000 1	0.600 3	0.000 1	0.000 4	0.667 3	0.000 1	0.472 1	0.333 2	0.337 3	0.605 3	0.305 2
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.	0.280 4	0.488 4	0.192 5	0.124 4	0.593 4	0.010 4	0.500 1	0.000 1	0.000 1	0.000 1	0.447 4	0.535 4	0.445 3	1.000 1	0.861 4	0.400 3	0.225 2	0.000 3	0.000 3	0.142 4	0.000 2	0.074 4	0.342 3	0.467 5	0.067 3	0.000 1	0.119 5	0.000 1	0.000 2	0.000 4	0.337 5	0.000 2	0.000 1	0.000 4	0.000 1	0.506 5	0.070 2	0.804 4	0.000 3	0.000 4	0.333 3	0.172 3	0.150 5	0.000 2	0.000 1	0.479 5	0.745 3	0.000 5	0.830 5	1.000 1	0.904 3	0.167 2	0.090 4	0.732 2	0.000 3	0.000 3	0.443 4	0.000 3	0.500 3	0.542 1	0.772 5	0.396 4	0.077 5	0.385 4	0.044 4	0.118 5	0.777 4	0.000 2	0.000 4	0.200 3	0.000 1	0.000 3	0.000 1	0.148 4	0.502 4	0.500 2	0.419 4	0.159 5	0.281 4	0.404 5	0.317 3	0.000 1	0.000 1	0.200 3	0.000 4	0.077 3	0.000 3	0.000 1	0.750 3	0.200 3	0.715 4	0.021 4	0.551 2	0.828 5	0.000 3	0.000 1	0.743 4	0.059 5	0.000 3	0.000 3	0.000 2	0.000 3	0.125 5	0.648 3	0.000 2	0.191 2	0.500 1	0.669 4	0.502 4	0.000 5	0.568 4	0.000 2	0.516 4	0.000 1	0.000 3	0.000 2	0.305 5	0.000 1	0.000 2	0.000 1	0.825 1	0.833 2	0.021 5	0.918 2	0.000 3	0.000 3	0.191 4	0.346 4	0.100 4	0.981 3	1.000 1	0.286 4	0.000 2	0.000 5	0.000 3	0.868 4	0.648 5	0.292 3	0.000 3	0.375 3	1.000 1	0.000 3	0.500 2	0.000 4	0.333 1	0.000 2	0.538 5	0.000 1	0.000 1	0.213 5	0.518 4	0.098 4	0.528 1	0.250 3	0.997 3	0.284 5	0.677 3	0.398 3	0.167 4	0.790 4	0.000 1	0.000 2	0.618 5	0.903 5	0.200 5	0.000 1	0.333 1	0.333 4	0.000 1	0.442 3	0.083 4	0.213 4	0.587 4	0.131 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.	0.275 5	0.466 5	0.218 4	0.110 5	0.625 3	0.007 5	0.500 1	0.000 1	0.000 1	0.000 1	0.000 5	0.222 5	0.377 4	1.000 1	0.661 5	0.400 3	0.000 4	0.000 3	0.000 3	0.119 5	0.000 2	0.000 5	0.277 4	0.685 4	0.067 3	0.000 1	0.132 3	0.000 1	0.000 2	0.000 4	0.367 4	0.000 2	0.000 1	0.000 4	0.000 1	0.591 3	0.055 4	0.783 5	0.000 3	0.014 3	0.500 2	0.161 4	0.278 3	0.000 2	0.000 1	0.667 2	0.768 2	0.500 2	0.866 2	1.000 1	0.829 5	0.000 4	0.019 5	0.555 5	0.000 3	0.000 3	0.305 5	0.000 3	0.750 1	0.200 4	0.783 4	0.429 3	0.395 3	0.677 2	0.020 5	0.286 3	0.584 5	0.000 2	0.000 4	0.115 5	0.000 1	0.000 3	0.000 1	0.145 5	0.423 5	0.500 2	0.364 5	0.369 4	0.571 1	0.448 3	0.206 5	0.000 1	0.000 1	0.200 3	0.106 1	0.065 5	0.000 3	0.000 1	0.750 3	0.200 3	0.774 2	0.000 5	0.501 3	0.841 4	0.000 3	0.000 1	0.692 5	0.063 4	0.000 3	0.000 3	0.000 2	0.000 3	0.500 4	0.649 2	0.000 2	0.084 4	0.125 4	0.719 1	0.413 5	0.004 4	0.450 5	0.000 2	0.638 1	0.000 1	0.000 3	0.000 2	0.505 3	0.000 1	0.000 2	0.000 1	0.727 3	0.833 2	0.221 2	0.779 5	0.000 3	0.000 3	0.168 5	0.311 5	0.125 2	0.571 4	0.500 5	0.143 5	0.000 2	0.250 4	0.000 3	0.869 2	0.667 4	0.162 5	0.000 3	0.250 4	1.000 1	0.000 3	0.500 2	0.000 4	0.000 3	0.000 2	0.689 4	0.000 1	0.000 1	0.312 4	0.383 5	0.114 3	0.333 3	0.000 4	0.997 3	0.420 3	0.613 4	0.212 5	0.500 2	0.819 2	0.000 1	0.000 2	0.768 2	1.000 1	0.918 1	0.000 1	0.000 4	0.278 5	0.000 1	0.333 5	0.000 5	0.353 2	0.546 5	0.258 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Mask3D Scannet200	0.445 1	0.653 1	0.392 1	0.254 1	0.648 1	0.097 1	0.125 5	0.000 1	0.000 1	0.000 1	0.657 1	0.971 1	0.451 2	1.000 1	1.000 1	0.640 1	0.500 1	0.045 1	1.000 1	0.241 2	0.409 1	0.363 1	0.440 1	0.686 3	0.300 1	0.000 1	0.201 1	0.000 1	0.009 1	0.290 1	0.556 1	1.000 1	0.000 1	0.063 3	0.000 1	0.830 1	0.573 1	0.844 2	0.333 1	0.204 1	0.058 5	0.158 5	0.552 2	0.056 1	0.000 1	1.000 1	0.725 4	0.750 1	0.927 1	1.000 1	0.888 4	0.042 3	0.120 2	0.615 4	0.226 1	0.250 1	0.890 1	0.792 1	0.677 2	0.510 2	0.818 1	0.699 1	0.512 2	0.167 5	0.125 1	0.315 2	0.943 1	0.309 1	0.017 3	0.200 3	0.000 1	0.188 1	0.000 1	0.183 3	0.815 1	1.000 1	0.827 1	0.741 1	0.442 3	0.414 4	0.600 1	0.000 1	0.000 1	0.458 1	0.049 3	0.321 1	0.381 1	0.000 1	0.908 2	0.400 1	0.841 1	0.260 1	0.710 1	0.966 1	0.265 1	0.000 1	0.924 1	0.152 1	0.025 2	0.500 1	0.027 1	0.028 1	1.000 1	0.556 5	0.016 1	0.080 5	0.500 1	0.694 3	0.608 1	0.084 1	0.604 3	0.194 1	0.538 3	0.000 1	0.500 1	0.000 2	0.354 4	0.000 1	1.000 1	0.000 1	0.761 2	0.930 1	0.053 4	0.890 3	1.000 1	0.008 2	0.262 1	0.358 2	1.000 1	1.000 1	0.792 4	0.966 1	1.000 1	0.765 2	0.004 2	0.930 1	0.780 2	0.330 2	0.027 2	0.625 1	0.974 4	0.050 1	0.412 5	0.021 2	0.000 3	0.000 2	0.778 1	0.000 1	0.000 1	0.493 2	0.746 2	0.454 1	0.335 2	0.396 1	0.930 5	0.551 2	1.000 1	0.552 1	0.606 1	0.853 1	0.000 1	0.004 1	0.806 1	1.000 1	0.727 2	0.000 1	0.042 3	0.745 2	0.000 1	0.399 4	0.391 1	0.630 1	0.721 1	0.619 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023

