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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort by
OccuSeg+Semantic0.764 10.758 280.796 70.839 40.746 20.907 10.562 10.850 50.680 20.672 10.978 10.610 10.335 30.777 10.819 130.847 10.830 10.691 30.972 10.885 10.727 2
VMNet0.746 30.870 50.838 10.858 10.729 40.850 40.501 70.874 20.587 190.658 30.956 40.564 40.299 100.765 30.900 10.716 70.812 40.631 100.939 20.858 40.709 5
MatchingNet0.724 70.812 160.812 60.810 90.735 30.834 80.495 100.860 30.572 240.602 100.954 60.512 90.280 160.757 40.845 100.725 50.780 100.606 180.937 30.851 70.700 7
Virtual MVFusion0.746 30.771 240.819 30.848 20.702 80.865 30.397 420.899 10.699 10.664 20.948 200.588 20.330 40.746 50.851 70.764 30.796 70.704 10.935 40.866 20.728 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
SparseConvNet0.725 60.647 460.821 20.846 30.721 50.869 20.533 20.754 180.603 160.614 60.955 50.572 30.325 50.710 70.870 30.724 60.823 20.628 110.934 50.865 30.683 8
BPNet0.749 20.909 10.818 40.811 80.752 10.839 60.485 120.842 70.673 30.644 40.957 30.528 70.305 90.773 20.859 40.788 20.818 30.693 20.916 60.856 50.723 4
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021
PointContrast_LA_SEM0.683 140.757 290.784 100.786 130.639 170.824 150.408 360.775 130.604 150.541 190.934 460.532 60.269 200.552 320.777 180.645 280.793 80.640 80.913 70.824 110.671 10
AttAN0.609 420.760 270.667 480.649 450.521 440.793 320.457 190.648 430.528 370.434 490.947 230.401 430.153 510.454 460.721 270.648 260.717 250.536 420.904 80.765 440.485 53
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
PointConvpermissive0.666 170.781 190.759 170.699 290.644 160.822 160.475 130.779 120.564 270.504 320.953 70.428 350.203 380.586 240.754 210.661 210.753 170.588 220.902 90.813 180.642 15
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
One Thing One Click0.701 90.825 140.796 70.723 240.716 60.832 90.433 320.816 80.634 80.609 80.969 20.418 400.344 20.559 310.833 110.715 80.808 50.560 300.902 90.847 80.680 9
PointNet2-SFPN0.631 340.771 240.692 410.672 380.524 430.837 70.440 300.706 320.538 340.446 450.944 290.421 390.219 320.552 320.751 230.591 420.737 220.543 390.901 110.768 430.557 43
HPEIN0.618 400.729 350.668 470.647 460.597 250.766 410.414 340.680 360.520 380.525 250.946 250.432 320.215 330.493 440.599 380.638 290.617 510.570 250.897 120.806 190.605 27
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
FusionAwareConv0.630 370.604 500.741 230.766 180.590 270.747 460.501 70.734 250.503 410.527 240.919 530.454 250.323 60.550 340.420 500.678 150.688 370.544 380.896 130.795 250.627 19
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
PointMRNet0.640 260.717 380.701 360.692 320.576 330.801 260.467 180.716 290.563 280.459 430.953 70.429 340.169 460.581 250.854 50.605 360.710 260.550 360.894 140.793 280.575 35
CU-Hybrid Net0.693 110.596 510.789 90.803 110.677 90.800 280.469 160.846 60.554 310.591 120.948 200.500 100.316 70.609 180.847 90.732 40.808 50.593 210.894 140.839 90.652 13
SALANet0.670 160.816 150.770 140.768 170.652 140.807 250.451 200.747 200.659 50.545 180.924 490.473 180.149 540.571 290.811 140.635 310.746 190.623 120.892 160.794 270.570 37
RFCR0.702 80.889 30.745 200.813 70.672 100.818 180.493 110.815 90.623 100.610 70.947 230.470 190.249 250.594 210.848 80.705 110.779 110.646 70.892 160.823 120.611 22
