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 10.758 180.796 60.839 30.746 20.907 10.562 10.850 40.680 20.672 10.978 10.610 10.335 20.777 10.819 100.847 10.830 10.691 30.972 10.885 10.727 2
BPNet0.749 20.909 10.818 30.811 60.752 10.839 50.485 100.842 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 30.771 150.819 20.848 10.702 60.865 30.397 290.899 10.699 10.664 20.948 160.588 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 40.818 30.832 40.709 50.840 40.521 40.853 30.660 40.643 40.951 80.544 40.286 120.731 50.893 10.675 130.772 80.683 40.874 170.852 50.727 2
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
SparseConvNet0.725 50.647 320.821 10.846 20.721 40.869 20.533 20.754 120.603 110.614 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 60.812 110.812 50.810 70.735 30.834 60.495 80.860 20.572 160.602 70.954 40.512 70.280 130.757 30.845 80.725 50.780 70.606 130.937 20.851 60.700 6
JSENet0.699 70.881 20.762 110.821 50.667 80.800 170.522 30.792 80.613 70.607 60.935 310.492 110.205 250.576 190.853 50.691 90.758 110.652 70.872 200.828 80.649 10
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 80.596 370.789 70.803 90.677 70.800 170.469 140.846 50.554 220.591 90.948 160.500 90.316 60.609 120.847 70.732 40.808 40.593 140.894 100.839 70.652 9
FusionNet0.688 90.704 260.741 170.754 160.656 90.829 80.501 60.741 130.609 90.548 120.950 120.522 60.371 10.633 100.756 130.715 70.771 90.623 90.861 250.814 100.658 8
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
KP-FCNN0.684 100.847 60.758 140.784 110.647 110.814 120.473 120.772 100.605 100.594 80.935 310.450 180.181 330.587 140.805 110.690 110.785 60.614 100.882 130.819 90.632 13
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
PointASNLpermissive0.666 110.703 270.781 80.751 180.655 100.830 70.471 130.769 110.474 340.537 130.951 80.475 120.279 140.635 80.698 210.675 130.751 140.553 240.816 320.806 130.703 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
PointConvpermissive0.666 110.781 130.759 130.699 200.644 120.822 110.475 110.779 90.564 190.504 220.953 50.428 260.203 270.586 160.754 140.661 160.753 130.588 150.902 70.813 120.642 11
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
HPGCNN0.661 130.689 280.749 150.689 230.595 180.828 90.490 90.695 220.659 50.498 230.946 200.503 80.247 180.531 240.769 120.691 90.758 110.653 60.860 260.787 240.578 25
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
DCM-Net0.658 140.778 140.702 230.806 80.619 140.813 130.468 150.693 230.494 310.524 170.941 250.449 190.298 80.510 260.821 90.675 130.727 160.568 180.826 290.803 150.637 12
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MVPNetpermissive0.641 150.831 80.715 210.671 280.590 200.781 250.394 300.679 260.642 60.553 110.937 290.462 140.256 150.649 70.406 370.626 210.691 240.666 50.877 140.792 220.608 17
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 160.717 250.701 240.692 220.576 240.801 150.467 160.716 180.563 200.459 310.953 50.429 250.169 350.581 170.854 40.605 240.710 180.550 250.894 100.793 210.575 26
FPConvpermissive0.639 170.785 120.760 120.713 190.603 160.798 190.392 310.534 390.603 110.524 170.948 160.457 150.250 170.538 220.723 150.598 280.696 230.614 100.872 200.799 160.567 29
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
SConv0.636 180.830 90.697 270.752 170.572 260.780 260.445 200.716 180.529 240.530 140.951 80.446 200.170 340.507 280.666 230.636 200.682 260.541 290.886 120.799 160.594 21
joint point-basedpermissive0.634 190.614 350.778 90.667 300.633 130.825 100.420 230.804 70.467 360.561 100.951 80.494 100.291 90.566 200.458 320.579 320.764 100.559 220.838 280.814 100.598 20
