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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
OccuSeg+Semantic0.764 10.758 230.796 60.839 30.746 20.907 10.562 10.850 40.680 20.672 10.978 10.610 10.335 20.777 10.819 110.847 10.830 10.691 30.972 10.885 10.727 2
BPNet0.749 20.909 10.818 30.811 70.752 10.839 50.485 110.842 60.673 30.644 30.957 20.528 50.305 70.773 20.859 30.788 20.818 30.693 20.916 50.856 40.723 4
Virtual MVFusion0.746 30.771 200.819 20.848 10.702 60.865 30.397 360.899 10.699 10.664 20.948 170.588 20.330 30.746 40.851 60.764 30.796 50.704 10.935 30.866 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
MinkowskiNetpermissive0.736 40.859 50.818 30.832 40.709 50.840 40.521 40.853 30.660 40.643 40.951 80.544 40.286 120.731 50.893 10.675 140.772 90.683 40.874 200.852 50.727 2
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
SparseConvNet0.725 50.647 390.821 10.846 20.721 40.869 20.533 20.754 150.603 130.614 50.955 30.572 30.325 40.710 60.870 20.724 60.823 20.628 80.934 40.865 30.683 7
MatchingNet0.724 60.812 140.812 50.810 80.735 30.834 60.495 90.860 20.572 190.602 80.954 40.512 70.280 130.757 30.845 90.725 50.780 70.606 150.937 20.851 60.700 6
RFCR0.702 70.889 20.745 160.813 60.672 80.818 140.493 100.815 70.623 80.610 60.947 200.470 150.249 210.594 170.848 70.705 90.779 80.646 70.892 120.823 90.611 18
JSENet0.699 80.881 30.762 120.821 50.667 90.800 210.522 30.792 90.613 90.607 70.935 360.492 100.205 290.576 230.853 50.691 100.758 120.652 60.872 230.828 80.649 11
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
CU-Hybrid Net0.693 90.596 440.789 70.803 100.677 70.800 210.469 150.846 50.554 270.591 100.948 170.500 80.316 60.609 150.847 80.732 40.808 40.593 170.894 100.839 70.652 10
FusionNet0.688 100.704 320.741 190.754 180.656 100.829 80.501 70.741 180.609 110.548 140.950 120.522 60.371 10.633 120.756 160.715 70.771 100.623 90.861 290.814 110.658 9
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
KP-FCNN0.684 110.847 70.758 150.784 120.647 130.814 150.473 130.772 110.605 120.594 90.935 360.450 230.181 370.587 180.805 130.690 110.785 60.614 110.882 150.819 100.632 14
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
SALANet0.670 120.816 130.770 110.768 150.652 120.807 180.451 190.747 160.659 50.545 150.924 420.473 140.149 470.571 250.811 120.635 250.746 150.623 90.892 120.794 230.570 32
PointConvpermissive0.666 130.781 160.759 140.699 250.644 140.822 120.475 120.779 100.564 230.504 270.953 50.428 310.203 310.586 200.754 170.661 180.753 130.588 180.902 70.813 130.642 12
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 130.703 330.781 80.751 200.655 110.830 70.471 140.769 120.474 410.537 160.951 80.475 130.279 140.635 100.698 270.675 140.751 140.553 300.816 380.806 140.703 5
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
DCM-Net0.658 150.778 170.702 280.806 90.619 160.813 160.468 160.693 280.494 370.524 210.941 300.449 240.298 80.510 320.821 100.675 140.727 190.568 230.826 350.803 160.637 13
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 160.698 340.743 180.650 370.564 320.820 130.505 60.758 140.631 70.479 340.945 240.480 110.226 240.572 240.774 150.690 110.735 180.614 110.853 310.776 340.597 25
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
RandLA-Netpermissive0.645 170.778 170.731 210.699 250.577 270.829 80.446 230.736 190.477 400.523 230.945 240.454 210.269 160.484 390.749 180.618 290.738 170.599 160.827 340.792 250.621 16
MVPNetpermissive0.641 180.831 90.715 240.671 340.590 230.781 310.394 370.679 310.642 60.553 130.937 340.462 180.256 170.649 70.406 440.626 260.691 300.666 50.877 160.792 250.608 21
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 180.776 190.703 270.721 210.557 340.826 100.451 190.672 330.563 240.483 330.943 280.425 330.162 410.644 80.726 190.659 190.709 220.572 200.875 180.786 290.559 37
PointMRNet0.640 200.717 310.701 290.692 280.576 280.801 190.467 170.716 240.563 240.459 370.953 50.429 300.169 390.581 210.854 40.605 310.710 210.550 310.894 100.793 240.575 30
FPConvpermissive0.639 210.785 150.760 130.713 240.603 190.798 230.392 380.534 460.603 130.524 210.948 170.457 190.250 200.538 280.723 200.598 350.696 280.614 110.872 230.799 180.567 34
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