SConv0.636 290.830 120.697 390.752 220.572 360.780 380.445 260.716 290.529 360.530 230.951 100.446 290.170 450.507 400.666 340.636 300.682 380.541 410.886 180.799 220.594 31
ROSMRF3D0.673 150.789 170.748 190.763 190.635 190.814 200.407 390.747 200.581 220.573 130.950 140.484 140.271 190.607 190.754 210.649 240.774 120.596 200.883 190.823 120.606 26
KP-FCNN0.684 130.847 90.758 180.784 140.647 150.814 200.473 140.772 140.605 140.594 110.935 420.450 270.181 440.587 220.805 150.690 130.785 90.614 140.882 200.819 140.632 18
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
MVPNetpermissive0.641 240.831 110.715 300.671 400.590 270.781 370.394 430.679 370.642 60.553 160.937 390.462 220.256 220.649 90.406 510.626 320.691 360.666 50.877 210.792 300.608 25
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
SIConv0.625 380.830 120.694 400.757 200.563 380.772 400.448 220.647 440.520 380.509 290.949 170.431 330.191 410.496 430.614 370.647 270.672 410.535 430.876 220.783 360.571 36
3DSM_DMMF0.631 340.626 480.745 200.801 120.607 230.751 450.506 50.729 270.565 260.491 350.866 600.434 310.197 400.595 200.630 360.709 100.705 300.560 300.875 230.740 500.491 52
PointConv-SFPN0.641 240.776 220.703 340.721 250.557 400.826 130.451 200.672 390.563 280.483 380.943 320.425 380.162 480.644 100.726 250.659 220.709 270.572 240.875 230.786 340.559 42
MinkowskiNetpermissive0.736 50.859 70.818 40.832 50.709 70.840 50.521 40.853 40.660 40.643 50.951 100.544 50.286 150.731 60.893 20.675 160.772 130.683 40.874 250.852 60.727 2
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
SegGCNpermissive0.589 450.833 100.731 260.539 550.514 450.789 350.448 220.467 540.573 230.484 370.936 400.396 440.061 620.501 410.507 450.594 410.700 320.563 290.874 250.771 420.493 51
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
APCF-Net0.631 340.742 310.687 460.672 380.557 400.792 330.408 360.665 410.545 330.508 300.952 90.428 350.186 420.634 130.702 300.620 330.706 290.555 340.873 270.798 240.581 33
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FPConvpermissive0.639 270.785 180.760 160.713 280.603 240.798 300.392 440.534 520.603 160.524 260.948 200.457 230.250 240.538 350.723 260.598 400.696 350.614 140.872 280.799 220.567 39
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
CCRFNet0.589 450.766 260.659 510.683 340.470 500.740 480.387 470.620 470.490 440.476 400.922 510.355 510.245 270.511 370.511 430.571 460.643 470.493 490.872 280.762 450.600 28
JSENet0.699 100.881 40.762 150.821 60.667 110.800 280.522 30.792 110.613 110.607 90.935 420.492 130.205 360.576 270.853 60.691 120.758 160.652 60.872 280.828 100.649 14
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
PointMRNet-lite0.625 380.643 470.711 310.697 310.581 310.801 260.408 360.670 400.558 300.497 330.944 290.436 300.152 520.617 170.708 280.603 370.743 200.532 440.870 310.784 350.545 44
PointMTL0.632 330.731 340.688 440.675 370.591 260.784 360.444 280.565 500.610 120.492 340.949 170.456 240.254 230.587 220.706 290.599 390.665 430.612 170.868 320.791 330.579 34
Supervoxel-CNN0.635 300.656 440.711 310.719 260.613 220.757 440.444 280.765 160.534 350.566 140.928 470.478 160.272 180.636 110.531 420.664 200.645 460.508 460.864 330.792 300.611 22
FusionNet0.688 120.704 390.741 230.754 210.656 120.829 110.501 70.741 230.609 130.548 170.950 140.522 80.371 10.633 140.756 200.715 80.771 140.623 120.861 340.814 160.658 12