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
MCCNNpermissive0.633 200.866 30.731 190.771 120.576 240.809 140.410 250.684 240.497 300.491 260.949 130.466 130.105 440.581 170.646 240.620 220.680 270.542 280.817 310.795 190.618 15
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
PointMTL0.632 210.731 210.688 300.675 260.591 190.784 240.444 220.565 370.610 80.492 250.949 130.456 160.254 160.587 140.706 180.599 270.665 300.612 120.868 240.791 230.579 24
3DSM_DMMF0.631 220.626 340.745 160.801 100.607 150.751 310.506 50.729 160.565 180.491 260.866 460.434 220.197 290.595 130.630 250.709 80.705 200.560 210.875 160.740 360.491 38
APCF-Net0.631 220.742 190.687 320.672 270.557 280.792 210.408 260.665 290.545 230.508 200.952 70.428 260.186 310.634 90.702 190.620 220.706 190.555 230.873 190.798 180.581 23
FusionAwareConv0.630 240.604 360.741 170.766 140.590 200.747 320.501 60.734 140.503 290.527 150.919 390.454 170.323 50.550 210.420 360.678 120.688 250.544 270.896 90.795 190.627 14
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
SIConv0.625 250.830 90.694 280.757 150.563 270.772 280.448 180.647 320.520 260.509 190.949 130.431 240.191 300.496 310.614 260.647 180.672 280.535 310.876 150.783 260.571 27
PointMRNet-lite0.625 250.643 330.711 220.697 210.581 230.801 150.408 260.670 280.558 210.497 240.944 230.436 210.152 400.617 110.708 170.603 250.743 150.532 320.870 230.784 250.545 31
HPEIN0.618 270.729 220.668 330.647 320.597 170.766 290.414 240.680 250.520 260.525 160.946 200.432 230.215 230.493 320.599 270.638 190.617 370.570 170.897 80.806 130.605 18
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-GCNpermissive0.610 280.858 50.772 100.489 430.532 290.792 210.404 280.643 330.570 170.507 210.935 310.414 280.046 490.510 260.702 190.602 260.705 200.549 260.859 270.773 280.534 33
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 290.760 170.667 340.649 310.521 300.793 200.457 170.648 310.528 250.434 350.947 190.401 290.153 390.454 330.721 160.648 170.717 170.536 300.904 60.765 300.485 39
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
LAP-D0.594 300.720 230.692 290.637 340.456 370.773 270.391 330.730 150.587 130.445 330.940 270.381 320.288 100.434 350.453 330.591 300.649 310.581 160.777 360.749 350.610 16
DPC0.592 310.720 230.700 250.602 370.480 340.762 300.380 350.713 200.585 140.437 340.940 270.369 340.288 100.434 350.509 300.590 310.639 350.567 190.772 370.755 330.592 22
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 320.766 160.659 370.683 250.470 360.740 340.387 340.620 340.490 320.476 290.922 370.355 370.245 190.511 250.511 290.571 330.643 330.493 360.872 200.762 310.600 19
SegGCNpermissive0.589 320.833 70.731 190.539 410.514 310.789 230.448 180.467 400.573 150.484 280.936 300.396 300.061 480.501 290.507 310.594 290.700 220.563 200.874 170.771 290.493 37
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
TextureNetpermissive0.566 340.672 300.664 350.671 280.494 320.719 350.445 200.678 270.411 410.396 360.935 310.356 360.225 210.412 370.535 280.565 340.636 360.464 380.794 350.680 410.568 28
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
DVVNet0.562 350.648 310.700 250.770 130.586 220.687 390.333 370.650 300.514 280.475 300.906 430.359 350.223 220.340 400.442 350.422 430.668 290.501 340.708 410.779 270.534 33
Pointnet++ & Featurepermissive0.557 360.735 200.661 360.686 240.491 330.744 330.392 310.539 380.451 370.375 390.946 200.376 330.205 250.403 380.356 390.553 350.643 330.497 350.824 300.756 320.515 35
PanopticFusion-label0.529 370.491 440.688 300.604 360.386 400.632 440.225 480.705 210.434 390.293 430.815 470.348 380.241 200.499 300.669 220.507 360.649 310.442 420.796 340.602 470.561 30