ScanNet Benchmark

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

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

PTv3-PPT-ALC	0.798 1	0.911 11	0.812 22	0.854 7	0.770 12	0.856 15	0.555 16	0.943 1	0.660 25	0.735 2	0.979 1	0.606 7	0.492 1	0.792 4	0.934 4	0.841 2	0.819 5	0.716 9	0.947 10	0.906 1	0.822 1
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. arxiv
DITR ScanNet	0.797 2	0.727 76	0.869 1	0.882 1	0.785 6	0.868 7	0.578 5	0.943 1	0.744 1	0.727 3	0.979 1	0.627 2	0.364 9	0.824 1	0.949 2	0.779 14	0.844 1	0.757 1	0.982 1	0.905 2	0.802 3
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation.
PTv3 ScanNet	0.794 3	0.941 3	0.813 21	0.851 10	0.782 7	0.890 2	0.597 1	0.916 5	0.696 10	0.713 5	0.979 1	0.635 1	0.384 3	0.793 3	0.907 10	0.821 5	0.790 35	0.696 14	0.967 4	0.903 3	0.805 2
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV2	0.785 4	0.978 1	0.800 30	0.833 28	0.788 4	0.853 20	0.545 20	0.910 8	0.713 3	0.705 6	0.979 1	0.596 9	0.390 2	0.769 15	0.832 45	0.821 5	0.792 34	0.730 2	0.975 2	0.897 6	0.785 7
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Mix3D	0.781 5	0.964 2	0.855 2	0.843 19	0.781 8	0.858 13	0.575 8	0.831 37	0.685 16	0.714 4	0.979 1	0.594 10	0.310 29	0.801 2	0.892 19	0.841 2	0.819 5	0.723 6	0.940 15	0.887 8	0.725 28
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3D	0.779 6	0.861 23	0.818 16	0.836 25	0.790 3	0.875 4	0.576 7	0.905 9	0.704 7	0.739 1	0.969 12	0.611 3	0.349 12	0.756 25	0.958 1	0.702 50	0.805 18	0.708 10	0.916 38	0.898 5	0.801 4

TTT-KD	0.773 7	0.646 96	0.818 16	0.809 40	0.774 10	0.878 3	0.581 3	0.943 1	0.687 14	0.704 7	0.978 6	0.607 6	0.336 18	0.775 11	0.912 8	0.838 4	0.823 3	0.694 15	0.967 4	0.899 4	0.794 6
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 34	0.564 12	0.900 11	0.686 15	0.677 14	0.961 18	0.537 35	0.348 13	0.769 15	0.903 12	0.785 12	0.815 8	0.676 26	0.939 16	0.880 13	0.772 11

OctFormer	0.766 9	0.925 7	0.808 26	0.849 12	0.786 5	0.846 30	0.566 11	0.876 18	0.690 12	0.674 16	0.960 19	0.576 21	0.226 71	0.753 27	0.904 11	0.777 15	0.815 8	0.722 7	0.923 31	0.877 16	0.776 10
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 36	0.829 30	0.751 26	0.854 18	0.540 24	0.903 10	0.630 38	0.672 17	0.963 16	0.565 25	0.357 10	0.788 5	0.900 14	0.737 30	0.802 19	0.685 20	0.950 8	0.887 8	0.780 8
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OccuSeg+Semantic	0.764 11	0.758 61	0.796 34	0.839 23	0.746 30	0.907 1	0.562 13	0.850 28	0.680 18	0.672 17	0.978 6	0.610 4	0.335 20	0.777 9	0.819 49	0.847 1	0.830 2	0.691 17	0.972 3	0.885 10	0.727 26

CU-Hybrid Net	0.764 11	0.924 8	0.819 14	0.840 22	0.757 21	0.853 20	0.580 4	0.848 29	0.709 5	0.643 27	0.958 23	0.587 15	0.295 37	0.753 27	0.884 23	0.758 22	0.815 8	0.725 5	0.927 27	0.867 27	0.743 19

O-CNN	0.762 13	0.924 8	0.823 8	0.844 18	0.770 12	0.852 22	0.577 6	0.847 31	0.711 4	0.640 31	0.958 23	0.592 11	0.217 77	0.762 20	0.888 20	0.758 22	0.813 12	0.726 4	0.932 25	0.868 26	0.744 18
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
DiffSegNet	0.758 14	0.725 78	0.789 41	0.843 19	0.762 17	0.856 15	0.562 13	0.920 4	0.657 28	0.658 21	0.958 23	0.589 13	0.337 17	0.782 6	0.879 24	0.787 10	0.779 40	0.678 22	0.926 29	0.880 13	0.799 5

DTC	0.757 15	0.843 29	0.820 12	0.847 15	0.791 2	0.862 11	0.511 37	0.870 21	0.707 6	0.652 23	0.954 40	0.604 8	0.279 47	0.760 21	0.942 3	0.734 31	0.766 49	0.701 13	0.884 60	0.874 22	0.736 20

OA-CNN-L_ScanNet20	0.756 16	0.783 47	0.826 6	0.858 5	0.776 9	0.837 38	0.548 19	0.896 14	0.649 30	0.675 15	0.962 17	0.586 16	0.335 20	0.771 14	0.802 54	0.770 18	0.787 37	0.691 17	0.936 20	0.880 13	0.761 13

PNE	0.755 17	0.786 45	0.835 5	0.834 27	0.758 19	0.849 25	0.570 10	0.836 36	0.648 31	0.668 19	0.978 6	0.581 19	0.367 7	0.683 38	0.856 33	0.804 7	0.801 23	0.678 22	0.961 6	0.889 7	0.716 34
P. Hermosilla: Point Neighborhood Embeddings.
LSK3DNet	0.755 17	0.899 16	0.823 8	0.843 19	0.764 16	0.838 37	0.584 2	0.845 32	0.717 2	0.638 33	0.956 30	0.580 20	0.229 70	0.640 47	0.900 14	0.750 25	0.813 12	0.729 3	0.920 35	0.872 24	0.757 14
Tuo Feng, Wenguan Wang, Fan Ma, Yi Yang: LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels. CVPR 2024
ConDaFormer	0.755 17	0.927 6	0.822 10	0.836 25	0.801 1	0.849 25	0.516 34	0.864 25	0.651 29	0.680 13	0.958 23	0.584 18	0.282 44	0.759 23	0.855 35	0.728 33	0.802 19	0.678 22	0.880 65	0.873 23	0.756 16
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 20	0.742 68	0.809 25	0.872 2	0.758 19	0.860 12	0.552 17	0.891 16	0.610 45	0.687 8	0.960 19	0.559 29	0.304 32	0.766 18	0.926 6	0.767 19	0.797 27	0.644 37	0.942 13	0.876 19	0.722 30
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net	0.752 20	0.906 14	0.793 38	0.802 46	0.689 44	0.825 51	0.556 15	0.867 22	0.681 17	0.602 49	0.960 19	0.555 31	0.365 8	0.779 8	0.859 30	0.747 26	0.795 31	0.717 8	0.917 37	0.856 35	0.764 12
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
BPNet	0.749 22	0.909 12	0.818 16	0.811 38	0.752 24	0.839 36	0.485 52	0.842 33	0.673 20	0.644 26	0.957 28	0.528 41	0.305 31	0.773 12	0.859 30	0.788 9	0.818 7	0.693 16	0.916 38	0.856 35	0.723 29
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer	0.749 22	0.793 43	0.790 39	0.807 42	0.750 28	0.856 15	0.524 30	0.881 17	0.588 57	0.642 30	0.977 10	0.591 12	0.274 50	0.781 7	0.929 5	0.804 7	0.796 28	0.642 38	0.947 10	0.885 10	0.715 35
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP	0.748 24	0.623 99	0.804 28	0.859 4	0.745 31	0.824 53	0.501 41	0.912 7	0.690 12	0.685 10	0.956 30	0.567 24	0.320 26	0.768 17	0.918 7	0.720 38	0.802 19	0.676 26	0.921 33	0.881 12	0.779 9

StratifiedFormer	0.747 25	0.901 15	0.803 29	0.845 17	0.757 21	0.846 30	0.512 36	0.825 40	0.696 10	0.645 25	0.956 30	0.576 21	0.262 61	0.744 33	0.861 29	0.742 28	0.770 47	0.705 11	0.899 50	0.860 32	0.734 21
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
VMNet	0.746 26	0.870 21	0.838 3	0.858 5	0.729 36	0.850 24	0.501 41	0.874 19	0.587 58	0.658 21	0.956 30	0.564 26	0.299 34	0.765 19	0.900 14	0.716 41	0.812 14	0.631 43	0.939 16	0.858 33	0.709 36
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion	0.746 26	0.771 55	0.819 14	0.848 14	0.702 42	0.865 10	0.397 89	0.899 12	0.699 8	0.664 20	0.948 61	0.588 14	0.330 22	0.746 32	0.851 39	0.764 20	0.796 28	0.704 12	0.935 21	0.866 28	0.728 24
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
DiffSeg3D2	0.745 28	0.725 78	0.814 20	0.837 24	0.751 26	0.831 45	0.514 35	0.896 14	0.674 19	0.684 11	0.960 19	0.564 26	0.303 33	0.773 12	0.820 48	0.713 44	0.798 26	0.690 19	0.923 31	0.875 20	0.757 14