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
VACNN++0.664 190.891 20.724 290.680 360.636 180.814 200.438 310.629 460.553 320.537 200.950 140.499 110.247 260.626 150.786 170.666 190.700 320.566 280.860 350.816 150.665 11
SPH3D-GCNpermissive0.610 410.858 80.772 130.489 570.532 420.792 330.404 400.643 450.570 250.507 310.935 420.414 410.046 630.510 380.702 300.602 380.705 300.549 370.859 360.773 410.534 46
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
SAFNet-segcopyleft0.654 220.752 300.734 250.664 430.583 300.815 190.399 410.754 180.639 70.535 220.942 330.470 190.309 80.665 80.539 400.650 230.708 280.635 90.857 370.793 280.642 15
HPGCNN0.656 210.698 410.743 220.650 440.564 370.820 170.505 60.758 170.631 90.479 390.945 270.480 150.226 290.572 280.774 190.690 130.735 230.614 140.853 380.776 400.597 30
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
PointSPNet0.637 280.734 330.692 410.714 270.576 330.797 310.446 240.743 220.598 180.437 470.942 330.403 420.150 530.626 150.800 160.649 240.697 340.557 330.846 390.777 390.563 40
joint point-basedpermissive0.634 310.614 490.778 120.667 420.633 200.825 140.420 330.804 100.467 490.561 150.951 100.494 120.291 120.566 300.458 460.579 450.764 150.559 320.838 400.814 160.598 29
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
RandLA-Netpermissive0.645 230.778 200.731 260.699 290.577 320.829 110.446 240.736 240.477 460.523 280.945 270.454 250.269 200.484 450.749 240.618 350.738 210.599 190.827 410.792 300.621 20
DCM-Net0.658 200.778 200.702 350.806 100.619 210.813 230.468 170.693 340.494 430.524 260.941 350.449 280.298 110.510 380.821 120.675 160.727 240.568 260.826 420.803 210.637 17
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
Pointnet++ & Featurepermissive0.557 500.735 320.661 500.686 330.491 470.744 470.392 440.539 510.451 510.375 530.946 250.376 470.205 360.403 520.356 530.553 480.643 470.497 480.824 430.756 460.515 49
MCCNNpermissive0.633 320.866 60.731 260.771 150.576 330.809 240.410 350.684 350.497 420.491 350.949 170.466 210.105 580.581 250.646 350.620 330.680 390.542 400.817 440.795 250.618 21
P. Hermosilla, T. Ritschel, P.P. Vazquez, A. Vinacua, T. Ropinski: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. SIGGRAPH Asia 2018
PointASNLpermissive0.666 170.703 400.781 110.751 230.655 130.830 100.471 150.769 150.474 470.537 200.951 100.475 170.279 170.635 120.698 320.675 160.751 180.553 350.816 450.806 190.703 6
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PCNN0.498 530.559 540.644 540.560 540.420 530.711 510.229 600.414 550.436 520.352 540.941 350.324 530.155 500.238 590.387 520.493 510.529 580.509 450.813 460.751 480.504 50
PanopticFusion-label0.529 510.491 580.688 440.604 500.386 540.632 580.225 620.705 330.434 530.293 570.815 610.348 520.241 280.499 420.669 330.507 500.649 440.442 560.796 470.602 610.561 41
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
TextureNetpermissive0.566 480.672 430.664 490.671 400.494 460.719 490.445 260.678 380.411 550.396 500.935 420.356 500.225 300.412 510.535 410.565 470.636 500.464 520.794 480.680 550.568 38
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
LAP-D0.594 430.720 360.692 410.637 480.456 510.773 390.391 460.730 260.587 190.445 460.940 370.381 460.288 130.434 490.453 470.591 420.649 440.581 230.777 490.749 490.610 24
