3D Semantic label benchmark
This table lists the benchmark results for the 3D semantic label scenario.
Method | Info | avg iou | bathtub | bed | bookshelf | cabinet | chair | counter | curtain | desk | door | floor | otherfurniture | picture | refrigerator | shower curtain | sink | sofa | table | toilet | wall | window |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ||
MinkowskiNet | ![]() | 0.736 1 | 0.859 2 | 0.818 2 | 0.832 2 | 0.709 2 | 0.840 2 | 0.521 2 | 0.853 1 | 0.660 1 | 0.643 1 | 0.951 3 | 0.544 2 | 0.286 6 | 0.731 1 | 0.893 1 | 0.675 4 | 0.772 3 | 0.683 1 | 0.874 7 | 0.852 2 | 0.727 1 |
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 | ||||||||||||||||||||||
SparseConvNet | 0.725 2 | 0.647 18 | 0.821 1 | 0.846 1 | 0.721 1 | 0.869 1 | 0.533 1 | 0.754 5 | 0.603 4 | 0.614 2 | 0.955 1 | 0.572 1 | 0.325 1 | 0.710 2 | 0.870 2 | 0.724 1 | 0.823 1 | 0.628 3 | 0.934 1 | 0.865 1 | 0.683 2 | |
KP-FCNN | 0.684 3 | 0.847 4 | 0.758 6 | 0.784 4 | 0.647 3 | 0.814 5 | 0.473 5 | 0.772 4 | 0.605 3 | 0.594 3 | 0.935 16 | 0.450 7 | 0.181 21 | 0.587 4 | 0.805 4 | 0.690 3 | 0.785 2 | 0.614 4 | 0.882 5 | 0.819 3 | 0.632 5 | |
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019 | ||||||||||||||||||||||
PointConv | ![]() | 0.666 4 | 0.781 6 | 0.759 5 | 0.699 9 | 0.644 4 | 0.822 4 | 0.475 4 | 0.779 3 | 0.564 8 | 0.504 10 | 0.953 2 | 0.428 9 | 0.203 17 | 0.586 5 | 0.754 5 | 0.661 6 | 0.753 5 | 0.588 5 | 0.902 2 | 0.813 5 | 0.642 3 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 | ||||||||||||||||||||||
DMC-Net | 0.653 5 | 0.771 7 | 0.701 11 | 0.801 3 | 0.619 7 | 0.807 7 | 0.463 6 | 0.680 11 | 0.495 15 | 0.520 8 | 0.940 12 | 0.452 6 | 0.301 2 | 0.496 14 | 0.816 3 | 0.664 5 | 0.719 6 | 0.563 11 | 0.822 14 | 0.799 8 | 0.638 4 | |
MVPNet | 0.641 6 | 0.831 5 | 0.715 10 | 0.671 12 | 0.590 10 | 0.781 9 | 0.394 14 | 0.679 13 | 0.642 2 | 0.553 5 | 0.937 15 | 0.462 5 | 0.256 8 | 0.649 3 | 0.406 19 | 0.626 9 | 0.691 9 | 0.666 2 | 0.877 6 | 0.792 10 | 0.608 9 | |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 | ||||||||||||||||||||||
joint point-based | ![]() | 0.634 7 | 0.614 19 | 0.778 3 | 0.667 14 | 0.633 6 | 0.825 3 | 0.420 8 | 0.804 2 | 0.467 19 | 0.561 4 | 0.951 3 | 0.494 3 | 0.291 3 | 0.566 8 | 0.458 16 | 0.579 15 | 0.764 4 | 0.559 12 | 0.838 12 | 0.814 4 | 0.598 12 |
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019 | ||||||||||||||||||||||
MCCNN | ![]() | 0.633 8 | 0.866 1 | 0.731 8 | 0.771 5 | 0.576 12 | 0.809 6 | 0.410 12 | 0.684 10 | 0.497 14 | 0.491 12 | 0.949 6 | 0.466 4 | 0.105 27 | 0.581 7 | 0.646 8 | 0.620 10 | 0.680 12 | 0.542 15 | 0.817 15 | 0.795 9 | 0.618 7 |