Retro-FPN	0.744 29	0.842 30	0.800 30	0.767 60	0.740 32	0.836 40	0.541 22	0.914 6	0.672 21	0.626 37	0.958 23	0.552 32	0.272 52	0.777 9	0.886 22	0.696 51	0.801 23	0.674 29	0.941 14	0.858 33	0.717 32
Peng Xiang, Xin Wen, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
EQ-Net	0.743 30	0.620 100	0.799 33	0.849 12	0.730 35	0.822 55	0.493 49	0.897 13	0.664 22	0.681 12	0.955 34	0.562 28	0.378 4	0.760 21	0.903 12	0.738 29	0.801 23	0.673 30	0.907 42	0.877 16	0.745 17
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT	0.742 31	0.860 24	0.765 55	0.819 33	0.769 14	0.848 27	0.533 26	0.829 38	0.663 23	0.631 36	0.955 34	0.586 16	0.274 50	0.753 27	0.896 17	0.729 32	0.760 55	0.666 32	0.921 33	0.855 37	0.733 22

LRPNet	0.742 31	0.816 38	0.806 27	0.807 42	0.752 24	0.828 49	0.575 8	0.839 35	0.699 8	0.637 34	0.954 40	0.520 44	0.320 26	0.755 26	0.834 43	0.760 21	0.772 44	0.676 26	0.915 40	0.862 30	0.717 32

LargeKernel3D	0.739 33	0.909 12	0.820 12	0.806 44	0.740 32	0.852 22	0.545 20	0.826 39	0.594 56	0.643 27	0.955 34	0.541 34	0.263 60	0.723 36	0.858 32	0.775 17	0.767 48	0.678 22	0.933 23	0.848 42	0.694 41
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN	0.736 34	0.776 51	0.790 39	0.851 10	0.754 23	0.854 18	0.491 51	0.866 23	0.596 55	0.686 9	0.955 34	0.536 36	0.342 15	0.624 54	0.869 26	0.787 10	0.802 19	0.628 44	0.927 27	0.875 20	0.704 38

MinkowskiNet	0.736 34	0.859 25	0.818 16	0.832 29	0.709 40	0.840 34	0.521 32	0.853 27	0.660 25	0.643 27	0.951 51	0.544 33	0.286 42	0.731 34	0.893 18	0.675 59	0.772 44	0.683 21	0.874 71	0.852 40	0.727 26
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA	0.731 36	0.890 17	0.837 4	0.864 3	0.726 37	0.873 5	0.530 29	0.824 41	0.489 91	0.647 24	0.978 6	0.609 5	0.336 18	0.624 54	0.733 63	0.758 22	0.776 42	0.570 69	0.949 9	0.877 16	0.728 24

online3d	0.727 37	0.715 83	0.777 48	0.854 7	0.748 29	0.858 13	0.497 46	0.872 20	0.572 64	0.639 32	0.957 28	0.523 42	0.297 36	0.750 30	0.803 53	0.744 27	0.810 15	0.587 65	0.938 18	0.871 25	0.719 31

SparseConvNet	0.725 38	0.647 95	0.821 11	0.846 16	0.721 38	0.869 6	0.533 26	0.754 62	0.603 51	0.614 41	0.955 34	0.572 23	0.325 24	0.710 37	0.870 25	0.724 36	0.823 3	0.628 44	0.934 22	0.865 29	0.683 44

PointTransformer++	0.725 38	0.727 76	0.811 24	0.819 33	0.765 15	0.841 33	0.502 40	0.814 46	0.621 41	0.623 39	0.955 34	0.556 30	0.284 43	0.620 56	0.866 27	0.781 13	0.757 59	0.648 35	0.932 25	0.862 30	0.709 36

MatchingNet	0.724 40	0.812 40	0.812 22	0.810 39	0.735 34	0.834 42	0.495 48	0.860 26	0.572 64	0.602 49	0.954 40	0.512 46	0.280 46	0.757 24	0.845 41	0.725 35	0.780 39	0.606 54	0.937 19	0.851 41	0.700 40

INS-Conv-semantic	0.717 41	0.751 64	0.759 58	0.812 37	0.704 41	0.868 7	0.537 25	0.842 33	0.609 47	0.608 45	0.953 44	0.534 38	0.293 38	0.616 57	0.864 28	0.719 40	0.793 32	0.640 39	0.933 23	0.845 46	0.663 49

PointMetaBase	0.714 42	0.835 31	0.785 43	0.821 31	0.684 46	0.846 30	0.531 28	0.865 24	0.614 42	0.596 53	0.953 44	0.500 49	0.246 66	0.674 39	0.888 20	0.692 52	0.764 51	0.624 46	0.849 86	0.844 47	0.675 46

contrastBoundary	0.705 43	0.769 58	0.775 49	0.809 40	0.687 45	0.820 58	0.439 77	0.812 47	0.661 24	0.591 55	0.945 69	0.515 45	0.171 96	0.633 51	0.856 33	0.720 38	0.796 28	0.668 31	0.889 57	0.847 43	0.689 42
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic	0.703 44	0.774 53	0.800 30	0.793 51	0.760 18	0.847 29	0.471 56	0.802 50	0.463 98	0.634 35	0.968 14	0.491 52	0.271 54	0.726 35	0.910 9	0.706 46	0.815 8	0.551 81	0.878 66	0.833 48	0.570 81

RFCR	0.702 45	0.889 18	0.745 68	0.813 36	0.672 49	0.818 62	0.493 49	0.815 45	0.623 39	0.610 43	0.947 63	0.470 61	0.249 65	0.594 61	0.848 40	0.705 47	0.779 40	0.646 36	0.892 55	0.823 54	0.611 64
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click	0.701 46	0.825 35	0.796 34	0.723 67	0.716 39	0.832 44	0.433 79	0.816 43	0.634 36	0.609 44	0.969 12	0.418 87	0.344 14	0.559 73	0.833 44	0.715 42	0.808 17	0.560 75	0.902 47	0.847 43	0.680 45

JSENet	0.699 47	0.881 20	0.762 56	0.821 31	0.667 50	0.800 75	0.522 31	0.792 53	0.613 43	0.607 46	0.935 89	0.492 51	0.205 83	0.576 66	0.853 37	0.691 53	0.758 57	0.652 34	0.872 74	0.828 51	0.649 53
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click	0.693 48	0.743 67	0.794 36	0.655 90	0.684 46	0.822 55	0.497 46	0.719 72	0.622 40	0.617 40	0.977 10	0.447 74	0.339 16	0.750 30	0.664 80	0.703 49	0.790 35	0.596 58	0.946 12	0.855 37	0.647 54
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-II	0.692 49	0.732 72	0.772 50	0.786 52	0.677 48	0.866 9	0.517 33	0.848 29	0.509 84	0.626 37	0.952 49	0.536 36	0.225 73	0.545 79	0.704 70	0.689 56	0.810 15	0.564 74	0.903 46	0.854 39	0.729 23
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNet	0.690 50	0.884 19	0.754 62	0.795 49	0.647 57	0.818 62	0.422 81	0.802 50	0.612 44	0.604 47	0.945 69	0.462 64	0.189 91	0.563 72	0.853 37	0.726 34	0.765 50	0.632 42	0.904 44	0.821 57	0.606 68
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet	0.688 51	0.704 85	0.741 72	0.754 64	0.656 52	0.829 47	0.501 41	0.741 67	0.609 47	0.548 62	0.950 55	0.522 43	0.371 5	0.633 51	0.756 58	0.715 42	0.771 46	0.623 47	0.861 82	0.814 60	0.658 50
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Net	0.685 52	0.866 22	0.748 65	0.819 33	0.645 59	0.794 78	0.450 67	0.802 50	0.587 58	0.604 47	0.945 69	0.464 63	0.201 86	0.554 75	0.840 42	0.723 37	0.732 70	0.602 56	0.907 42	0.822 56	0.603 71

VACNN++	0.684 53	0.728 75	0.757 61	0.776 57	0.690 43	0.804 73	0.464 61	0.816 43	0.577 63	0.587 56	0.945 69	0.508 48	0.276 49	0.671 40	0.710 68	0.663 64	0.750 63	0.589 63	0.881 63	0.832 50	0.653 52

KP-FCNN	0.684 53	0.847 28	0.758 60	0.784 54	0.647 57	0.814 65	0.473 55	0.772 56	0.605 49	0.594 54	0.935 89	0.450 72	0.181 94	0.587 62	0.805 52	0.690 54	0.785 38	0.614 50	0.882 62	0.819 58	0.632 60
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet	0.684 53	0.712 84	0.784 44	0.782 56	0.658 51	0.835 41	0.499 45	0.823 42	0.641 33	0.597 52	0.950 55	0.487 54	0.281 45	0.575 67	0.619 84	0.647 72	0.764 51	0.620 49	0.871 77	0.846 45	0.688 43

