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
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions.
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. IROS 2019 (to appear)
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
DCNet0.607 11.000 10.907 10.792 10.462 20.788 10.151 10.535 50.292 20.395 30.501 10.263 30.600 11.000 10.598 10.857 10.502 50.918 50.368 4
MTML0.549 21.000 10.807 20.588 40.327 50.647 20.004 120.815 10.180 50.418 10.364 60.182 50.445 31.000 10.442 40.688 30.571 21.000 10.396 2
Occipital-SCS0.512 31.000 10.716 40.509 50.506 10.611 40.092 50.602 40.177 60.346 50.383 50.165 60.442 40.850 50.386 70.618 50.543 30.889 80.389 3
3D-BoNet0.488 41.000 10.672 70.590 30.301 60.484 90.098 40.620 20.306 10.341 60.259 80.125 80.434 50.796 60.402 60.499 100.513 40.909 70.439 1
PanopticFusion-inst0.478 50.667 70.712 60.595 20.259 80.550 80.000 150.613 30.175 70.250 90.434 20.437 10.411 70.857 30.485 20.591 80.267 120.944 40.359 5
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
ResNet-backbone0.459 61.000 10.737 30.159 130.259 70.587 60.138 20.475 70.217 40.416 20.408 40.128 70.315 80.714 70.411 50.536 90.590 10.873 100.304 6
MASCpermissive0.447 70.528 100.555 90.381 70.382 30.633 30.002 130.509 60.260 30.361 40.432 30.327 20.451 20.571 80.367 80.639 40.386 70.980 20.276 7
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.382 81.000 10.432 110.245 100.190 100.577 70.013 100.263 100.033 130.320 70.240 100.075 110.422 60.857 30.117 120.699 20.271 110.883 90.235 10
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
DPC-instance0.355 90.500 110.517 100.467 60.228 90.422 110.133 30.405 80.111 90.205 100.241 90.075 100.233 90.306 120.445 30.439 110.457 60.974 30.239 9
UNet-backbone0.319 100.667 70.715 50.233 110.189 110.479 100.008 110.218 110.067 120.201 110.173 110.107 90.123 110.438 90.150 100.615 60.355 80.916 60.093 14
R-PointNet0.306 110.500 110.405 120.311 80.348 40.589 50.054 60.068 130.126 80.283 80.290 70.028 120.219 100.214 130.331 90.396 120.275 100.821 120.245 8
3D-BEVIS0.248 120.667 70.566 80.076 140.035 150.394 120.027 80.035 140.098 100.099 130.030 140.025 130.098 120.375 100.126 110.604 70.181 130.854 110.171 11
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Seg-Clusterpermissive0.215 130.370 130.337 140.285 90.105 120.325 130.025 90.282 90.085 110.105 120.107 120.007 150.079 130.317 110.114 130.309 140.304 90.587 130.123 13
Sgpn_scannet0.143 140.208 150.390 130.169 120.065 130.275 140.029 70.069 120.000 140.087 140.043 130.014 140.027 150.000 140.112 140.351 130.168 140.438 140.138 12
MaskRCNN 2d->3d Proj0.058 150.333 140.002 150.000 150.053 140.002 150.002 140.021 150.000 140.045 150.024 150.238 40.065 140.000 140.014 150.107 150.020 150.110 150.006 15

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. International Journal of Computer Vision, 2019
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. International Journal of Computer Vision, 2019
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
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort 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