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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
SparseConvNet0.726 10.629 60.801 10.858 10.713 10.884 10.505 10.799 10.636 20.628 10.956 10.602 10.299 10.712 10.858 10.717 10.808 10.629 20.929 10.858 10.694 1
joint point-based0.621 30.645 40.746 20.612 60.571 40.795 30.386 50.798 20.485 40.539 30.943 40.445 30.287 20.520 40.418 60.635 30.744 20.570 30.859 30.795 30.628 3
MinkowskiNet340.679 20.811 10.734 30.739 30.641 20.804 20.413 40.759 30.696 10.545 20.938 50.518 20.141 100.623 20.757 20.680 20.723 30.684 10.896 20.821 20.651 2
DVVNet0.562 50.648 30.700 40.770 20.586 30.687 80.333 70.650 50.514 30.475 40.906 120.359 60.223 40.340 90.442 50.422 100.668 40.501 60.708 80.779 40.534 6
TextureNet0.566 40.672 20.664 50.671 40.494 50.719 50.445 20.678 40.411 80.396 70.935 60.356 70.225 30.412 70.535 40.565 40.636 60.464 80.794 60.680 100.568 4
Tangent Convolutionspermissive0.438 110.437 130.646 60.474 100.369 100.645 110.353 60.258 130.282 140.279 100.918 90.298 100.147 90.283 100.294 80.487 70.562 80.427 100.619 110.633 120.352 12
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
PointConv0.556 60.636 50.640 70.574 80.472 70.739 40.430 30.433 80.418 70.445 60.944 20.372 50.185 70.464 50.575 30.540 50.639 50.505 50.827 40.762 50.515 7
SurfaceConvPF0.442 100.505 100.622 80.380 150.342 120.654 100.227 140.397 100.367 110.276 110.924 80.240 110.198 60.359 80.262 90.366 110.581 70.435 90.640 100.668 110.398 10
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
3DMV, FTSDF0.501 70.558 80.608 90.424 130.478 60.690 70.246 110.586 60.468 50.450 50.911 100.394 40.160 80.438 60.212 110.432 90.541 90.475 70.742 70.727 70.477 9
PointCNN with RGBpermissive0.479 90.510 90.583 100.417 140.414 90.708 60.241 130.367 110.405 100.323 90.944 20.300 90.132 110.226 130.417 70.534 60.525 100.511 40.806 50.743 60.479 8
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NIPS 2018
3DMV0.484 80.484 110.538 110.643 50.424 80.606 130.310 80.574 70.433 60.378 80.796 140.301 80.214 50.537 30.208 120.472 80.507 130.413 110.693 90.602 130.539 5
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
SPLAT Netcopyleft0.393 120.472 120.511 120.606 70.311 130.656 90.245 120.405 90.328 130.197 140.927 70.227 130.000 160.001 160.249 100.271 160.510 110.383 130.593 120.699 80.267 14
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+FTSDF0.383 130.297 150.491 130.432 120.358 110.612 120.274 90.116 150.411 80.265 120.904 130.229 120.079 140.250 110.185 130.320 140.510 110.385 120.548 130.597 140.394 11
PointNet++permissive0.339 140.584 70.478 140.458 110.256 150.360 160.250 100.247 140.278 150.261 130.677 160.183 140.117 120.212 140.145 150.364 120.346 160.232 160.548 130.523 150.252 15
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
ScanNetpermissive0.306 160.203 160.366 150.501 90.311 130.524 150.211 150.002 160.342 120.189 150.786 150.145 160.102 130.245 120.152 140.318 150.348 150.300 150.460 160.437 160.182 16
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
SSC-UNetpermissive0.308 150.353 140.290 160.278 160.166 160.553 140.169 160.286 120.147 160.148 160.908 110.182 150.064 150.023 150.018 160.354 130.363 140.345 140.546 150.685 90.278 13

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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
MTML0.212 40.667 10.614 10.337 10.027 60.390 40.000 50.118 10.001 40.100 30.028 50.000 60.167 30.143 40.046 50.500 30.105 50.570 30.003 6
3D-BEVIS0.248 30.667 10.566 20.076 50.035 50.394 30.027 30.035 50.098 30.099 40.030 40.025 40.098 40.375 10.126 30.604 10.181 20.854 10.171 3
3D-SIS0.271 20.528 30.558 30.241 30.272 20.488 20.000 50.093 20.167 10.179 20.231 20.040 20.182 20.286 20.168 20.546 20.155 40.555 40.185 2
R-PointNet0.306 10.500 40.405 40.311 20.348 10.589 10.054 10.068 40.126 20.283 10.290 10.028 30.219 10.214 30.331 10.396 40.275 10.821 20.245 1
Sgpn_scannet0.143 50.208 60.390 50.169 40.065 30.275 50.029 20.069 30.000 50.087 50.043 30.014 50.027 60.000 50.112 40.351 50.168 30.438 50.138 4
MaskRCNN 2d->3d Proj0.058 60.333 50.002 60.000 60.053 40.002 60.002 40.021 60.000 50.045 60.024 60.238 10.065 50.000 50.014 60.107 60.020 60.110 60.006 5

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
FuseNetpermissive0.521 10.591 10.682 10.220 50.488 20.279 20.344 30.610 20.461 20.475 10.910 10.293 10.447 10.512 20.397 10.618 10.567 20.452 10.734 30.782 10.566 1
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
3DMV (2d proj)0.498 20.481 30.612 20.579 20.456 30.343 10.384 10.623 10.525 10.381 30.845 20.254 30.264 30.557 10.182 30.581 30.598 10.429 20.760 20.661 40.446 4
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
ILC-PSPNet0.475 30.490 20.581 30.289 40.507 10.067 50.379 20.610 20.417 40.435 20.822 40.278 20.267 20.503 30.228 20.616 20.533 30.375 30.820 10.729 20.560 2
ScanNet (2d proj)permissive0.330 50.293 40.521 40.657 10.361 50.161 40.250 40.004 50.440 30.183 50.836 30.125 50.060 50.319 50.132 40.417 40.412 40.344 40.541 50.427 50.109 5
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
Enet (reimpl)0.376 40.264 50.452 50.452 30.365 40.181 30.143 50.456 40.409 50.346 40.769 50.164 40.218 40.359 40.123 50.403 50.381 50.313 50.571 40.685 30.472 3
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysorted 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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort by
resnet50_scannet0.353 10.250 10.812 10.529 10.500 10.500 10.000 10.500 10.571 10.000 10.556 10.000 10.375 10.000 1