PointContrast_LA_SEM	0.683 56	0.757 62	0.784 44	0.786 52	0.639 61	0.824 53	0.408 84	0.775 55	0.604 50	0.541 64	0.934 93	0.532 39	0.269 56	0.552 76	0.777 56	0.645 75	0.793 32	0.640 39	0.913 41	0.824 53	0.671 47

Superpoint Network	0.683 56	0.851 27	0.728 76	0.800 48	0.653 54	0.806 71	0.468 58	0.804 48	0.572 64	0.602 49	0.946 66	0.453 71	0.239 69	0.519 84	0.822 46	0.689 56	0.762 54	0.595 60	0.895 53	0.827 52	0.630 61

VI-PointConv	0.676 58	0.770 57	0.754 62	0.783 55	0.621 65	0.814 65	0.552 17	0.758 60	0.571 67	0.557 60	0.954 40	0.529 40	0.268 58	0.530 82	0.682 74	0.675 59	0.719 73	0.603 55	0.888 58	0.833 48	0.665 48
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D	0.673 59	0.789 44	0.748 65	0.763 62	0.635 63	0.814 65	0.407 86	0.747 64	0.581 62	0.573 57	0.950 55	0.484 55	0.271 54	0.607 58	0.754 59	0.649 69	0.774 43	0.596 58	0.883 61	0.823 54	0.606 68

SALANet	0.670 60	0.816 38	0.770 53	0.768 59	0.652 55	0.807 70	0.451 64	0.747 64	0.659 27	0.545 63	0.924 99	0.473 60	0.149 106	0.571 69	0.811 51	0.635 79	0.746 64	0.623 47	0.892 55	0.794 73	0.570 81

O3DSeg	0.668 61	0.822 36	0.771 52	0.496 110	0.651 56	0.833 43	0.541 22	0.761 59	0.555 73	0.611 42	0.966 15	0.489 53	0.370 6	0.388 103	0.580 87	0.776 16	0.751 61	0.570 69	0.956 7	0.817 59	0.646 55

PointConv	0.666 62	0.781 48	0.759 58	0.699 75	0.644 60	0.822 55	0.475 54	0.779 54	0.564 70	0.504 81	0.953 44	0.428 81	0.203 85	0.586 64	0.754 59	0.661 65	0.753 60	0.588 64	0.902 47	0.813 62	0.642 56
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNL	0.666 62	0.703 86	0.781 46	0.751 66	0.655 53	0.830 46	0.471 56	0.769 57	0.474 94	0.537 66	0.951 51	0.475 59	0.279 47	0.635 49	0.698 73	0.675 59	0.751 61	0.553 80	0.816 93	0.806 64	0.703 39
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++	0.663 64	0.746 65	0.708 79	0.722 68	0.638 62	0.820 58	0.451 64	0.566 100	0.599 53	0.541 64	0.950 55	0.510 47	0.313 28	0.648 45	0.819 49	0.616 84	0.682 88	0.590 62	0.869 78	0.810 63	0.656 51
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net	0.658 65	0.778 49	0.702 82	0.806 44	0.619 66	0.813 68	0.468 58	0.693 80	0.494 87	0.524 72	0.941 81	0.449 73	0.298 35	0.510 86	0.821 47	0.675 59	0.727 72	0.568 72	0.826 91	0.803 66	0.637 58
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN	0.656 66	0.698 88	0.743 70	0.650 91	0.564 83	0.820 58	0.505 39	0.758 60	0.631 37	0.479 85	0.945 69	0.480 57	0.226 71	0.572 68	0.774 57	0.690 54	0.735 68	0.614 50	0.853 85	0.776 88	0.597 74
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-seg	0.654 67	0.752 63	0.734 74	0.664 88	0.583 78	0.815 64	0.399 88	0.754 62	0.639 34	0.535 68	0.942 79	0.470 61	0.309 30	0.665 41	0.539 90	0.650 68	0.708 78	0.635 41	0.857 84	0.793 75	0.642 56
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Net	0.645 68	0.778 49	0.731 75	0.699 75	0.577 79	0.829 47	0.446 69	0.736 68	0.477 93	0.523 74	0.945 69	0.454 68	0.269 56	0.484 93	0.749 62	0.618 82	0.738 66	0.599 57	0.827 90	0.792 78	0.621 63

MVPNet	0.641 69	0.831 32	0.715 77	0.671 85	0.590 74	0.781 84	0.394 90	0.679 82	0.642 32	0.553 61	0.937 86	0.462 64	0.256 62	0.649 44	0.406 103	0.626 80	0.691 85	0.666 32	0.877 67	0.792 78	0.608 67
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN	0.641 69	0.776 51	0.703 81	0.721 69	0.557 86	0.826 50	0.451 64	0.672 85	0.563 71	0.483 84	0.943 78	0.425 84	0.162 101	0.644 46	0.726 64	0.659 66	0.709 77	0.572 68	0.875 69	0.786 83	0.559 87

PointMRNet	0.640 71	0.717 82	0.701 83	0.692 78	0.576 80	0.801 74	0.467 60	0.716 73	0.563 71	0.459 91	0.953 44	0.429 80	0.169 98	0.581 65	0.854 36	0.605 85	0.710 75	0.550 82	0.894 54	0.793 75	0.575 79

FPConv	0.639 72	0.785 46	0.760 57	0.713 73	0.603 69	0.798 76	0.392 92	0.534 105	0.603 51	0.524 72	0.948 61	0.457 66	0.250 64	0.538 80	0.723 66	0.598 89	0.696 83	0.614 50	0.872 74	0.799 68	0.567 84
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net	0.638 73	0.797 42	0.769 54	0.641 96	0.590 74	0.820 58	0.461 62	0.537 104	0.637 35	0.536 67	0.947 63	0.388 94	0.206 82	0.656 42	0.668 78	0.647 72	0.732 70	0.585 66	0.868 79	0.793 75	0.473 107

PointSPNet	0.637 74	0.734 71	0.692 90	0.714 72	0.576 80	0.797 77	0.446 69	0.743 66	0.598 54	0.437 96	0.942 79	0.403 90	0.150 105	0.626 53	0.800 55	0.649 69	0.697 82	0.557 78	0.846 87	0.777 87	0.563 85

SConv	0.636 75	0.830 33	0.697 86	0.752 65	0.572 82	0.780 86	0.445 71	0.716 73	0.529 77	0.530 69	0.951 51	0.446 75	0.170 97	0.507 88	0.666 79	0.636 78	0.682 88	0.541 88	0.886 59	0.799 68	0.594 75

Supervoxel-CNN	0.635 76	0.656 93	0.711 78	0.719 70	0.613 67	0.757 95	0.444 74	0.765 58	0.534 76	0.566 58	0.928 97	0.478 58	0.272 52	0.636 48	0.531 92	0.664 63	0.645 98	0.508 96	0.864 81	0.792 78	0.611 64

joint point-based	0.634 77	0.614 101	0.778 47	0.667 87	0.633 64	0.825 51	0.420 82	0.804 48	0.467 96	0.561 59	0.951 51	0.494 50	0.291 39	0.566 70	0.458 98	0.579 95	0.764 51	0.559 77	0.838 88	0.814 60	0.598 73
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL	0.632 78	0.731 73	0.688 93	0.675 82	0.591 73	0.784 83	0.444 74	0.565 101	0.610 45	0.492 82	0.949 59	0.456 67	0.254 63	0.587 62	0.706 69	0.599 88	0.665 94	0.612 53	0.868 79	0.791 81	0.579 78

PointNet2-SFPN	0.631 79	0.771 55	0.692 90	0.672 83	0.524 92	0.837 38	0.440 76	0.706 78	0.538 75	0.446 93	0.944 75	0.421 86	0.219 76	0.552 76	0.751 61	0.591 91	0.737 67	0.543 87	0.901 49	0.768 90	0.557 88

3DSM_DMMF	0.631 79	0.626 98	0.745 68	0.801 47	0.607 68	0.751 96	0.506 38	0.729 71	0.565 69	0.491 83	0.866 113	0.434 76	0.197 89	0.595 60	0.630 83	0.709 45	0.705 80	0.560 75	0.875 69	0.740 98	0.491 102

