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 bysorted 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
Virtual MVFusion0.746 30.771 150.819 20.848 10.702 60.865 30.397 280.899 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
SparseConvNet0.725 50.647 310.821 10.846 20.721 40.869 20.533 20.754 120.603 100.614 50.955 30.572 30.325 40.710 60.870 20.724 60.823 20.628 70.934 40.865 30.683 7
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 10.675 120.772 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
BPNet0.749 20.909 10.818 30.811 60.752 10.839 50.485 90.842 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
FusionNet0.688 90.704 260.741 160.754 160.656 90.829 80.501 60.741 130.609 80.548 120.950 120.522 60.371 10.633 100.756 120.715 70.771 90.623 80.861 250.814 100.658 8
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
MatchingNet0.724 60.812 110.812 50.810 70.735 30.834 60.495 80.860 20.572 150.602 70.954 40.512 70.280 130.757 30.845 80.725 50.780 70.606 120.937 20.851 60.700 6
CU-Hybrid Net0.693 80.596 360.789 70.803 90.677 70.800 160.469 130.846 50.554 210.591 90.948 160.500 80.316 60.609 120.847 70.732 40.808 40.593 130.894 100.839 70.652 9
joint point-basedpermissive0.634 180.614 340.778 90.667 290.633 130.825 90.420 220.804 70.467 350.561 100.951 80.494 90.291 90.566 200.458 310.579 310.764 100.559 210.838 270.814 100.598 20
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
JSENet0.699 70.881 20.762 110.821 50.667 80.800 160.522 30.792 80.613 60.607 60.935 300.492 100.205 240.576 190.853 50.691 90.758 110.652 60.872 200.828 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
PointASNLpermissive0.666 110.703 270.781 80.751 180.655 100.830 70.471 120.769 110.474 330.537 130.951 80.475 110.279 140.635 80.698 200.675 120.751 130.553 230.816 310.806 130.703 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
MCCNNpermissive0.633 190.866 30.731 180.771 120.576 230.809 130.410 240.684 230.497 290.491 250.949 130.466 120.105 430.581 170.646 230.620 210.680 260.542 270.817 300.795 190.618 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
MVPNetpermissive0.641 140.831 80.715 200.671 270.590 190.781 240.394 290.679 250.642 50.553 110.937 280.462 130.256 150.649 70.406 360.626 200.691 230.666 50.877 140.792 220.608 17
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
FPConvpermissive0.639 160.785 120.760 120.713 190.603 160.798 180.392 300.534 380.603 100.524 170.948 160.457 140.250 170.538 220.723 140.598 270.696 220.614 90.872 200.799 160.567 28
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PointMTL0.632 200.731 210.688 290.675 250.591 180.784 230.444 210.565 360.610 70.492 240.949 130.456 150.254 160.587 140.706 170.599 260.665 290.612 110.868 240.791 230.579 24
FusionAwareConv0.630 230.604 350.741 160.766 140.590 190.747 310.501 60.734 140.503 280.527 150.919 380.454 160.323 50.550 210.420 350.678 110.688 240.544 260.896 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
KP-FCNN0.684 100.847 60.758 140.784 110.647 110.814 110.473 110.772 100.605 90.594 80.935 300.450 170.181 320.587 140.805 110.690 100.785 60.614 90.882 130.819 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
DCM-Net0.658 130.778 140.702 220.806 80.619 140.813 120.468 140.693 220.494 300.524 170.941 240.449 180.298 80.510 250.821 90.675 120.727 150.568 170.826 280.803 150.637 12
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
SConv0.636 170.830 90.697 260.752 170.572 250.780 250.445 190.716 180.529 230.530 140.951 80.446 190.170 330.507 270.666 220.636 190.682 250.541 280.886 120.799 160.594 21
PointMRNet-lite0.625 240.643 320.711 210.697 210.581 220.801 140.408 250.670 270.558 200.497 230.944 220.436 200.152 390.617 110.708 160.603 240.743 140.532 310.870 230.784 240.545 30
3DSM_DMMF0.631 210.626 330.745 150.801 100.607 150.751 300.506 50.729 160.565 170.491 250.866 450.434 210.197 280.595 130.630 240.709 80.705 190.560 200.875 160.740 350.491 37
HPEIN0.618 260.729 220.668 320.647 310.597 170.766 280.414 230.680 240.520 250.525 160.946 200.432 220.215 220.493 310.599 260.638 180.617 360.570 160.897 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
SIConv0.625 240.830 90.694 270.757 150.563 260.772 270.448 170.647 310.520 250.509 190.949 130.431 230.191 290.496 300.614 250.647 170.672 270.535 300.876 150.783 250.571 26
PointMRNet0.640 150.717 250.701 230.692 220.576 230.801 140.467 150.716 180.563 190.459 300.953 50.429 240.169 340.581 170.854 40.605 230.710 170.550 240.894 100.793 210.575 25
APCF-Net0.631 210.742 190.687 310.672 260.557 270.792 200.408 250.665 280.545 220.508 200.952 70.428 250.186 300.634 90.702 180.620 210.706 180.555 220.873 190.798 180.581 23
PointConvpermissive0.666 110.781 130.759 130.699 200.644 120.822 100.475 100.779 90.564 180.504 220.953 50.428 250.203 260.586 160.754 130.661 150.753 120.588 140.902 70.813 120.642 11
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
SPH3D-GCNpermissive0.610 270.858 50.772 100.489 420.532 280.792 200.404 270.643 320.570 160.507 210.935 300.414 270.046 480.510 250.702 180.602 250.705 190.549 250.859 260.773 270.534 32
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 280.760 170.667 330.649 300.521 290.793 190.457 160.648 300.528 240.434 340.947 190.401 280.153 380.454 320.721 150.648 160.717 160.536 290.904 60.765 290.485 38
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
SegGCNpermissive0.589 310.833 70.731 180.539 400.514 300.789 220.448 170.467 390.573 140.484 270.936 290.396 290.061 470.501 280.507 300.594 280.700 210.563 190.874 170.771 280.493 36
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
3DMV, FTSDF0.501 370.558 400.608 420.424 470.478 340.690 370.246 430.586 340.468 340.450 310.911 400.394 300.160 360.438 330.212 440.432 410.541 420.475 360.742 380.727 360.477 39