VACNN++0.638 220.820 120.701 290.687 290.594 210.791 280.430 290.587 410.569 210.529 180.950 120.467 160.253 190.524 300.722 210.618 290.694 290.570 210.793 420.802 170.659 8
PointSPNet0.637 230.734 260.692 350.714 230.576 280.797 240.446 230.743 170.598 150.437 400.942 290.403 350.150 460.626 130.800 140.649 200.697 270.557 280.846 320.777 330.563 35
SConv0.636 240.830 100.697 330.752 190.572 310.780 320.445 250.716 240.529 300.530 170.951 80.446 250.170 380.507 340.666 290.636 240.682 320.541 350.886 140.799 180.594 26
Supervoxel-CNN0.635 250.656 370.711 250.719 220.613 170.757 370.444 270.765 130.534 290.566 110.928 400.478 120.272 150.636 90.531 350.664 170.645 390.508 400.864 280.792 250.611 18
joint point-basedpermissive0.634 260.614 420.778 90.667 360.633 150.825 110.420 300.804 80.467 430.561 120.951 80.494 90.291 90.566 260.458 390.579 390.764 110.559 270.838 330.814 110.598 24
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
MCCNNpermissive0.633 270.866 40.731 210.771 130.576 280.809 170.410 320.684 290.497 360.491 300.949 140.466 170.105 510.581 210.646 300.620 270.680 330.542 340.817 370.795 210.618 17
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
PointMTL0.632 280.731 270.688 370.675 320.591 220.784 300.444 270.565 440.610 100.492 290.949 140.456 200.254 180.587 180.706 240.599 340.665 360.612 140.868 270.791 280.579 29
3DSM_DMMF0.631 290.626 410.745 160.801 110.607 180.751 380.506 50.729 220.565 220.491 300.866 530.434 270.197 330.595 160.630 310.709 80.705 240.560 260.875 180.740 430.491 45
APCF-Net0.631 290.742 240.687 390.672 330.557 340.792 260.408 330.665 350.545 280.508 250.952 70.428 310.186 350.634 110.702 250.620 270.706 230.555 290.873 220.798 200.581 28
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 310.604 430.741 190.766 160.590 230.747 390.501 70.734 200.503 350.527 190.919 460.454 210.323 50.550 270.420 430.678 130.688 310.544 330.896 90.795 210.627 15
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
PointMRNet-lite0.625 320.643 400.711 250.697 270.581 260.801 190.408 330.670 340.558 260.497 280.944 260.436 260.152 450.617 140.708 230.603 320.743 160.532 380.870 260.784 300.545 38
SIConv0.625 320.830 100.694 340.757 170.563 330.772 340.448 210.647 380.520 320.509 240.949 140.431 290.191 340.496 370.614 320.647 220.672 340.535 370.876 170.783 310.571 31
HPEIN0.618 340.729 280.668 400.647 390.597 200.766 350.414 310.680 300.520 320.525 200.946 220.432 280.215 270.493 380.599 330.638 230.617 440.570 210.897 80.806 140.605 22
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-GCNpermissive0.610 350.858 60.772 100.489 500.532 360.792 260.404 350.643 390.570 200.507 260.935 360.414 340.046 560.510 320.702 250.602 330.705 240.549 320.859 300.773 350.534 40
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 360.760 220.667 410.649 380.521 370.793 250.457 180.648 370.528 310.434 420.947 200.401 360.153 440.454 400.721 220.648 210.717 200.536 360.904 60.765 370.485 46
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
LAP-D0.594 370.720 290.692 350.637 410.456 440.773 330.391 400.730 210.587 160.445 390.940 320.381 390.288 100.434 420.453 400.591 370.649 370.581 190.777 430.749 420.610 20
DPC0.592 380.720 290.700 310.602 440.480 410.762 360.380 420.713 260.585 170.437 400.940 320.369 410.288 100.434 420.509 370.590 380.639 420.567 240.772 440.755 400.592 27
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 390.766 210.659 440.683 310.470 430.740 410.387 410.620 400.490 380.476 350.922 440.355 440.245 220.511 310.511 360.571 400.643 400.493 430.872 230.762 380.600 23
SegGCNpermissive0.589 390.833 80.731 210.539 480.514 380.789 290.448 210.467 470.573 180.484 320.936 350.396 370.061 550.501 350.507 380.594 360.700 260.563 250.874 200.771 360.493 44
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
TextureNetpermissive0.566 410.672 360.664 420.671 340.494 390.719 420.445 250.678 320.411 480.396 430.935 360.356 430.225 250.412 440.535 340.565 410.636 430.464 450.794 410.680 480.568 33
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
DVVNet0.562 420.648 380.700 310.770 140.586 250.687 460.333 440.650 360.514 340.475 360.906 500.359 420.223 260.340 470.442 420.422 500.668 350.501 410.708 480.779 320.534 40
Pointnet++ & Featurepermissive0.557 430.735 250.661 430.686 300.491 400.744 400.392 380.539 450.451 440.375 460.946 220.376 400.205 290.403 450.356 460.553 420.643 400.497 420.824 360.756 390.515 42