APCF-Net	0.631 79	0.742 68	0.687 95	0.672 83	0.557 86	0.792 81	0.408 84	0.665 87	0.545 74	0.508 78	0.952 49	0.428 81	0.186 92	0.634 50	0.702 71	0.620 81	0.706 79	0.555 79	0.873 72	0.798 70	0.581 77
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv	0.630 82	0.604 103	0.741 72	0.766 61	0.590 74	0.747 97	0.501 41	0.734 69	0.503 86	0.527 70	0.919 103	0.454 68	0.323 25	0.550 78	0.420 102	0.678 58	0.688 86	0.544 85	0.896 52	0.795 72	0.627 62
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR	0.628 83	0.800 41	0.625 105	0.719 70	0.545 89	0.806 71	0.445 71	0.597 95	0.448 101	0.519 76	0.938 85	0.481 56	0.328 23	0.489 92	0.499 97	0.657 67	0.759 56	0.592 61	0.881 63	0.797 71	0.634 59

SegGroup_sem	0.627 84	0.818 37	0.747 67	0.701 74	0.602 70	0.764 92	0.385 96	0.629 92	0.490 89	0.508 78	0.931 96	0.409 89	0.201 86	0.564 71	0.725 65	0.618 82	0.692 84	0.539 89	0.873 72	0.794 73	0.548 91
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv	0.625 85	0.830 33	0.694 88	0.757 63	0.563 84	0.772 90	0.448 68	0.647 90	0.520 80	0.509 77	0.949 59	0.431 79	0.191 90	0.496 90	0.614 85	0.647 72	0.672 92	0.535 92	0.876 68	0.783 84	0.571 80

Weakly-Openseg v3	0.625 85	0.924 8	0.787 42	0.620 98	0.555 88	0.811 69	0.393 91	0.666 86	0.382 109	0.520 75	0.953 44	0.250 113	0.208 80	0.604 59	0.670 76	0.644 76	0.742 65	0.538 90	0.919 36	0.803 66	0.513 99

dtc_net	0.625 85	0.703 86	0.751 64	0.794 50	0.535 90	0.848 27	0.480 53	0.676 84	0.528 78	0.469 88	0.944 75	0.454 68	0.004 118	0.464 95	0.636 82	0.704 48	0.758 57	0.548 84	0.924 30	0.787 82	0.492 101

HPEIN	0.618 88	0.729 74	0.668 96	0.647 93	0.597 72	0.766 91	0.414 83	0.680 81	0.520 80	0.525 71	0.946 66	0.432 77	0.215 78	0.493 91	0.599 86	0.638 77	0.617 103	0.570 69	0.897 51	0.806 64	0.605 70
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCN	0.610 89	0.858 26	0.772 50	0.489 111	0.532 91	0.792 81	0.404 87	0.643 91	0.570 68	0.507 80	0.935 89	0.414 88	0.046 115	0.510 86	0.702 71	0.602 87	0.705 80	0.549 83	0.859 83	0.773 89	0.534 94
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN	0.609 90	0.760 60	0.667 97	0.649 92	0.521 93	0.793 79	0.457 63	0.648 89	0.528 78	0.434 98	0.947 63	0.401 91	0.153 104	0.454 96	0.721 67	0.648 71	0.717 74	0.536 91	0.904 44	0.765 91	0.485 103
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer	0.600 91	0.634 97	0.743 70	0.697 77	0.601 71	0.781 84	0.437 78	0.585 98	0.493 88	0.446 93	0.933 94	0.394 92	0.011 117	0.654 43	0.661 81	0.603 86	0.733 69	0.526 93	0.832 89	0.761 93	0.480 104

LAP-D	0.594 92	0.720 80	0.692 90	0.637 97	0.456 102	0.773 89	0.391 94	0.730 70	0.587 58	0.445 95	0.940 83	0.381 95	0.288 40	0.434 99	0.453 100	0.591 91	0.649 96	0.581 67	0.777 97	0.749 97	0.610 66

DPC	0.592 93	0.720 80	0.700 84	0.602 102	0.480 98	0.762 94	0.380 97	0.713 76	0.585 61	0.437 96	0.940 83	0.369 97	0.288 40	0.434 99	0.509 96	0.590 93	0.639 101	0.567 73	0.772 98	0.755 95	0.592 76
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet	0.589 94	0.766 59	0.659 100	0.683 80	0.470 101	0.740 99	0.387 95	0.620 94	0.490 89	0.476 86	0.922 101	0.355 100	0.245 67	0.511 85	0.511 95	0.571 96	0.643 99	0.493 100	0.872 74	0.762 92	0.600 72

ROSMRF	0.580 95	0.772 54	0.707 80	0.681 81	0.563 84	0.764 92	0.362 99	0.515 106	0.465 97	0.465 90	0.936 88	0.427 83	0.207 81	0.438 97	0.577 88	0.536 99	0.675 91	0.486 101	0.723 104	0.779 85	0.524 96

SD-DETR	0.576 96	0.746 65	0.609 109	0.445 115	0.517 94	0.643 110	0.366 98	0.714 75	0.456 99	0.468 89	0.870 112	0.432 77	0.264 59	0.558 74	0.674 75	0.586 94	0.688 86	0.482 102	0.739 102	0.733 100	0.537 93

SQN_0.1%	0.569 97	0.676 90	0.696 87	0.657 89	0.497 95	0.779 87	0.424 80	0.548 102	0.515 82	0.376 103	0.902 110	0.422 85	0.357 10	0.379 104	0.456 99	0.596 90	0.659 95	0.544 85	0.685 107	0.665 111	0.556 89

TextureNet	0.566 98	0.672 92	0.664 98	0.671 85	0.494 96	0.719 100	0.445 71	0.678 83	0.411 107	0.396 101	0.935 89	0.356 99	0.225 73	0.412 101	0.535 91	0.565 97	0.636 102	0.464 104	0.794 96	0.680 108	0.568 83
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet	0.562 99	0.648 94	0.700 84	0.770 58	0.586 77	0.687 104	0.333 103	0.650 88	0.514 83	0.475 87	0.906 107	0.359 98	0.223 75	0.340 106	0.442 101	0.422 110	0.668 93	0.501 97	0.708 105	0.779 85	0.534 94

Pointnet++ & Feature	0.557 100	0.735 70	0.661 99	0.686 79	0.491 97	0.744 98	0.392 92	0.539 103	0.451 100	0.375 104	0.946 66	0.376 96	0.205 83	0.403 102	0.356 106	0.553 98	0.643 99	0.497 98	0.824 92	0.756 94	0.515 97

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

PanopticFusion-label	0.529 102	0.491 112	0.688 93	0.604 101	0.386 107	0.632 111	0.225 117	0.705 79	0.434 104	0.293 111	0.815 115	0.348 101	0.241 68	0.499 89	0.669 77	0.507 101	0.649 96	0.442 110	0.796 95	0.602 115	0.561 86
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak	0.516 103	0.676 90	0.591 112	0.609 99	0.442 103	0.774 88	0.335 102	0.597 95	0.422 106	0.357 106	0.932 95	0.341 102	0.094 111	0.298 108	0.528 94	0.473 106	0.676 90	0.495 99	0.602 113	0.721 103	0.349 115

Online SegFusion	0.515 104	0.607 102	0.644 103	0.579 104	0.434 104	0.630 112	0.353 100	0.628 93	0.440 102	0.410 99	0.762 118	0.307 105	0.167 99	0.520 83	0.403 104	0.516 100	0.565 106	0.447 108	0.678 108	0.701 105	0.514 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	0.501 105	0.558 107	0.608 110	0.424 117	0.478 99	0.690 103	0.246 113	0.586 97	0.468 95	0.450 92	0.911 105	0.394 92	0.160 102	0.438 97	0.212 113	0.432 109	0.541 111	0.475 103	0.742 101	0.727 101	0.477 105

PCNN	0.498 106	0.559 106	0.644 103	0.560 106	0.420 106	0.711 102	0.229 115	0.414 108	0.436 103	0.352 107	0.941 81	0.324 104	0.155 103	0.238 113	0.387 105	0.493 102	0.529 112	0.509 94	0.813 94	0.751 96	0.504 100

3DMV	0.484 107	0.484 113	0.538 115	0.643 95	0.424 105	0.606 115	0.310 104	0.574 99	0.433 105	0.378 102	0.796 116	0.301 106	0.214 79	0.537 81	0.208 114	0.472 107	0.507 115	0.413 113	0.693 106	0.602 115	0.539 92
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 108	0.577 105	0.611 108	0.356 119	0.321 115	0.715 101	0.299 108	0.376 112	0.328 115	0.319 109	0.944 75	0.285 108	0.164 100	0.216 116	0.229 111	0.484 104	0.545 110	0.456 106	0.755 99	0.709 104	0.475 106
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 109	0.679 89	0.604 111	0.578 105	0.380 108	0.682 105	0.291 109	0.106 119	0.483 92	0.258 117	0.920 102	0.258 112	0.025 116	0.231 115	0.325 107	0.480 105	0.560 108	0.463 105	0.725 103	0.666 110	0.231 119
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproduce	0.446 110	0.474 114	0.623 106	0.463 113	0.366 110	0.651 108	0.310 104	0.389 111	0.349 113	0.330 108	0.937 86	0.271 110	0.126 108	0.285 109	0.224 112	0.350 115	0.577 105	0.445 109	0.625 111	0.723 102	0.394 111
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET2	0.442 111	0.548 108	0.548 114	0.597 103	0.363 111	0.628 113	0.300 106	0.292 114	0.374 110	0.307 110	0.881 111	0.268 111	0.186 92	0.238 113	0.204 115	0.407 111	0.506 116	0.449 107	0.667 109	0.620 114	0.462 109

