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
MinkowskiNetpermissive0.734 10.858 20.833 10.834 20.716 20.855 20.459 30.836 10.639 20.641 10.953 20.541 20.302 20.743 10.865 20.726 10.771 30.664 20.891 30.851 20.694 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
SparseConvNet0.725 20.647 120.821 20.846 10.721 10.869 10.533 10.754 40.603 40.614 20.955 10.572 10.325 10.710 20.870 10.724 20.823 10.628 30.934 10.865 10.683 2
KP-FCNN0.684 30.847 30.758 40.784 30.647 30.814 40.473 20.772 30.605 30.594 30.935 120.450 60.181 160.587 40.805 30.690 30.785 20.614 40.882 40.819 30.632 3
MVPNet0.641 40.831 40.715 60.671 70.590 60.781 60.394 90.679 100.642 10.553 50.937 110.462 50.256 60.649 30.406 140.626 50.691 50.666 10.877 50.792 70.608 6
joint point-based0.634 50.614 140.778 30.667 90.633 40.825 30.420 60.804 20.467 130.561 40.951 30.494 30.291 30.566 60.458 110.579 90.764 40.559 80.838 70.814 40.598 9
MCCNNpermissive0.633 60.866 10.731 50.771 40.576 80.809 50.410 80.684 80.497 90.491 70.949 40.466 40.105 220.581 50.646 50.620 60.680 60.542 90.817 90.795 60.618 4
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
HPEIN0.618 70.729 60.668 110.647 100.597 50.766 80.414 70.680 90.520 70.525 60.946 50.432 70.215 110.493 100.599 60.638 40.617 140.570 60.897 20.806 50.605 7
LAP-D0.594 80.720 70.692 90.637 120.456 140.773 70.391 100.730 50.587 50.445 110.940 90.381 90.288 40.434 130.453 120.591 70.649 80.581 50.777 130.749 130.610 5
DPC0.592 90.720 70.700 70.602 150.480 100.762 90.380 120.713 60.585 60.437 130.940 90.369 110.288 40.434 130.509 100.590 80.639 110.567 70.772 140.755 110.592 10
CCRFNet0.589 100.766 50.659 130.683 60.470 130.740 100.387 110.620 130.490 100.476 80.922 160.355 140.245 70.511 80.511 90.571 100.643 100.493 130.872 60.762 90.600 8
TextureNetpermissive0.566 110.672 100.664 120.671 70.494 90.719 120.445 40.678 110.411 180.396 140.935 120.356 130.225 90.412 150.535 80.565 110.636 130.464 150.794 120.680 180.568 11
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
DVVNet0.562 120.648 110.700 70.770 50.586 70.687 160.333 140.650 120.514 80.475 90.906 210.359 120.223 100.340 170.442 130.422 200.668 70.501 120.708 180.779 80.534 14
PointConv0.556 130.636 130.640 160.574 180.472 120.739 110.430 50.433 160.418 170.445 110.944 60.372 100.185 150.464 110.575 70.540 120.639 110.505 110.827 80.762 90.515 15
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PanopticFusion-label0.529 140.491 210.688 100.604 140.386 170.632 210.225 250.705 70.434 150.293 190.815 240.348 150.241 80.499 90.669 40.507 130.649 80.442 190.796 110.602 230.561 12
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. arXiv
3DMV, FTSDF0.501 150.558 180.608 190.424 240.478 110.690 150.246 210.586 140.468 120.450 100.911 190.394 80.160 180.438 120.212 210.432 190.541 190.475 140.742 160.727 140.477 17
PCNN0.498 160.559 170.644 150.560 190.420 160.711 140.229 230.414 170.436 140.352 160.941 80.324 160.155 190.238 210.387 150.493 140.529 200.509 100.813 100.751 120.504 16
3DMV0.484 170.484 220.538 220.643 110.424 150.606 240.310 150.574 150.433 160.378 150.796 250.301 170.214 120.537 70.208 220.472 180.507 230.413 220.693 190.602 230.539 13
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 180.577 160.611 180.356 260.321 230.715 130.299 170.376 200.328 230.319 170.944 60.285 190.164 170.216 240.229 200.484 160.545 180.456 170.755 150.709 150.475 18