DPC0.592 440.720 360.700 370.602 510.480 480.762 430.380 480.713 310.585 210.437 470.940 370.369 480.288 130.434 490.509 440.590 440.639 490.567 270.772 500.755 470.592 32
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
PointCNN with RGBpermissive0.458 550.577 530.611 560.356 640.321 600.715 500.299 540.376 580.328 610.319 550.944 290.285 560.164 470.216 620.229 580.484 530.545 560.456 540.755 510.709 520.475 55
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
3DMV, FTSDF0.501 520.558 550.608 570.424 620.478 490.690 520.246 580.586 480.468 480.450 440.911 550.394 450.160 490.438 470.212 590.432 560.541 570.475 510.742 520.727 510.477 54
FCPNpermissive0.447 560.679 420.604 580.578 530.380 550.682 540.291 550.106 640.483 450.258 620.920 520.258 580.025 640.231 610.325 540.480 540.560 550.463 530.725 530.666 570.231 64
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
ROSMRF0.580 470.772 230.707 330.681 350.563 380.764 420.362 490.515 530.465 500.465 420.936 400.427 370.207 350.438 470.577 390.536 490.675 400.486 500.723 540.779 370.524 48
DVVNet0.562 490.648 450.700 370.770 160.586 290.687 530.333 510.650 420.514 400.475 410.906 570.359 490.223 310.340 540.442 490.422 570.668 420.501 470.708 550.779 370.534 46
3DMV0.484 540.484 590.538 600.643 470.424 520.606 610.310 520.574 490.433 540.378 520.796 620.301 540.214 340.537 360.208 600.472 550.507 610.413 590.693 560.602 610.539 45
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PNET20.442 570.548 560.548 590.597 520.363 570.628 590.300 530.292 590.374 580.307 560.881 590.268 570.186 420.238 590.204 610.407 580.506 620.449 550.667 570.620 590.462 56
SurfaceConvPF0.442 570.505 570.622 550.380 630.342 590.654 560.227 610.397 570.367 590.276 590.924 490.240 600.198 390.359 530.262 560.366 590.581 530.435 570.640 580.668 560.398 57
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 590.437 620.646 530.474 590.369 560.645 570.353 500.258 610.282 630.279 580.918 540.298 550.147 550.283 560.294 550.487 520.562 540.427 580.619 590.633 580.352 59
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
SPLAT Netcopyleft0.393 610.472 610.511 610.606 490.311 610.656 550.245 590.405 560.328 610.197 630.927 480.227 620.000 660.001 660.249 570.271 640.510 590.383 610.593 600.699 530.267 62
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+FTSDF0.383 620.297 640.491 620.432 610.358 580.612 600.274 560.116 630.411 550.265 600.904 580.229 610.079 600.250 570.185 620.320 620.510 590.385 600.548 610.597 630.394 58
PointNet++permissive0.339 630.584 520.478 630.458 600.256 640.360 650.250 570.247 620.278 640.261 610.677 640.183 630.117 560.212 630.145 640.364 600.346 650.232 650.548 610.523 640.252 63
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 640.353 630.290 650.278 650.166 650.553 620.169 640.286 600.147 650.148 650.908 560.182 640.064 610.023 650.018 660.354 610.363 630.345 630.546 630.685 540.278 61
ScanNetpermissive0.306 650.203 650.366 640.501 560.311 610.524 630.211 630.002 660.342 600.189 640.786 630.145 650.102 590.245 580.152 630.318 630.348 640.300 640.460 640.437 650.182 65
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
subcloud_weak0.411 600.479 600.650 520.475 580.285 630.519 640.087 650.725 280.396 570.386 510.621 650.250 590.117 560.338 550.443 480.188 650.594 520.369 620.377 650.616 600.306 60
ERROR0.054 660.000 660.041 660.172 660.030 660.062 660.001 660.035 650.004 660.051 660.143 660.019 660.003 650.041 640.050 650.003 660.054 660.018 660.005 660.264 660.082 66

