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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
OccuSeg+Semantic0.764 10.758 220.796 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
MatchingNet0.724 60.812 140.812 50.810 80.735 30.834 60.495 90.860 20.572 180.602 80.954 40.512 70.280 130.757 30.845 90.725 50.780 70.606 150.937 20.851 60.700 6
SparseConvNet0.725 50.647 370.821 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
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 40.874 190.852 50.727 2
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
Virtual MVFusion0.746 30.771 180.819 20.848 10.702 60.865 30.397 340.899 10.699 10.664 20.948 180.588 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
CU-Hybrid Net0.693 90.596 420.789 70.803 100.677 70.800 190.469 150.846 50.554 260.591 100.948 180.500 80.316 60.609 140.847 80.732 40.808 40.593 160.894 100.839 70.652 10
RFCR0.702 70.889 20.745 160.813 60.672 80.818 120.493 100.815 70.623 80.610 60.947 210.470 150.249 200.594 160.848 70.705 90.779 80.646 70.892 120.823 90.611 17
JSENet0.699 80.881 30.762 120.821 50.667 90.800 190.522 30.792 90.613 90.607 70.935 340.492 100.205 280.576 220.853 50.691 100.758 120.652 60.872 220.828 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
FusionNet0.688 100.704 300.741 190.754 180.656 100.829 80.501 70.741 170.609 110.548 140.950 120.522 60.371 10.633 110.756 150.715 70.771 100.623 90.861 280.814 110.658 9
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
PointASNLpermissive0.666 130.703 310.781 80.751 200.655 110.830 70.471 140.769 120.474 390.537 160.951 80.475 130.279 140.635 90.698 240.675 140.751 140.553 270.816 360.806 140.703 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
SALANet0.670 120.816 130.770 110.768 150.652 120.807 160.451 190.747 160.659 50.545 150.924 400.473 140.149 440.571 240.811 120.635 240.746 150.623 90.892 120.794 230.570 31
KP-FCNN0.684 110.847 70.758 150.784 120.647 130.814 130.473 130.772 110.605 120.594 90.935 340.450 220.181 360.587 170.805 130.690 110.785 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
PointConvpermissive0.666 130.781 160.759 140.699 230.644 140.822 100.475 120.779 100.564 220.504 260.953 50.428 300.203 300.586 190.754 160.661 180.753 130.588 170.902 70.813 130.642 12
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
joint point-basedpermissive0.634 230.614 400.778 90.667 340.633 150.825 90.420 280.804 80.467 410.561 120.951 80.494 90.291 90.566 250.458 370.579 370.764 110.559 250.838 320.814 110.598 23
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
DCM-Net0.658 150.778 170.702 260.806 90.619 160.813 140.468 160.693 260.494 360.524 210.941 280.449 230.298 80.510 310.821 100.675 140.727 190.568 210.826 330.803 160.637 13
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
Supervoxel-CNN0.635 220.656 350.711 240.719 210.613 170.757 350.444 240.765 130.534 280.566 110.928 380.478 120.272 150.636 80.531 330.664 170.645 370.508 380.864 270.792 250.611 17
3DSM_DMMF0.631 260.626 390.745 160.801 110.607 180.751 360.506 50.729 200.565 210.491 290.866 510.434 260.197 320.595 150.630 290.709 80.705 230.560 240.875 180.740 410.491 43
FPConvpermissive0.639 190.785 150.760 130.713 220.603 190.798 210.392 360.534 440.603 130.524 210.948 180.457 190.250 190.538 270.723 170.598 330.696 260.614 110.872 220.799 180.567 33
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
HPEIN0.618 320.729 260.668 380.647 370.597 200.766 330.414 290.680 280.520 310.525 200.946 230.432 270.215 260.493 370.599 310.638 220.617 420.570 190.897 80.806 140.605 21
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
VACNN++0.638 200.820 120.701 270.687 270.594 210.791 260.430 270.587 390.569 200.529 180.950 120.467 160.253 180.524 290.722 180.618 280.694 270.570 190.793 400.802 170.659 8
PointMTL0.632 250.731 250.688 340.675 300.591 220.784 280.444 240.565 420.610 100.492 280.949 150.456 200.254 170.587 170.706 210.599 320.665 340.612 140.868 260.791 270.579 28
FusionAwareConv0.630 280.604 410.741 190.766 160.590 230.747 370.501 70.734 180.503 340.527 190.919 440.454 210.323 50.550 260.420 410.678 130.688 290.544 300.896 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
MVPNetpermissive0.641 170.831 90.715 230.671 320.590 230.781 290.394 350.679 290.642 60.553 130.937 320.462 180.256 160.649 70.406 420.626 250.691 280.666 50.877 160.792 250.608 20
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