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 190.679 90.604 200.578 170.380 180.682 170.291 180.106 260.483 110.258 240.920 170.258 210.025 260.231 230.325 160.480 170.560 170.463 160.725 170.666 200.231 26
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
PNET20.442 200.548 190.548 210.597 160.363 200.628 220.300 160.292 210.374 200.307 180.881 230.268 200.186 140.238 210.204 230.407 210.506 240.449 180.667 200.620 220.462 19
SurfaceConvPF0.442 200.505 200.622 170.380 250.342 220.654 190.227 240.397 190.367 210.276 210.924 150.240 220.198 130.359 160.262 180.366 220.581 150.435 200.640 210.668 190.398 20
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 220.437 240.646 140.474 210.369 190.645 200.353 130.258 230.282 250.279 200.918 180.298 180.147 200.283 180.294 170.487 150.562 160.427 210.619 220.633 210.352 22
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
SPLAT Netcopyleft0.393 230.472 230.511 230.606 130.311 240.656 180.245 220.405 180.328 230.197 250.927 140.227 240.000 280.001 280.249 190.271 270.510 210.383 240.593 230.699 160.267 24
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+FTSDF0.383 240.297 260.491 240.432 230.358 210.612 230.274 190.116 250.411 180.265 220.904 220.229 230.079 240.250 190.185 240.320 250.510 210.385 230.548 240.597 250.394 21
PointNet++permissive0.339 250.584 150.478 250.458 220.256 260.360 270.250 200.247 240.278 260.261 230.677 270.183 250.117 210.212 250.145 260.364 230.346 270.232 270.548 240.523 260.252 25
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 260.353 250.290 270.278 270.166 270.553 250.169 270.286 220.147 270.148 270.908 200.182 260.064 250.023 270.018 280.354 240.363 250.345 250.546 260.685 170.278 23
ScanNetpermissive0.306 270.203 270.366 260.501 200.311 240.524 260.211 260.002 280.342 220.189 260.786 260.145 270.102 230.245 200.152 250.318 260.348 260.300 260.460 270.437 270.182 27
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 280.000 280.041 280.172 280.030 280.062 280.001 280.035 270.004 280.051 280.143 280.019 280.003 270.041 260.050 270.003 280.054 280.018 280.005 280.264 280.082 28

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
Occipital-SCS0.512 11.000 10.716 20.509 30.506 10.611 30.092 30.602 30.177 50.346 40.383 40.165 40.442 20.850 40.386 40.618 30.543 20.889 60.389 2
3D-BoNet0.488 21.000 10.672 50.590 20.301 40.484 80.098 20.620 10.306 10.341 50.259 70.125 60.434 30.796 50.402 30.499 90.513 30.909 50.439 1
MTML0.481 31.000 10.666 60.377 50.272 50.709 10.001 120.579 40.254 30.361 30.318 50.095 80.432 41.000 10.184 70.601 60.487 40.938 30.384 3
PanopticFusion-inst0.478 40.667 60.712 40.595 10.259 70.550 70.000 130.613 20.175 60.250 80.434 10.437 10.411 60.857 20.485 10.591 70.267 100.944 20.359 4
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. arXiv
ResNet-backbone0.459 51.000 10.737 10.159 110.259 60.587 50.138 10.475 60.217 40.416 10.408 30.128 50.315 70.714 60.411 20.536 80.590 10.873 80.304 5
MASCpermissive0.447 60.528 90.555 80.381 40.382 20.633 20.002 100.509 50.260 20.361 20.432 20.327 20.451 10.571 70.367 50.639 20.386 50.980 10.276 6
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.382 71.000 10.432 90.245 80.190 80.577 60.013 80.263 80.033 110.320 60.240 80.075 90.422 50.857 20.117 100.699 10.271 90.883 70.235 8