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 | ||||||||||||||||||||||
PointASNL | 0.630 9 | 0.738 10 | 0.729 9 | 0.764 8 | 0.637 5 | 0.779 10 | 0.416 10 | 0.626 17 | 0.518 11 | 0.530 6 | 0.951 3 | 0.398 13 | 0.260 7 | 0.518 10 | 0.576 12 | 0.590 13 | 0.687 10 | 0.568 8 | 0.872 8 | 0.810 6 | 0.631 6 | |
CDF-SM3D | 0.626 10 | 0.592 20 | 0.746 7 | 0.767 7 | 0.607 8 | 0.761 15 | 0.501 3 | 0.738 6 | 0.546 9 | 0.503 11 | 0.864 29 | 0.421 10 | 0.198 18 | 0.584 6 | 0.579 11 | 0.694 2 | 0.706 7 | 0.566 10 | 0.885 4 | 0.745 19 | 0.523 19 | |
HPEIN | 0.618 11 | 0.729 12 | 0.668 16 | 0.647 15 | 0.597 9 | 0.766 13 | 0.414 11 | 0.680 11 | 0.520 10 | 0.525 7 | 0.946 7 | 0.432 8 | 0.215 13 | 0.493 15 | 0.599 9 | 0.638 7 | 0.617 20 | 0.570 7 | 0.897 3 | 0.806 7 | 0.605 10 | |
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-GCN | ![]() | 0.610 12 | 0.858 3 | 0.772 4 | 0.489 26 | 0.532 14 | 0.792 8 | 0.404 13 | 0.643 16 | 0.570 7 | 0.507 9 | 0.935 16 | 0.414 11 | 0.046 31 | 0.510 12 | 0.702 6 | 0.602 11 | 0.705 8 | 0.549 13 | 0.859 10 | 0.773 13 | 0.534 17 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. | ||||||||||||||||||||||
LAP-D | 0.594 13 | 0.720 13 | 0.692 14 | 0.637 17 | 0.456 20 | 0.773 11 | 0.391 16 | 0.730 7 | 0.587 5 | 0.445 17 | 0.940 12 | 0.381 15 | 0.288 4 | 0.434 17 | 0.453 17 | 0.591 12 | 0.649 14 | 0.581 6 | 0.777 19 | 0.749 18 | 0.610 8 | |
SIConv | 0.594 13 | 0.768 8 | 0.639 22 | 0.616 18 | 0.544 13 | 0.768 12 | 0.419 9 | 0.601 19 | 0.513 13 | 0.474 15 | 0.946 7 | 0.402 12 | 0.213 15 | 0.387 21 | 0.581 10 | 0.633 8 | 0.683 11 | 0.549 13 | 0.843 11 | 0.774 12 | 0.521 20 | |
DPC | 0.592 15 | 0.720 13 | 0.700 12 | 0.602 21 | 0.480 17 | 0.762 14 | 0.380 18 | 0.713 8 | 0.585 6 | 0.437 18 | 0.940 12 | 0.369 17 | 0.288 4 | 0.434 17 | 0.509 15 | 0.590 13 | 0.639 18 | 0.567 9 | 0.772 20 | 0.755 16 | 0.592 13 | |
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions. arXiv | ||||||||||||||||||||||
CCRFNet | 0.589 16 | 0.766 9 | 0.659 19 | 0.683 11 | 0.470 19 | 0.740 17 | 0.387 17 | 0.620 18 | 0.490 16 | 0.476 13 | 0.922 21 | 0.355 20 | 0.245 9 | 0.511 11 | 0.511 14 | 0.571 16 | 0.643 16 | 0.493 19 | 0.872 8 | 0.762 14 | 0.600 11 | |
TextureNet | ![]() | 0.566 17 | 0.672 16 | 0.664 17 | 0.671 12 | 0.494 15 | 0.719 18 | 0.445 7 | 0.678 14 | 0.411 24 | 0.396 19 | 0.935 16 | 0.356 19 | 0.225 11 | 0.412 19 | 0.535 13 | 0.565 17 | 0.636 19 | 0.464 21 | 0.794 18 | 0.680 24 | 0.568 14 |
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 | ||||||||||||||||||||||