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, FTSDF0.501 380.558 410.608 430.424 480.478 350.690 380.246 440.586 350.468 350.450 320.911 410.394 310.160 370.438 340.212 450.432 420.541 430.475 370.742 390.727 370.477 40
PCNN0.498 390.559 400.644 400.560 400.420 390.711 370.229 460.414 410.436 380.352 400.941 250.324 390.155 380.238 450.387 380.493 370.529 440.509 330.813 330.751 340.504 36
3DMV0.484 400.484 450.538 460.643 330.424 380.606 470.310 380.574 360.433 400.378 380.796 480.301 400.214 240.537 230.208 460.472 410.507 470.413 450.693 420.602 470.539 32
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 410.577 390.611 420.356 500.321 460.715 360.299 400.376 440.328 470.319 410.944 230.285 420.164 360.216 480.229 440.484 390.545 420.456 400.755 380.709 380.475 41
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 420.679 290.604 440.578 390.380 410.682 400.291 410.106 500.483 330.258 480.920 380.258 440.025 500.231 470.325 400.480 400.560 410.463 390.725 400.666 430.231 50
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
PNET20.442 430.548 420.548 450.597 380.363 430.628 450.300 390.292 450.374 440.307 420.881 450.268 430.186 310.238 450.204 470.407 440.506 480.449 410.667 430.620 450.462 42
SurfaceConvPF0.442 430.505 430.622 410.380 490.342 450.654 420.227 470.397 430.367 450.276 450.924 360.240 460.198 280.359 390.262 420.366 450.581 390.435 430.640 440.668 420.398 43
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 450.437 480.646 390.474 450.369 420.645 430.353 360.258 470.282 490.279 440.918 400.298 410.147 410.283 420.294 410.487 380.562 400.427 440.619 450.633 440.352 45
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
subcloud_weak0.411 460.479 460.650 380.475 440.285 490.519 500.087 510.725 170.396 430.386 370.621 510.250 450.117 420.338 410.443 340.188 510.594 380.369 480.377 510.616 460.306 46
SPLAT Netcopyleft0.393 470.472 470.511 470.606 350.311 470.656 410.245 450.405 420.328 470.197 490.927 350.227 480.000 520.001 520.249 430.271 500.510 450.383 470.593 460.699 390.267 48
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+FTSDF0.383 480.297 500.491 480.432 470.358 440.612 460.274 420.116 490.411 410.265 460.904 440.229 470.079 460.250 430.185 480.320 480.510 450.385 460.548 470.597 490.394 44
PointNet++permissive0.339 490.584 380.478 490.458 460.256 500.360 510.250 430.247 480.278 500.261 470.677 500.183 490.117 420.212 490.145 500.364 460.346 510.232 510.548 470.523 500.252 49
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 500.353 490.290 510.278 510.166 510.553 480.169 500.286 460.147 510.148 510.908 420.182 500.064 470.023 510.018 520.354 470.363 490.345 490.546 490.685 400.278 47
ScanNetpermissive0.306 510.203 510.366 500.501 420.311 470.524 490.211 490.002 520.342 460.189 500.786 490.145 510.102 450.245 440.152 490.318 490.348 500.300 500.460 500.437 510.182 51
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 520.000 520.041 520.172 520.030 520.062 520.001 520.035 510.004 520.051 520.143 520.019 520.003 510.041 500.050 510.003 520.054 520.018 520.005 520.264 520.082 52

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
OccuSeg+instance0.672 11.000 10.758 70.682 30.576 20.842 10.477 10.504 120.524 20.567 10.585 10.451 20.557 61.000 10.751 10.797 20.563 61.000 10.467 4
GICN0.638 21.000 10.895 10.800 10.480 60.676 80.144 40.737 20.354 70.447 50.400 110.365 60.700 11.000 10.569 30.836 10.599 31.000 10.473 3
PointGroup0.636 31.000 10.765 50.624 50.505 50.797 40.116 70.696 30.384 60.441 60.559 20.476 10.596 41.000 10.666 20.756 50.556 90.997 60.513 1
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 41.000 10.833 20.765 20.526 30.756 70.136 60.588 80.470 40.438 70.432 90.358 70.650 20.857 60.429 80.765 40.557 81.000 10.430 6