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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort by
SphereNet0.606 121.000 10.776 90.745 90.436 160.834 20.035 190.587 160.518 40.338 200.534 90.352 150.594 121.000 10.391 180.696 140.624 81.000 10.451 11
GICN0.638 91.000 10.895 10.800 40.480 120.676 150.144 50.737 60.354 130.447 70.400 180.365 130.700 11.000 10.569 80.836 10.599 91.000 10.473 9
3D-MPA0.611 111.000 10.833 40.765 70.526 80.756 140.136 90.588 140.470 50.438 100.432 160.358 140.650 20.857 120.429 140.765 50.557 141.000 10.430 13
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML0.549 151.000 10.807 80.588 140.327 190.647 160.004 240.815 20.180 190.418 140.364 200.182 180.445 171.000 10.442 130.688 170.571 111.000 10.396 14
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
CSC-Pretrained0.648 51.000 10.810 60.768 60.523 90.813 50.143 60.819 10.389 100.422 130.511 100.443 80.650 21.000 10.624 60.732 100.634 71.000 10.375 16
SSEN0.575 141.000 10.761 120.473 180.477 130.795 110.066 150.529 170.658 10.460 50.461 130.380 120.331 220.859 110.401 170.692 160.653 41.000 10.348 18
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
OccuSeg+instance0.672 31.000 10.758 140.682 100.576 30.842 10.477 10.504 190.524 30.567 10.585 40.451 70.557 131.000 10.751 20.797 30.563 121.000 10.467 10
Mask-Group0.664 41.000 10.822 50.764 80.616 20.815 40.139 70.694 80.597 20.459 60.566 50.599 20.600 70.516 230.715 30.819 20.635 61.000 10.603 1
SSTNet0.698 21.000 10.697 190.888 20.556 40.803 80.387 20.626 90.417 90.556 20.585 30.702 10.600 71.000 10.824 10.720 110.692 11.000 10.509 5
PE0.645 61.000 10.773 100.798 50.538 60.786 130.088 140.799 30.350 140.435 120.547 70.545 40.646 60.933 100.562 90.761 70.556 160.997 100.501 7
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 71.000 10.758 130.582 160.539 50.826 30.046 170.765 40.372 120.436 110.588 20.539 50.650 21.000 10.577 70.750 90.653 50.997 100.495 8
PointGroup0.636 101.000 10.765 110.624 110.505 110.797 100.116 100.696 70.384 110.441 80.559 60.476 60.596 101.000 10.666 40.756 80.556 150.997 100.513 4
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]
HAIS0.699 11.000 10.849 20.820 30.675 10.808 70.279 30.757 50.465 60.517 30.596 10.559 30.600 71.000 10.654 50.767 40.676 20.994 130.560 3
Dyco3Dcopyleft0.641 81.000 10.841 30.893 10.531 70.802 90.115 110.588 140.448 70.438 90.537 80.430 100.550 140.857 120.534 100.764 60.657 30.987 140.568 2
Sparse R-CNN0.515 161.000 10.538 240.282 210.468 140.790 120.173 40.345 210.429 80.413 160.484 110.176 190.595 110.591 210.522 110.668 180.476 190.986 150.327 19
MASCpermissive0.447 210.528 260.555 220.381 190.382 170.633 170.002 250.509 180.260 170.361 170.432 150.327 160.451 160.571 220.367 200.639 190.386 200.980 160.276 21
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
PanopticFusion-inst0.478 190.667 210.712 180.595 120.259 220.550 230.000 270.613 110.175 210.250 240.434 140.437 90.411 210.857 120.485 120.591 230.267 260.944 170.359 17
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
UNet-backbone0.319 240.667 210.715 170.233 240.189 240.479 260.008 230.218 230.067 250.201 250.173 250.107 230.123 260.438 240.150 230.615 210.355 210.916 180.093 28