SurfaceConvPF	0.442 111	0.505 110	0.622 107	0.380 118	0.342 113	0.654 107	0.227 116	0.397 110	0.367 111	0.276 113	0.924 99	0.240 114	0.198 88	0.359 105	0.262 109	0.366 112	0.581 104	0.435 111	0.640 110	0.668 109	0.398 110
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutions	0.438 113	0.437 116	0.646 102	0.474 112	0.369 109	0.645 109	0.353 100	0.258 116	0.282 118	0.279 112	0.918 104	0.298 107	0.147 107	0.283 110	0.294 108	0.487 103	0.562 107	0.427 112	0.619 112	0.633 113	0.352 114
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS	0.425 114	0.525 109	0.647 101	0.522 107	0.324 114	0.488 119	0.077 120	0.712 77	0.353 112	0.401 100	0.636 120	0.281 109	0.176 95	0.340 106	0.565 89	0.175 119	0.551 109	0.398 114	0.370 120	0.602 115	0.361 113

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

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

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

ScanNet	0.306 120	0.203 119	0.366 119	0.501 108	0.311 116	0.524 118	0.211 118	0.002 121	0.342 114	0.189 119	0.786 117	0.145 119	0.102 110	0.245 112	0.152 117	0.318 117	0.348 119	0.300 119	0.460 118	0.437 120	0.182 120
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR	0.054 121	0.000 121	0.041 121	0.172 121	0.030 121	0.062 122	0.001 121	0.035 120	0.004 121	0.051 121	0.143 121	0.019 121	0.003 119	0.041 118	0.050 119	0.003 120	0.054 121	0.018 121	0.005 122	0.264 121	0.082 121

MVF-GNN	0.014 122	0.000 121	0.000 122	0.000 122	0.007 122	0.086 121	0.000 122	0.000 122	0.001 122	0.000 122	0.029 122	0.001 122	0.000 120	0.000 122	0.000 121	0.000 121	0.000 122	0.018 121	0.015 121	0.115 122	0.000 122

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

Method	avg ap 25%	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

PointRel	0.901 1	1.000 1	0.978 23	0.928 3	0.879 1	0.962 5	0.882 5	0.749 37	0.947 3	0.912 2	0.802 3	0.753 18	0.820 2	1.000 1	0.984 4	0.919 5	0.894 4	1.000 1	0.815 15
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
Competitor-MAFT	0.896 2	1.000 1	1.000 1	0.872 16	0.847 11	0.967 3	0.955 1	0.778 33	0.901 15	0.919 1	0.784 5	0.812 1	0.770 12	1.000 1	0.949 9	0.865 34	0.868 18	1.000 1	0.840 5

OneFormer3D	0.896 2	1.000 1	1.000 1	0.913 6	0.858 6	0.951 10	0.786 15	0.837 19	0.916 12	0.908 4	0.778 8	0.803 6	0.750 14	1.000 1	0.976 6	0.926 4	0.882 8	0.995 48	0.849 2
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
MG-Former	0.887 4	1.000 1	0.991 14	0.837 26	0.801 24	0.935 19	0.887 4	0.857 11	0.946 4	0.891 10	0.748 18	0.805 5	0.739 16	1.000 1	0.993 2	0.809 58	0.876 15	1.000 1	0.842 4

DCD	0.885 5	1.000 1	0.933 40	0.856 22	0.832 15	0.959 7	0.930 2	0.858 10	0.802 37	0.859 18	0.767 9	0.796 9	0.709 20	1.000 1	0.971 7	0.871 28	0.904 2	1.000 1	0.874 1

UniPerception	0.884 6	1.000 1	0.979 20	0.872 16	0.869 3	0.892 28	0.806 12	0.890 6	0.835 29	0.892 9	0.755 14	0.811 2	0.779 10	0.955 48	0.951 8	0.876 22	0.914 1	0.997 40	0.840 6

KmaxOneFormerNet	0.883 7	1.000 1	1.000 1	0.798 40	0.848 10	0.971 1	0.853 6	0.903 3	0.827 32	0.910 3	0.748 17	0.809 4	0.724 18	1.000 1	0.980 5	0.855 40	0.844 24	1.000 1	0.832 7

InsSSM	0.883 7	1.000 1	0.996 6	0.800 39	0.865 4	0.960 6	0.808 11	0.852 16	0.940 6	0.899 8	0.785 4	0.810 3	0.700 22	1.000 1	0.912 20	0.851 43	0.895 3	0.997 40	0.827 9
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
Competitor-SPFormer	0.881 9	1.000 1	1.000 1	0.845 24	0.854 7	0.962 4	0.714 22	0.857 12	0.904 14	0.902 6	0.782 7	0.789 12	0.662 28	1.000 1	0.988 3	0.874 25	0.886 7	0.997 40	0.847 3

TST3D	0.879 10	1.000 1	0.994 9	0.921 5	0.807 23	0.939 16	0.771 16	0.887 7	0.923 10	0.862 17	0.722 23	0.768 15	0.756 13	1.000 1	0.910 30	0.904 7	0.836 27	0.999 39	0.824 11
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
SIM3D	0.878 11	1.000 1	0.972 25	0.863 19	0.817 21	0.952 9	0.821 9	0.783 30	0.890 18	0.902 7	0.735 21	0.797 7	0.799 9	1.000 1	0.931 17	0.893 13	0.853 22	1.000 1	0.792 18

EV3D	0.877 12	1.000 1	0.996 8	0.873 14	0.854 8	0.950 11	0.691 26	0.783 31	0.926 7	0.889 13	0.754 15	0.794 11	0.820 2	1.000 1	0.912 20	0.900 9	0.860 20	1.000 1	0.779 21

Spherical Mask(CtoF)	0.875 13	1.000 1	0.991 15	0.873 14	0.850 9	0.946 13	0.691 26	0.752 36	0.926 7	0.889 12	0.759 12	0.794 10	0.820 2	1.000 1	0.912 20	0.900 9	0.878 12	1.000 1	0.769 23

TD3D	0.875 13	1.000 1	0.976 24	0.877 12	0.783 30	0.970 2	0.889 3	0.828 20	0.945 5	0.803 23	0.713 25	0.720 25	0.709 19	1.000 1	0.936 15	0.934 3	0.873 16	1.000 1	0.791 19
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Queryformer	0.874 15	1.000 1	0.978 22	0.809 37	0.876 2	0.936 18	0.702 23	0.716 42	0.920 11	0.875 16	0.766 10	0.772 14	0.818 6	1.000 1	0.995 1	0.916 6	0.892 5	1.000 1	0.767 24

SoftGroup++	0.874 15	1.000 1	0.972 26	0.947 1	0.839 14	0.898 27	0.556 41	0.913 2	0.881 21	0.756 25	0.828 2	0.748 20	0.821 1	1.000 1	0.937 14	0.937 1	0.887 6	1.000 1	0.821 12

Mask3D	0.870 17	1.000 1	0.985 17	0.782 47	0.818 20	0.938 17	0.760 17	0.749 37	0.923 9	0.877 15	0.760 11	0.785 13	0.820 2	1.000 1	0.912 20	0.864 36	0.878 12	0.983 54	0.825 10
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
ExtMask3D	0.867 18	1.000 1	1.000 1	0.756 54	0.816 22	0.940 15	0.795 13	0.760 35	0.862 23	0.888 14	0.739 19	0.763 16	0.774 11	1.000 1	0.929 18	0.878 21	0.879 10	1.000 1	0.819 14

SoftGroup	0.865 19	1.000 1	0.969 27	0.860 20	0.860 5	0.913 23	0.558 38	0.899 4	0.911 13	0.760 24	0.828 1	0.736 22	0.802 8	0.981 45	0.919 19	0.875 23	0.877 14	1.000 1	0.820 13
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
MAFT	0.860 20	1.000 1	0.990 16	0.810 36	0.829 16	0.949 12	0.809 10	0.688 48	0.836 28	0.904 5	0.751 16	0.796 8	0.741 15	1.000 1	0.864 40	0.848 45	0.837 25	1.000 1	0.828 8