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
PCJC0.578 51.000 10.810 30.583 90.449 90.813 20.042 120.603 60.341 80.490 30.465 40.410 40.650 20.835 100.264 150.694 70.561 70.889 130.504 2
SSEN0.575 61.000 10.761 60.473 110.477 70.795 50.066 100.529 90.658 10.460 40.461 50.380 50.331 140.859 50.401 110.692 80.653 11.000 10.348 10
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
PE0.560 70.667 130.722 90.673 40.577 10.806 30.202 20.520 100.524 30.491 20.450 60.061 170.553 70.688 130.507 50.777 30.604 20.941 100.322 12
MTML0.549 81.000 10.807 40.588 80.327 120.647 90.004 180.815 10.180 120.418 80.364 130.182 100.445 91.000 10.442 70.688 90.571 51.000 10.396 7
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Sparse R-CNN0.515 91.000 10.538 160.282 150.468 80.790 60.173 30.345 140.429 50.413 100.484 30.176 110.595 50.591 140.522 40.668 100.476 120.986 70.327 11
Occipital-SCS0.512 101.000 10.716 100.509 100.506 40.611 110.092 90.602 70.177 130.346 120.383 120.165 120.442 100.850 90.386 120.618 120.543 100.889 130.389 8
3D-BoNet0.488 111.000 10.672 130.590 70.301 130.484 170.098 80.620 40.306 90.341 130.259 150.125 140.434 110.796 110.402 100.499 170.513 110.909 120.439 5
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-inst0.478 120.667 130.712 120.595 60.259 150.550 150.000 210.613 50.175 140.250 160.434 70.437 30.411 130.857 60.485 60.591 150.267 190.944 90.359 9
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-ResNet0.459 131.000 10.737 80.159 200.259 140.587 130.138 50.475 130.217 110.416 90.408 100.128 130.315 150.714 120.411 90.536 160.590 40.873 160.304 13
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)
MASCpermissive0.447 140.528 180.555 150.381 120.382 100.633 100.002 190.509 110.260 100.361 110.432 80.327 80.451 80.571 150.367 130.639 110.386 130.980 80.276 14
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.382 151.000 10.432 180.245 160.190 160.577 140.013 160.263 160.033 200.320 140.240 160.075 160.422 120.857 60.117 180.699 60.271 180.883 150.235 16
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
UNet-backbone0.319 160.667 130.715 110.233 170.189 170.479 180.008 170.218 170.067 190.201 170.173 170.107 150.123 180.438 160.150 160.615 130.355 140.916 110.093 21
R-PointNet0.306 170.500 190.405 190.311 130.348 110.589 120.054 110.068 200.126 150.283 150.290 140.028 190.219 160.214 190.331 140.396 190.275 170.821 180.245 15
Region0.248 180.667 130.437 170.188 180.153 180.491 160.000 210.208 180.094 170.153 180.099 190.057 180.217 170.119 200.039 210.466 180.302 160.640 190.140 18
3D-BEVIS0.248 180.667 130.566 140.076 210.035 220.394 190.027 140.035 210.098 160.099 200.030 210.025 200.098 190.375 170.126 170.604 140.181 200.854 170.171 17
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Seg-Clusterpermissive0.215 200.370 200.337 210.285 140.105 190.325 200.025 150.282 150.085 180.105 190.107 180.007 220.079 200.317 180.114 190.309 210.304 150.587 200.123 20
Sgpn_scannet0.143 210.208 220.390 200.169 190.065 200.275 210.029 130.069 190.000 210.087 210.043 200.014 210.027 220.000 210.112 200.351 200.168 210.438 210.138 19
MaskRCNN 2d->3d Proj0.058 220.333 210.002 220.000 220.053 210.002 220.002 200.021 220.000 210.045 220.024 220.238 90.065 210.000 210.014 220.107 220.020 220.110 220.006 22

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
Marin Oršić, Ivan Krešo, Petra Bevandić, Siniša Šegvić: In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images. CVPR 2019
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
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