DVVNet0.562 400.648 360.700 290.770 140.586 250.687 440.333 420.650 340.514 330.475 340.906 480.359 400.223 250.340 450.442 400.422 480.668 330.501 390.708 460.779 300.534 38
PointMRNet-lite0.625 290.643 380.711 240.697 250.581 260.801 170.408 310.670 310.558 250.497 270.944 260.436 250.152 430.617 120.708 200.603 300.743 160.532 360.870 250.784 280.545 35
MCCNNpermissive0.633 240.866 40.731 210.771 130.576 270.809 150.410 300.684 270.497 350.491 290.949 150.466 170.105 490.581 200.646 280.620 260.680 310.542 320.817 350.795 210.618 16
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
PointMRNet0.640 180.717 290.701 270.692 260.576 270.801 170.467 170.716 220.563 230.459 350.953 50.429 290.169 380.581 200.854 40.605 290.710 210.550 280.894 100.793 240.575 29
SConv0.636 210.830 100.697 310.752 190.572 290.780 300.445 220.716 220.529 290.530 170.951 80.446 240.170 370.507 330.666 270.636 230.682 300.541 330.886 140.799 180.594 25
HPGCNN0.656 160.698 320.743 180.650 350.564 300.820 110.505 60.758 140.631 70.479 320.945 250.480 110.226 230.572 230.774 140.690 110.735 170.614 110.853 310.776 320.597 24
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SIConv0.625 290.830 100.694 320.757 170.563 310.772 320.448 200.647 360.520 310.509 230.949 150.431 280.191 330.496 360.614 300.647 210.672 320.535 350.876 170.783 290.571 30
APCF-Net0.631 260.742 230.687 360.672 310.557 320.792 240.408 310.665 320.545 270.508 240.952 70.428 300.186 340.634 100.702 220.620 260.706 220.555 260.873 210.798 200.581 27
PointSPNet0.625 290.770 190.684 370.698 240.552 330.793 220.434 260.662 330.560 240.457 360.950 120.416 320.130 460.610 130.682 250.648 190.734 180.544 300.857 300.779 300.541 36
SPH3D-GCNpermissive0.610 330.858 60.772 100.489 480.532 340.792 240.404 330.643 370.570 190.507 250.935 340.414 330.046 540.510 310.702 220.602 310.705 230.549 290.859 290.773 330.534 38
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 340.760 210.667 390.649 360.521 350.793 220.457 180.648 350.528 300.434 400.947 210.401 340.153 420.454 380.721 190.648 190.717 200.536 340.904 60.765 350.485 44
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
SegGCNpermissive0.589 370.833 80.731 210.539 460.514 360.789 270.448 200.467 450.573 170.484 310.936 330.396 350.061 530.501 340.507 360.594 340.700 250.563 230.874 190.771 340.493 42
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
TextureNetpermissive0.566 390.672 340.664 400.671 320.494 370.719 400.445 220.678 300.411 460.396 410.935 340.356 410.225 240.412 420.535 320.565 390.636 410.464 430.794 390.680 460.568 32
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
Pointnet++ & Featurepermissive0.557 410.735 240.661 410.686 280.491 380.744 380.392 360.539 430.451 420.375 440.946 230.376 380.205 280.403 430.356 440.553 400.643 380.497 400.824 340.756 370.515 40
DPC0.592 360.720 270.700 290.602 420.480 390.762 340.380 400.713 240.585 160.437 390.940 300.369 390.288 100.434 400.509 350.590 360.639 400.567 220.772 420.755 380.592 26
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
3DMV, FTSDF0.501 430.558 460.608 480.424 530.478 400.690 430.246 490.586 400.468 400.450 370.911 460.394 360.160 400.438 390.212 500.432 470.541 480.475 420.742 440.727 420.477 45
CCRFNet0.589 370.766 200.659 420.683 290.470 410.740 390.387 390.620 380.490 370.476 330.922 420.355 420.245 210.511 300.511 340.571 380.643 380.493 410.872 220.762 360.600 22
LAP-D0.594 350.720 270.692 330.637 390.456 420.773 310.391 380.730 190.587 150.445 380.940 300.381 370.288 100.434 400.453 380.591 350.649 350.581 180.777 410.749 400.610 19
3DMV0.484 450.484 500.538 510.643 380.424 430.606 520.310 430.574 410.433 450.378 430.796 530.301 450.214 270.537 280.208 510.472 460.507 520.413 500.693 470.602 520.539 37
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PCNN0.498 440.559 450.644 450.560 450.420 440.711 420.229 510.414 460.436 430.352 450.941 280.324 440.155 410.238 500.387 430.493 420.529 490.509 370.813 370.751 390.504 41
PanopticFusion-label0.529 420.491 490.688 340.604 410.386 450.632 490.225 530.705 250.434 440.293 480.815 520.348 430.241 220.499 350.669 260.507 410.649 350.442 470.796 380.602 520.561 34
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
FCPNpermissive0.447 470.679 330.604 490.578 440.380 460.682 450.291 460.106 550.483 380.258 530.920 430.258 490.025 550.231 520.325 450.480 450.560 460.463 440.725 450.666 480.231 55
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