3D-BoNet0.488 181.000 10.672 200.590 130.301 200.484 250.098 120.620 100.306 160.341 190.259 220.125 220.434 190.796 180.402 160.499 250.513 180.909 190.439 12
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
Occipital-SCS0.512 171.000 10.716 160.509 170.506 100.611 180.092 130.602 130.177 200.346 180.383 190.165 200.442 180.850 160.386 190.618 200.543 170.889 200.389 15
PCJC0.578 131.000 10.810 70.583 150.449 150.813 60.042 180.603 120.341 150.490 40.465 120.410 110.650 20.835 170.264 220.694 150.561 130.889 200.504 6
3D-SISpermissive0.382 221.000 10.432 260.245 230.190 230.577 210.013 220.263 220.033 260.320 210.240 230.075 240.422 200.857 120.117 250.699 120.271 250.883 220.235 23
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
SALoss-ResNet0.459 201.000 10.737 150.159 270.259 210.587 200.138 80.475 200.217 180.416 150.408 170.128 210.315 230.714 190.411 150.536 240.590 100.873 230.304 20
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)
3D-BEVIS0.248 260.667 210.566 210.076 280.035 290.394 270.027 210.035 280.098 230.099 270.030 280.025 280.098 270.375 250.126 240.604 220.181 270.854 240.171 25
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
R-PointNet0.306 250.500 270.405 270.311 200.348 180.589 190.054 160.068 270.126 220.283 220.290 210.028 270.219 240.214 260.331 210.396 270.275 240.821 250.245 22
Region0.248 260.667 210.437 250.188 250.153 260.491 240.000 270.208 240.094 240.153 260.099 260.057 260.217 250.119 270.039 270.466 260.302 220.640 260.140 26
Hier3Dcopyleft0.323 230.667 210.542 230.264 220.157 250.550 220.000 270.205 250.009 270.270 230.218 240.075 240.500 150.688 200.007 290.698 130.301 230.459 270.200 24
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Sgpn_scannet0.143 280.208 290.390 280.169 260.065 270.275 280.029 200.069 260.000 280.087 280.043 270.014 290.027 290.000 280.112 260.351 280.168 280.438 280.138 27
MaskRCNN 2d->3d Proj0.058 290.333 280.002 290.000 290.053 280.002 290.002 260.021 290.000 280.045 290.024 290.238 170.065 280.000 280.014 280.107 290.020 290.110 290.006 29

This table lists the benchmark results for the 2D semantic label scenario.


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort by
Virtual MVFusion (R)0.745 10.861 10.839 10.881 10.672 10.512 10.422 100.898 10.723 10.714 10.954 20.454 10.509 10.773 10.895 10.756 10.820 10.653 10.935 10.891 10.728 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
SSMAcopyleft0.577 90.695 40.716 100.439 120.563 80.314 100.444 80.719 50.551 70.503 70.887 100.346 90.348 70.603 40.353 120.709 30.600 110.457 100.901 20.786 60.599 7
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
BPNet_2D0.670 20.822 30.795 30.836 20.659 20.481 20.451 60.769 20.656 30.567 30.931 30.395 30.390 40.700 20.534 30.689 50.770 20.574 30.865 30.831 30.675 2
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021
DCRedNet0.583 80.682 50.723 80.542 100.510 110.310 110.451 60.668 80.549 80.520 60.920 50.375 40.446 20.528 110.417 90.670 90.577 130.478 80.862 40.806 50.628 5
SN_RN152pyrx8_RVCcopyleft0.546 100.572 90.663 130.638 80.518 90.298 120.366 150.633 120.510 110.446 120.864 110.296 110.267 120.542 100.346 130.704 40.575 140.431 120.853 50.766 100.630 4
DMMF0.597 50.543 110.755 60.749 40.585 60.338 60.494 40.704 60.598 40.494 100.911 60.347 80.327 90.593 70.527 40.675 70.646 80.513 40.842 60.774 80.527 11
MCA-Net0.595 60.533 120.756 50.746 50.590 50.334 80.506 30.670 70.587 60.500 80.905 80.366 50.352 60.601 50.506 60.669 100.648 60.501 50.839 70.769 90.516 12