PanopticFusion-label0.529 440.491 510.688 370.604 430.386 470.632 510.225 550.705 270.434 460.293 500.815 540.348 450.241 230.499 360.669 280.507 430.649 370.442 490.796 400.602 540.561 36
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
3DMV, FTSDF0.501 450.558 480.608 500.424 550.478 420.690 450.246 510.586 420.468 420.450 380.911 480.394 380.160 420.438 410.212 520.432 490.541 500.475 440.742 460.727 440.477 47
PCNN0.498 460.559 470.644 470.560 470.420 460.711 440.229 530.414 480.436 450.352 470.941 300.324 460.155 430.238 520.387 450.493 440.529 510.509 390.813 390.751 410.504 43
3DMV0.484 470.484 520.538 530.643 400.424 450.606 540.310 450.574 430.433 470.378 450.796 550.301 470.214 280.537 290.208 530.472 480.507 540.413 520.693 490.602 540.539 39
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 480.577 460.611 490.356 570.321 530.715 430.299 470.376 510.328 540.319 480.944 260.285 490.164 400.216 550.229 510.484 460.545 490.456 470.755 450.709 450.475 48
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 490.679 350.604 510.578 460.380 480.682 470.291 480.106 570.483 390.258 550.920 450.258 510.025 570.231 540.325 470.480 470.560 480.463 460.725 470.666 500.231 57
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
PNET20.442 500.548 490.548 520.597 450.363 500.628 520.300 460.292 520.374 510.307 490.881 520.268 500.186 350.238 520.204 540.407 510.506 550.449 480.667 500.620 520.462 49
SurfaceConvPF0.442 500.505 500.622 480.380 560.342 520.654 490.227 540.397 500.367 520.276 520.924 420.240 530.198 320.359 460.262 490.366 520.581 460.435 500.640 510.668 490.398 50
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 520.437 550.646 460.474 520.369 490.645 500.353 430.258 540.282 560.279 510.918 470.298 480.147 480.283 490.294 480.487 450.562 470.427 510.619 520.633 510.352 52
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
subcloud_weak0.411 530.479 530.650 450.475 510.285 560.519 570.087 580.725 230.396 500.386 440.621 580.250 520.117 490.338 480.443 410.188 580.594 450.369 550.377 580.616 530.306 53
SPLAT Netcopyleft0.393 540.472 540.511 540.606 420.311 540.656 480.245 520.405 490.328 540.197 560.927 410.227 550.000 590.001 590.249 500.271 570.510 520.383 540.593 530.699 460.267 55
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+FTSDF0.383 550.297 570.491 550.432 540.358 510.612 530.274 490.116 560.411 480.265 530.904 510.229 540.079 530.250 500.185 550.320 550.510 520.385 530.548 540.597 560.394 51
PointNet++permissive0.339 560.584 450.478 560.458 530.256 570.360 580.250 500.247 550.278 570.261 540.677 570.183 560.117 490.212 560.145 570.364 530.346 580.232 580.548 540.523 570.252 56
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 570.353 560.290 580.278 580.166 580.553 550.169 570.286 530.147 580.148 580.908 490.182 570.064 540.023 580.018 590.354 540.363 560.345 560.546 560.685 470.278 54
ScanNetpermissive0.306 580.203 580.366 570.501 490.311 540.524 560.211 560.002 590.342 530.189 570.786 560.145 580.102 520.245 510.152 560.318 560.348 570.300 570.460 570.437 580.182 58
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 590.000 590.041 590.172 590.030 590.062 590.001 590.035 580.004 590.051 590.143 590.019 590.003 580.041 570.050 580.003 590.054 590.018 590.005 590.264 590.082 59

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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CRNet0.688 11.000 10.802 60.783 30.644 10.804 40.182 20.874 10.511 40.501 20.592 10.536 30.587 81.000 10.637 40.730 70.683 10.994 90.523 2
OccuSeg+instance0.672 21.000 10.758 100.682 60.576 30.842 10.477 10.504 140.524 30.567 10.585 20.451 50.557 91.000 10.751 10.797 30.563 71.000 10.467 7
Mask-Group0.664 31.000 10.822 30.764 50.616 20.815 20.139 50.694 60.597 20.459 50.566 30.599 10.600 50.516 170.715 20.819 20.635 31.000 10.603 1
PE0.645 41.000 10.773 70.798 20.538 40.786 80.088 110.799 30.350 90.435 90.547 50.545 20.646 40.933 60.562 60.761 50.556 110.997 70.501 5
GICN0.638 51.000 10.895 10.800 10.480 80.676 100.144 40.737 40.354 80.447 60.400 130.365 90.700 11.000 10.569 50.836 10.599 41.000 10.473 6
PointGroup0.636 61.000 10.765 80.624 70.505 70.797 50.116 80.696 50.384 70.441 70.559 40.476 40.596 61.000 10.666 30.756 60.556 100.997 70.513 3