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
UNet-backbone0.319 80.667 60.715 30.233 90.189 90.479 90.008 90.218 90.067 100.201 90.173 90.107 70.123 90.438 80.150 80.615 40.355 60.916 40.093 12
R-PointNet0.306 90.500 100.405 100.311 60.348 30.589 40.054 40.068 110.126 70.283 70.290 60.028 100.219 80.214 110.331 60.396 100.275 80.821 100.245 7
3D-BEVIS0.248 100.667 60.566 70.076 120.035 130.394 100.027 60.035 120.098 80.099 110.030 120.025 110.098 100.375 90.126 90.604 50.181 110.854 90.171 9
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Seg-Clusterpermissive0.215 110.370 110.337 120.285 70.105 100.325 110.025 70.282 70.085 90.105 100.107 100.007 130.079 110.317 100.114 110.309 120.304 70.587 110.123 11
Sgpn_scannet0.143 120.208 130.390 110.169 100.065 110.275 120.029 50.069 100.000 120.087 120.043 110.014 120.027 130.000 120.112 120.351 110.168 120.438 120.138 10
MaskRCNN 2d->3d Proj0.058 130.333 120.002 130.000 130.053 120.002 130.002 110.021 130.000 120.045 130.024 130.238 30.065 120.000 120.014 130.107 130.020 130.110 130.006 13

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
RFBNet0.592 10.616 30.758 10.659 10.581 10.330 30.469 10.655 40.543 30.524 10.924 10.355 20.336 40.572 20.479 20.671 30.648 20.480 10.814 40.814 10.614 2
DCRedNet0.583 20.682 20.723 20.542 40.510 40.310 50.451 20.668 20.549 20.520 20.920 20.375 10.446 10.528 40.417 30.670 40.577 60.478 20.862 20.806 20.628 1
SSMAcopyleft0.577 30.695 10.716 40.439 60.563 20.314 40.444 30.719 10.551 10.503 30.887 50.346 30.348 30.603 10.353 50.709 10.600 40.457 40.901 10.786 30.599 3
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
FuseNetpermissive0.535 40.570 50.681 50.182 90.512 30.290 60.431 40.659 30.504 50.495 40.903 40.308 40.428 20.523 50.365 40.676 20.621 30.470 30.762 60.779 40.541 5
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 50.613 40.722 30.418 70.358 90.337 20.370 70.479 70.443 60.368 70.907 30.207 70.213 80.464 70.525 10.618 50.657 10.450 50.788 50.721 60.408 8
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
3DMV (2d proj)0.498 60.481 70.612 60.579 30.456 60.343 10.384 50.623 50.525 40.381 60.845 60.254 60.264 60.557 30.182 70.581 70.598 50.429 60.760 70.661 80.446 7
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
ILC-PSPNet0.475 70.490 60.581 70.289 80.507 50.067 90.379 60.610 60.417 80.435 50.822 80.278 50.267 50.503 60.228 60.616 60.533 70.375 70.820 30.729 50.560 4
Enet (reimpl)0.376 80.264 90.452 90.452 50.365 70.181 70.143 90.456 80.409 90.346 80.769 90.164 80.218 70.359 80.123 90.403 90.381 90.313 90.571 80.685 70.472 6
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 90.293 80.521 80.657 20.361 80.161 80.250 80.004 90.440 70.183 90.836 70.125 90.060 90.319 90.132 80.417 80.412 80.344 80.541 90.427 90.109 9
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
MaskRCNN_ScanNetpermissive0.119 10.129 10.212 10.002 10.112 10.148 10.014 10.205 10.044 10.066 10.078 10.095 10.142 10.030 10.128 10.139 10.080 10.459 10.057 1
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 bysorted bysort bysort bysort bysort by
SE-ResNeXt-SSMA0.498 10.000 20.812 10.941 10.500 10.500 10.500 10.500 10.429 20.500 10.667 10.500 10.625 10.000 1
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 20.250 10.812 10.529 20.500 10.500 10.000 20.500 10.571 10.000 20.556 20.000 20.375 20.000 1