CU-Hybrid-2D Net0.636 30.825 20.820 20.179 160.648 30.463 30.549 10.742 30.676 20.628 20.961 10.420 20.379 50.684 30.381 100.732 20.723 30.599 20.827 80.851 20.634 3
ILC-PSPNet0.475 150.490 140.581 150.289 140.507 120.067 170.379 130.610 140.417 150.435 130.822 160.278 120.267 120.503 130.228 140.616 140.533 150.375 140.820 90.729 120.560 8
RFBNet0.592 70.616 70.758 40.659 60.581 70.330 90.469 50.655 100.543 90.524 50.924 40.355 70.336 80.572 80.479 80.671 80.648 60.480 70.814 100.814 40.614 6
AdapNet++copyleft0.503 120.613 80.722 90.418 130.358 170.337 70.370 140.479 150.443 130.368 150.907 70.207 140.213 160.464 150.525 50.618 130.657 50.450 110.788 110.721 140.408 16
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
DMMF_3d0.605 40.651 60.744 70.782 30.637 40.387 40.536 20.732 40.590 50.540 40.856 130.359 60.306 100.596 60.539 20.627 120.706 40.497 60.785 120.757 110.476 13
FuseNetpermissive0.535 110.570 100.681 120.182 150.512 100.290 130.431 90.659 90.504 120.495 90.903 90.308 100.428 30.523 120.365 110.676 60.621 100.470 90.762 130.779 70.541 9
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
3DMV (2d proj)0.498 130.481 150.612 140.579 90.456 130.343 50.384 120.623 130.525 100.381 140.845 140.254 130.264 140.557 90.182 150.581 150.598 120.429 130.760 140.661 160.446 15
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 140.505 130.709 110.092 170.427 140.241 140.411 110.654 110.385 170.457 110.861 120.053 170.279 110.503 130.481 70.645 110.626 90.365 150.748 150.725 130.529 10
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
Enet (reimpl)0.376 160.264 170.452 170.452 110.365 150.181 150.143 170.456 160.409 160.346 160.769 170.164 150.218 150.359 160.123 170.403 170.381 170.313 170.571 160.685 150.472 14
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)permissive0.330 170.293 160.521 160.657 70.361 160.161 160.250 160.004 170.440 140.183 170.836 150.125 160.060 170.319 170.132 160.417 160.412 160.344 160.541 170.427 170.109 17
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17

This table lists the benchmark results for the 2D semantic instance scenario.




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort by
UniDet_RVC0.205 10.381 10.323 10.037 10.226 10.177 10.063 10.277 10.120 10.067 10.131 10.074 20.317 10.080 10.235 10.289 10.141 10.678 10.080 1
MaskRCNN_ScanNetpermissive0.119 20.129 20.212 20.002 20.112 20.148 20.014 20.205 20.044 20.066 20.078 20.095 10.142 20.030 20.128 20.139 20.080 20.459 20.057 2
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17

This table lists the benchmark results for the scene type classification scenario.




Method Infoavg recallapartmentbathroombedroom / hotelbookstore / libraryconference roomcopy/mail roomhallwaykitchenlaundry roomliving room / loungemiscofficestorage / basement / garage
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
multi-taskpermissive0.700 10.500 11.000 10.882 20.500 11.000 11.000 10.500 11.000 11.000 10.778 10.000 20.938 10.000 1
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
SE-ResNeXt-SSMA0.498 20.000 30.812 20.941 10.500 10.500 20.500 20.500 10.429 30.500 20.667 20.500 10.625 20.000 1
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 30.250 20.812 20.529 30.500 10.500 20.000 30.500 10.571 20.000 30.556 30.000 20.375 30.000 1