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]
3D-MPA0.611 71.000 10.833 20.765 40.526 50.756 90.136 70.588 110.470 50.438 80.432 110.358 100.650 20.857 80.429 100.765 40.557 91.000 10.430 9
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
PCJC0.578 81.000 10.810 40.583 110.449 110.813 30.042 140.603 90.341 100.490 30.465 70.410 70.650 20.835 120.264 170.694 90.561 80.889 150.504 4
SSEN0.575 91.000 10.761 90.473 130.477 90.795 60.066 120.529 120.658 10.460 40.461 80.380 80.331 160.859 70.401 130.692 100.653 21.000 10.348 13
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
MTML0.549 101.000 10.807 50.588 100.327 140.647 110.004 200.815 20.180 140.418 100.364 150.182 130.445 111.000 10.442 90.688 110.571 61.000 10.396 10
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Sparse R-CNN0.515 111.000 10.538 180.282 170.468 100.790 70.173 30.345 160.429 60.413 120.484 60.176 140.595 70.591 150.522 70.668 120.476 140.986 100.327 14
Occipital-SCS0.512 121.000 10.716 120.509 120.506 60.611 130.092 100.602 100.177 150.346 140.383 140.165 150.442 120.850 110.386 140.618 140.543 120.889 150.389 11
3D-BoNet0.488 131.000 10.672 150.590 90.301 150.484 190.098 90.620 70.306 110.341 150.259 170.125 170.434 130.796 130.402 120.499 190.513 130.909 140.439 8
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst0.478 140.667 160.712 140.595 80.259 170.550 170.000 230.613 80.175 160.250 180.434 90.437 60.411 150.857 80.485 80.591 170.267 210.944 120.359 12
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SALoss-ResNet0.459 151.000 10.737 110.159 220.259 160.587 150.138 60.475 150.217 130.416 110.408 120.128 160.315 170.714 140.411 110.536 180.590 50.873 180.304 15
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)
MASCpermissive0.447 160.528 200.555 170.381 140.382 120.633 120.002 210.509 130.260 120.361 130.432 100.327 110.451 100.571 160.367 150.639 130.386 150.980 110.276 16
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.382 171.000 10.432 200.245 180.190 180.577 160.013 180.263 180.033 220.320 160.240 180.075 190.422 140.857 80.117 200.699 80.271 200.883 170.235 18
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
UNet-backbone0.319 180.667 160.715 130.233 190.189 190.479 200.008 190.218 190.067 210.201 190.173 190.107 180.123 200.438 180.150 180.615 150.355 160.916 130.093 23
R-PointNet0.306 190.500 210.405 210.311 150.348 130.589 140.054 130.068 220.126 170.283 170.290 160.028 210.219 180.214 210.331 160.396 210.275 190.821 200.245 17
Region0.248 200.667 160.437 190.188 200.153 200.491 180.000 230.208 200.094 190.153 200.099 210.057 200.217 190.119 220.039 230.466 200.302 180.640 210.140 20
3D-BEVIS0.248 200.667 160.566 160.076 230.035 240.394 210.027 160.035 230.098 180.099 220.030 230.025 220.098 210.375 190.126 190.604 160.181 220.854 190.171 19
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Seg-Clusterpermissive0.215 220.370 220.337 230.285 160.105 210.325 220.025 170.282 170.085 200.105 210.107 200.007 240.079 220.317 200.114 210.309 230.304 170.587 220.123 22
Sgpn_scannet0.143 230.208 240.390 220.169 210.065 220.275 230.029 150.069 210.000 230.087 230.043 220.014 230.027 240.000 230.112 220.351 220.168 230.438 230.138 21
MaskRCNN 2d->3d Proj0.058 240.333 230.002 240.000 240.053 230.002 240.002 220.021 240.000 230.045 240.024 240.238 120.065 230.000 230.014 240.107 240.020 240.110 240.006 24

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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
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
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
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
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
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
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
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
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
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
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
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
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
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
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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