LAP-D0.594 290.720 230.692 280.637 330.456 360.773 260.391 320.730 150.587 120.445 320.940 260.381 310.288 100.434 340.453 320.591 290.649 300.581 150.777 350.749 340.610 16
Pointnet++ & Featurepermissive0.557 350.735 200.661 350.686 230.491 320.744 320.392 300.539 370.451 360.375 380.946 200.376 320.205 240.403 370.356 380.553 340.643 320.497 340.824 290.756 310.515 34
DPC0.592 300.720 230.700 240.602 360.480 330.762 290.380 340.713 200.585 130.437 330.940 260.369 330.288 100.434 340.509 290.590 300.639 340.567 180.772 360.755 320.592 22
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
DVVNet0.562 340.648 300.700 240.770 130.586 210.687 380.333 360.650 290.514 270.475 290.906 420.359 340.223 210.340 390.442 340.422 420.668 280.501 330.708 400.779 260.534 32
TextureNetpermissive0.566 330.672 290.664 340.671 270.494 310.719 340.445 190.678 260.411 400.396 350.935 300.356 350.225 200.412 360.535 270.565 330.636 350.464 370.794 340.680 400.568 27
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
CCRFNet0.589 310.766 160.659 360.683 240.470 350.740 330.387 330.620 330.490 310.476 280.922 360.355 360.245 180.511 240.511 280.571 320.643 320.493 350.872 200.762 300.600 19
PanopticFusion-label0.529 360.491 430.688 290.604 350.386 390.632 430.225 470.705 210.434 380.293 420.815 460.348 370.241 190.499 290.669 210.507 350.649 300.442 410.796 330.602 460.561 29
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
PCNN0.498 380.559 390.644 390.560 390.420 380.711 360.229 450.414 400.436 370.352 390.941 240.324 380.155 370.238 440.387 370.493 360.529 430.509 320.813 320.751 330.504 35
3DMV0.484 390.484 440.538 450.643 320.424 370.606 460.310 370.574 350.433 390.378 370.796 470.301 390.214 230.537 230.208 450.472 400.507 460.413 440.693 410.602 460.539 31
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
Tangent Convolutionspermissive0.438 440.437 470.646 380.474 440.369 410.645 420.353 350.258 460.282 480.279 430.918 390.298 400.147 400.283 410.294 400.487 370.562 390.427 430.619 440.633 430.352 44
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
PointCNN with RGBpermissive0.458 400.577 380.611 410.356 490.321 450.715 350.299 390.376 430.328 460.319 400.944 220.285 410.164 350.216 470.229 430.484 380.545 410.456 390.755 370.709 370.475 40
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
PNET20.442 420.548 410.548 440.597 370.363 420.628 440.300 380.292 440.374 430.307 410.881 440.268 420.186 300.238 440.204 460.407 430.506 470.449 400.667 420.620 440.462 41
FCPNpermissive0.447 410.679 280.604 430.578 380.380 400.682 390.291 400.106 490.483 320.258 470.920 370.258 430.025 490.231 460.325 390.480 390.560 400.463 380.725 390.666 420.231 49
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
subcloud_weak0.411 450.479 450.650 370.475 430.285 480.519 490.087 500.725 170.396 420.386 360.621 500.250 440.117 410.338 400.443 330.188 500.594 370.369 470.377 500.616 450.306 45
SurfaceConvPF0.442 420.505 420.622 400.380 480.342 440.654 410.227 460.397 420.367 440.276 440.924 350.240 450.198 270.359 380.262 410.366 440.581 380.435 420.640 430.668 410.398 42
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
ScanNet+FTSDF0.383 470.297 490.491 470.432 460.358 430.612 450.274 410.116 480.411 400.265 450.904 430.229 460.079 450.250 420.185 470.320 470.510 440.385 450.548 460.597 480.394 43
SPLAT Netcopyleft0.393 460.472 460.511 460.606 340.311 460.656 400.245 440.405 410.328 460.197 480.927 340.227 470.000 510.001 510.249 420.271 490.510 440.383 460.593 450.699 380.267 47
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
PointNet++permissive0.339 480.584 370.478 480.458 450.256 490.360 500.250 420.247 470.278 490.261 460.677 490.183 480.117 410.212 480.145 490.364 450.346 500.232 500.548 460.523 490.252 48
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 490.353 480.290 500.278 500.166 500.553 470.169 490.286 450.147 500.148 500.908 410.182 490.064 460.023 500.018 510.354 460.363 480.345 480.546 480.685 390.278 46
ScanNetpermissive0.306 500.203 500.366 490.501 410.311 460.524 480.211 480.002 510.342 450.189 490.786 480.145 500.102 440.245 430.152 480.318 480.348 490.300 490.460 490.437 500.182 50
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 510.000 510.041 510.172 510.030 510.062 510.001 510.035 500.004 510.051 510.143 510.019 510.003 500.041 490.050 500.003 510.054 510.018 510.005 510.264 510.082 51

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 bysorted 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
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]
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
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
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)
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-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
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)
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
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
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]
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
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
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
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
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
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
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.
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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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 bysorted 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