SPFormer	0.851 21	1.000 1	0.994 10	0.806 38	0.774 32	0.942 14	0.637 30	0.849 17	0.859 25	0.889 11	0.720 24	0.730 23	0.665 27	1.000 1	0.911 27	0.868 33	0.873 17	1.000 1	0.796 17
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
IPCA-Inst	0.851 21	1.000 1	0.968 28	0.884 11	0.842 13	0.862 40	0.693 25	0.812 25	0.888 20	0.677 37	0.783 6	0.698 26	0.807 7	1.000 1	0.911 27	0.865 35	0.865 19	1.000 1	0.757 27

Mask3D_evaluation	0.843 23	1.000 1	0.955 33	0.847 23	0.795 26	0.932 20	0.750 19	0.780 32	0.891 17	0.818 20	0.737 20	0.633 35	0.703 21	1.000 1	0.902 32	0.870 29	0.820 28	0.941 62	0.805 16

ISBNet	0.835 24	1.000 1	0.950 34	0.731 56	0.819 18	0.918 21	0.790 14	0.740 39	0.851 27	0.831 19	0.661 33	0.742 21	0.650 31	1.000 1	0.937 13	0.814 57	0.836 26	1.000 1	0.765 25
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
SphereSeg	0.835 24	1.000 1	0.963 31	0.891 9	0.794 27	0.954 8	0.822 8	0.710 43	0.961 2	0.721 29	0.693 31	0.530 48	0.653 30	1.000 1	0.867 39	0.857 39	0.859 21	0.991 51	0.771 22

GraphCut	0.832 26	1.000 1	0.922 49	0.724 58	0.798 25	0.902 26	0.701 24	0.856 14	0.859 24	0.715 30	0.706 26	0.748 19	0.640 42	1.000 1	0.934 16	0.862 37	0.880 9	1.000 1	0.729 30

TopoSeg	0.832 26	1.000 1	0.981 19	0.933 2	0.819 19	0.826 49	0.524 47	0.841 18	0.811 34	0.681 36	0.759 13	0.687 27	0.727 17	0.981 45	0.911 27	0.883 17	0.853 23	1.000 1	0.756 28

PBNet	0.825 28	1.000 1	0.963 30	0.837 28	0.843 12	0.865 35	0.822 7	0.647 51	0.878 22	0.733 27	0.639 40	0.683 28	0.650 31	1.000 1	0.853 41	0.870 30	0.820 29	1.000 1	0.744 29
Weiguang Zhao, Yuyao Yan, Chaolong Yang, Jianan Ye, Xi Yang, Kaizhu Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
SSEC	0.820 29	1.000 1	0.983 18	0.924 4	0.826 17	0.817 52	0.415 56	0.899 5	0.793 39	0.673 38	0.731 22	0.636 33	0.653 29	1.000 1	0.939 12	0.804 60	0.878 11	1.000 1	0.780 20

DKNet	0.815 30	1.000 1	0.930 41	0.844 25	0.765 36	0.915 22	0.534 45	0.805 27	0.805 36	0.807 22	0.654 34	0.763 17	0.650 31	1.000 1	0.794 53	0.881 18	0.766 33	1.000 1	0.758 26
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN	0.806 31	1.000 1	0.992 12	0.789 42	0.723 49	0.891 29	0.650 29	0.810 26	0.832 30	0.665 40	0.699 29	0.658 29	0.700 22	1.000 1	0.881 34	0.832 49	0.774 31	0.997 40	0.613 50
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
HAIS	0.803 32	1.000 1	0.994 10	0.820 32	0.759 37	0.855 41	0.554 42	0.882 8	0.827 33	0.615 46	0.676 32	0.638 32	0.646 40	1.000 1	0.912 20	0.797 63	0.767 32	0.994 49	0.726 31
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Box2Mask	0.803 32	1.000 1	0.962 32	0.874 13	0.707 53	0.887 32	0.686 28	0.598 56	0.961 1	0.715 31	0.694 30	0.469 53	0.700 22	1.000 1	0.912 20	0.902 8	0.753 38	0.997 40	0.637 44
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
Mask-Group	0.792 34	1.000 1	0.968 29	0.812 33	0.766 35	0.864 36	0.460 50	0.815 24	0.888 19	0.598 50	0.651 37	0.639 31	0.600 48	0.918 51	0.941 10	0.896 12	0.721 45	1.000 1	0.723 32
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained	0.791 35	1.000 1	0.996 6	0.829 31	0.767 34	0.889 31	0.600 33	0.819 23	0.770 44	0.594 51	0.620 44	0.541 45	0.700 22	1.000 1	0.941 10	0.889 15	0.763 34	1.000 1	0.526 60

SSTNet	0.789 36	1.000 1	0.840 63	0.888 10	0.717 50	0.835 45	0.717 21	0.684 49	0.627 59	0.724 28	0.652 36	0.727 24	0.600 48	1.000 1	0.912 20	0.822 52	0.757 37	1.000 1	0.691 38
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN	0.788 37	1.000 1	0.978 21	0.867 18	0.781 31	0.833 46	0.527 46	0.824 21	0.806 35	0.549 59	0.596 47	0.551 41	0.700 22	1.000 1	0.853 41	0.935 2	0.733 42	1.000 1	0.651 41

DENet	0.786 38	1.000 1	0.929 42	0.736 55	0.750 43	0.720 65	0.755 18	0.934 1	0.794 38	0.590 52	0.561 53	0.537 46	0.650 31	1.000 1	0.882 33	0.804 61	0.789 30	1.000 1	0.719 33

DANCENET	0.786 38	1.000 1	0.936 37	0.783 45	0.737 46	0.852 43	0.742 20	0.647 51	0.765 46	0.811 21	0.624 43	0.579 38	0.632 45	1.000 1	0.909 31	0.898 11	0.696 50	0.944 58	0.601 53

DualGroup	0.782 40	1.000 1	0.927 43	0.811 34	0.772 33	0.853 42	0.631 32	0.805 27	0.773 41	0.613 47	0.611 45	0.610 36	0.650 31	0.835 62	0.881 34	0.879 20	0.750 40	1.000 1	0.675 39

PointGroup	0.778 41	1.000 1	0.900 53	0.798 41	0.715 51	0.863 37	0.493 48	0.706 44	0.895 16	0.569 57	0.701 27	0.576 39	0.639 43	1.000 1	0.880 36	0.851 42	0.719 46	0.997 40	0.709 35
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
PE	0.776 42	1.000 1	0.900 54	0.860 20	0.728 48	0.869 33	0.400 57	0.857 13	0.774 40	0.568 58	0.701 28	0.602 37	0.646 40	0.933 50	0.843 44	0.890 14	0.691 54	0.997 40	0.709 34
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
AOIA	0.767 43	1.000 1	0.937 36	0.810 35	0.740 45	0.906 24	0.550 43	0.800 29	0.706 51	0.577 56	0.624 42	0.544 44	0.596 53	0.857 54	0.879 38	0.880 19	0.750 39	0.992 50	0.658 40

DD-UNet+Group	0.764 44	1.000 1	0.897 56	0.837 27	0.753 40	0.830 48	0.459 52	0.824 21	0.699 53	0.629 44	0.653 35	0.438 56	0.650 31	1.000 1	0.880 36	0.858 38	0.690 55	1.000 1	0.650 42
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
INS-Conv-instance	0.762 45	1.000 1	0.923 46	0.765 50	0.785 29	0.905 25	0.600 33	0.655 50	0.646 58	0.683 35	0.647 38	0.530 47	0.650 31	1.000 1	0.824 46	0.830 50	0.693 53	0.944 58	0.644 43

Dyco3D	0.761 46	1.000 1	0.935 38	0.893 8	0.752 42	0.863 38	0.600 33	0.588 57	0.742 48	0.641 42	0.633 41	0.546 43	0.550 55	0.857 54	0.789 55	0.853 41	0.762 35	0.987 52	0.699 36
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance	0.742 47	1.000 1	0.923 46	0.785 43	0.745 44	0.867 34	0.557 39	0.578 60	0.729 49	0.670 39	0.644 39	0.488 51	0.577 54	1.000 1	0.794 53	0.830 50	0.620 63	1.000 1	0.550 56
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg	0.739 48	1.000 1	0.899 55	0.759 52	0.753 41	0.823 50	0.282 62	0.691 47	0.658 56	0.582 55	0.594 48	0.547 42	0.628 46	1.000 1	0.795 52	0.868 32	0.728 44	1.000 1	0.692 37