Tangent Convolutionspermissive0.438 500.437 530.646 440.474 500.369 470.645 480.353 410.258 520.282 540.279 490.918 450.298 460.147 450.283 470.294 460.487 430.562 450.427 490.619 500.633 490.352 50
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
PNET20.442 480.548 470.548 500.597 430.363 480.628 500.300 440.292 500.374 490.307 470.881 500.268 480.186 340.238 500.204 520.407 490.506 530.449 460.667 480.620 500.462 47
ScanNet+FTSDF0.383 530.297 550.491 530.432 520.358 490.612 510.274 470.116 540.411 460.265 510.904 490.229 520.079 510.250 480.185 530.320 530.510 500.385 510.548 520.597 540.394 49
SurfaceConvPF0.442 480.505 480.622 460.380 540.342 500.654 470.227 520.397 480.367 500.276 500.924 400.240 510.198 310.359 440.262 470.366 500.581 440.435 480.640 490.668 470.398 48
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PointCNN with RGBpermissive0.458 460.577 440.611 470.356 550.321 510.715 410.299 450.376 490.328 520.319 460.944 260.285 470.164 390.216 530.229 490.484 440.545 470.456 450.755 430.709 430.475 46
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
SPLAT Netcopyleft0.393 520.472 520.511 520.606 400.311 520.656 460.245 500.405 470.328 520.197 540.927 390.227 530.000 570.001 570.249 480.271 550.510 500.383 520.593 510.699 440.267 53
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
ScanNetpermissive0.306 560.203 560.366 550.501 470.311 520.524 540.211 540.002 570.342 510.189 550.786 540.145 560.102 500.245 490.152 540.318 540.348 550.300 550.460 550.437 560.182 56
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
subcloud_weak0.411 510.479 510.650 430.475 490.285 540.519 550.087 560.725 210.396 480.386 420.621 560.250 500.117 470.338 460.443 390.188 560.594 430.369 530.377 560.616 510.306 51
PointNet++permissive0.339 540.584 430.478 540.458 510.256 550.360 560.250 480.247 530.278 550.261 520.677 550.183 540.117 470.212 540.145 550.364 510.346 560.232 560.548 520.523 550.252 54
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 550.353 540.290 560.278 560.166 560.553 530.169 550.286 510.147 560.148 560.908 470.182 550.064 520.023 560.018 570.354 520.363 540.345 540.546 540.685 450.278 52
ERROR0.054 570.000 570.041 570.172 570.030 570.062 570.001 570.035 560.004 570.051 570.143 570.019 570.003 560.041 550.050 560.003 570.054 570.018 570.005 570.264 570.082 57

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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysorted 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
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
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
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
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
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
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]
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
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
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
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
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.
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
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]
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
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)
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)
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
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
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
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.

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysorted 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
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
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
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
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
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
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
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
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

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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysorted 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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
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
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