3D-MPA	0.737 49	1.000 1	0.933 39	0.785 43	0.794 28	0.831 47	0.279 64	0.588 57	0.695 54	0.616 45	0.559 54	0.556 40	0.650 31	1.000 1	0.809 50	0.875 24	0.696 51	1.000 1	0.608 52
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML	0.731 50	1.000 1	0.992 12	0.779 49	0.609 62	0.746 60	0.308 61	0.867 9	0.601 62	0.607 48	0.539 57	0.519 49	0.550 55	1.000 1	0.824 46	0.869 31	0.729 43	1.000 1	0.616 48
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
OSIS	0.725 51	1.000 1	0.885 59	0.653 64	0.657 59	0.801 53	0.576 37	0.695 46	0.828 31	0.698 33	0.534 58	0.457 55	0.500 62	0.857 54	0.831 45	0.841 47	0.627 61	1.000 1	0.619 47

SSEN	0.724 52	1.000 1	0.926 44	0.781 48	0.661 57	0.845 44	0.596 36	0.529 63	0.764 47	0.653 41	0.489 64	0.461 54	0.500 62	0.859 53	0.765 56	0.872 27	0.761 36	1.000 1	0.577 54
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
NeuralBF	0.718 53	1.000 1	0.945 35	0.901 7	0.754 39	0.817 51	0.460 50	0.700 45	0.772 42	0.688 34	0.568 52	0.000 75	0.500 62	0.981 45	0.606 66	0.872 26	0.740 41	1.000 1	0.614 49
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
Sparse R-CNN	0.714 54	1.000 1	0.926 45	0.694 59	0.699 55	0.890 30	0.636 31	0.516 64	0.693 55	0.743 26	0.588 49	0.369 60	0.601 47	0.594 68	0.800 51	0.886 16	0.676 56	0.986 53	0.546 57

SALoss-ResNet	0.695 55	1.000 1	0.855 61	0.579 69	0.589 64	0.735 63	0.484 49	0.588 57	0.856 26	0.634 43	0.571 51	0.298 61	0.500 62	1.000 1	0.824 46	0.818 53	0.702 49	0.935 65	0.545 58
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
PanopticFusion-inst	0.693 56	1.000 1	0.852 62	0.655 63	0.616 61	0.788 55	0.334 59	0.763 34	0.771 43	0.457 69	0.555 55	0.652 30	0.518 59	0.857 54	0.765 56	0.732 69	0.631 59	0.944 58	0.577 55
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Occipital-SCS	0.688 57	1.000 1	0.913 50	0.730 57	0.737 47	0.743 62	0.442 53	0.855 15	0.655 57	0.546 60	0.546 56	0.263 63	0.508 61	0.889 52	0.568 67	0.771 66	0.705 48	0.889 68	0.625 46

3D-BoNet	0.687 58	1.000 1	0.887 58	0.836 29	0.587 65	0.643 72	0.550 43	0.620 53	0.724 50	0.522 64	0.501 62	0.243 64	0.512 60	1.000 1	0.751 58	0.807 59	0.661 58	0.909 67	0.612 51
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
ClickSeg_Instance	0.685 59	1.000 1	0.818 65	0.600 67	0.715 52	0.795 54	0.557 39	0.533 62	0.591 64	0.601 49	0.519 60	0.429 58	0.638 44	0.938 49	0.706 61	0.817 55	0.624 62	0.944 58	0.502 62

PCJC	0.684 60	1.000 1	0.895 57	0.757 53	0.659 58	0.862 39	0.189 71	0.739 40	0.606 61	0.712 32	0.581 50	0.515 50	0.650 31	0.857 54	0.357 72	0.785 64	0.631 60	0.889 68	0.635 45

SPG_WSIS	0.678 61	1.000 1	0.880 60	0.836 29	0.701 54	0.727 64	0.273 66	0.607 55	0.706 52	0.541 62	0.515 61	0.174 67	0.600 48	0.857 54	0.716 60	0.846 46	0.711 47	1.000 1	0.506 61

One_Thing_One_Click	0.675 62	1.000 1	0.823 64	0.782 46	0.621 60	0.766 57	0.211 68	0.736 41	0.560 66	0.586 53	0.522 59	0.636 34	0.453 66	0.641 66	0.853 41	0.850 44	0.694 52	0.997 40	0.411 67
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SegGroup_ins	0.637 63	1.000 1	0.923 48	0.593 68	0.561 66	0.746 61	0.143 73	0.504 65	0.766 45	0.485 67	0.442 65	0.372 59	0.530 58	0.714 63	0.815 49	0.775 65	0.673 57	1.000 1	0.431 66
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MASC	0.615 64	0.711 71	0.802 66	0.540 70	0.757 38	0.777 56	0.029 74	0.577 61	0.588 65	0.521 65	0.600 46	0.436 57	0.534 57	0.697 64	0.616 65	0.838 48	0.526 65	0.980 55	0.534 59
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone	0.605 65	1.000 1	0.909 51	0.764 51	0.603 63	0.704 66	0.415 55	0.301 70	0.548 67	0.461 68	0.394 66	0.267 62	0.386 68	0.857 54	0.649 64	0.817 54	0.504 67	0.959 56	0.356 70

3D-SIS	0.558 66	1.000 1	0.773 67	0.614 66	0.503 69	0.691 68	0.200 69	0.412 66	0.498 70	0.546 61	0.311 71	0.103 71	0.600 48	0.857 54	0.382 69	0.799 62	0.445 73	0.938 64	0.371 68
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet	0.544 67	0.500 74	0.655 73	0.661 62	0.663 56	0.765 58	0.432 54	0.214 73	0.612 60	0.584 54	0.499 63	0.204 66	0.286 72	0.429 71	0.655 63	0.650 74	0.539 64	0.950 57	0.499 63

Hier3D	0.540 68	1.000 1	0.727 68	0.626 65	0.467 72	0.693 67	0.200 69	0.412 66	0.480 71	0.528 63	0.318 70	0.077 74	0.600 48	0.688 65	0.382 69	0.768 67	0.472 69	0.941 62	0.350 71
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class	0.497 69	0.250 76	0.902 52	0.689 60	0.540 67	0.747 59	0.276 65	0.610 54	0.268 75	0.489 66	0.348 67	0.000 75	0.243 75	0.220 74	0.663 62	0.814 56	0.459 71	0.928 66	0.496 64

Sem_Recon_ins	0.484 70	0.764 70	0.608 75	0.470 72	0.521 68	0.637 73	0.311 60	0.218 72	0.348 74	0.365 73	0.223 72	0.222 65	0.258 73	0.629 67	0.734 59	0.596 75	0.509 66	0.858 71	0.444 65

tmp	0.474 71	1.000 1	0.727 68	0.433 74	0.481 71	0.673 70	0.022 76	0.380 68	0.517 69	0.436 71	0.338 69	0.128 69	0.343 70	0.429 71	0.291 74	0.728 70	0.473 68	0.833 72	0.300 73

SemRegionNet-20cls	0.470 72	1.000 1	0.727 68	0.447 73	0.481 70	0.678 69	0.024 75	0.380 68	0.518 68	0.440 70	0.339 68	0.128 69	0.350 69	0.429 71	0.212 75	0.711 71	0.465 70	0.833 72	0.290 74

ASIS	0.422 73	0.333 75	0.707 71	0.676 61	0.401 73	0.650 71	0.350 58	0.177 74	0.594 63	0.376 72	0.202 73	0.077 73	0.404 67	0.571 69	0.197 76	0.674 73	0.447 72	0.500 75	0.260 75

3D-BEVIS	0.401 74	0.667 72	0.687 72	0.419 75	0.137 76	0.587 74	0.188 72	0.235 71	0.359 73	0.211 75	0.093 76	0.080 72	0.311 71	0.571 69	0.382 69	0.754 68	0.300 75	0.874 70	0.357 69
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet	0.390 75	0.556 73	0.636 74	0.493 71	0.353 74	0.539 75	0.271 67	0.160 75	0.450 72	0.359 74	0.178 74	0.146 68	0.250 74	0.143 75	0.347 73	0.698 72	0.436 74	0.667 74	0.331 72

MaskRCNN 2d->3d Proj	0.261 76	0.903 69	0.081 76	0.008 76	0.233 75	0.175 76	0.280 63	0.106 76	0.150 76	0.203 76	0.175 75	0.480 52	0.218 76	0.143 75	0.542 68	0.404 76	0.153 76	0.393 76	0.049 76

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Presenting the ScanNet200 Benchmark

ScanNet200 Benchmark

ScanNet200 3D Semantic Label Benchmark

ScanNet200 3D Semantic Instance Benchmark

ScanNet Benchmark

3D Semantic Label Benchmark

3D Semantic Instance Benchmark

2D Semantic Label Benchmark

2D Semantic Instance Benchmark

Scene Type Classification Benchmark

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

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

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