DVVNet | 0.562 18 | 0.648 17 | 0.700 12 | 0.770 6 | 0.586 11 | 0.687 22 | 0.333 20 | 0.650 15 | 0.514 12 | 0.475 14 | 0.906 26 | 0.359 18 | 0.223 12 | 0.340 23 | 0.442 18 | 0.422 26 | 0.668 13 | 0.501 17 | 0.708 24 | 0.779 11 | 0.534 17 | |
Pointnet++ & Feature | ![]() | 0.557 19 | 0.735 11 | 0.661 18 | 0.686 10 | 0.491 16 | 0.744 16 | 0.392 15 | 0.539 22 | 0.451 20 | 0.375 21 | 0.946 7 | 0.376 16 | 0.205 16 | 0.403 20 | 0.356 21 | 0.553 18 | 0.643 16 | 0.497 18 | 0.824 13 | 0.756 15 | 0.515 21 |
PanopticFusion-label | 0.529 20 | 0.491 27 | 0.688 15 | 0.604 20 | 0.386 23 | 0.632 27 | 0.225 31 | 0.705 9 | 0.434 22 | 0.293 25 | 0.815 30 | 0.348 21 | 0.241 10 | 0.499 13 | 0.669 7 | 0.507 19 | 0.649 14 | 0.442 25 | 0.796 17 | 0.602 29 | 0.561 15 | |
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, FTSDF | 0.501 21 | 0.558 24 | 0.608 25 | 0.424 30 | 0.478 18 | 0.690 21 | 0.246 27 | 0.586 20 | 0.468 18 | 0.450 16 | 0.911 24 | 0.394 14 | 0.160 23 | 0.438 16 | 0.212 27 | 0.432 25 | 0.541 25 | 0.475 20 | 0.742 22 | 0.727 20 | 0.477 23 | |
PCNN | 0.498 22 | 0.559 23 | 0.644 21 | 0.560 24 | 0.420 22 | 0.711 20 | 0.229 29 | 0.414 23 | 0.436 21 | 0.352 22 | 0.941 11 | 0.324 22 | 0.155 24 | 0.238 27 | 0.387 20 | 0.493 20 | 0.529 26 | 0.509 16 | 0.813 16 | 0.751 17 | 0.504 22 | |
3DMV | 0.484 23 | 0.484 28 | 0.538 28 | 0.643 16 | 0.424 21 | 0.606 30 | 0.310 21 | 0.574 21 | 0.433 23 | 0.378 20 | 0.796 31 | 0.301 23 | 0.214 14 | 0.537 9 | 0.208 28 | 0.472 24 | 0.507 29 | 0.413 28 | 0.693 25 | 0.602 29 | 0.539 16 | |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 | ||||||||||||||||||||||
PointCNN with RGB | ![]() | 0.458 24 | 0.577 22 | 0.611 24 | 0.356 32 | 0.321 29 | 0.715 19 | 0.299 23 | 0.376 26 | 0.328 29 | 0.319 23 | 0.944 10 | 0.285 25 | 0.164 22 | 0.216 30 | 0.229 26 | 0.484 22 | 0.545 24 | 0.456 23 | 0.755 21 | 0.709 21 | 0.475 24 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 | ||||||||||||||||||||||
FCPN | ![]() | 0.447 25 | 0.679 15 | 0.604 26 | 0.578 23 | 0.380 24 | 0.682 23 | 0.291 24 | 0.106 32 | 0.483 17 | 0.258 30 | 0.920 22 | 0.258 27 | 0.025 32 | 0.231 29 | 0.325 22 | 0.480 23 | 0.560 23 | 0.463 22 | 0.725 23 | 0.666 26 | 0.231 32 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 | ||||||||||||||||||||||
SurfaceConvPF | 0.442 26 | 0.505 26 | 0.622 23 | 0.380 31 | 0.342 28 | 0.654 25 | 0.227 30 | 0.397 25 | 0.367 27 | 0.276 27 | 0.924 20 | 0.240 28 | 0.198 18 | 0.359 22 | 0.262 24 | 0.366 28 | 0.581 21 | 0.435 26 | 0.640 27 | 0.668 25 | 0.398 26 | |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. | ||||||||||||||||||||||
PNET2 | 0.442 26 | 0.548 25 | 0.548 27 | 0.597 22 | 0.363 26 | 0.628 28 | 0.300 22 | 0.292 27 | 0.374 26 | 0.307 24 | 0.881 28 | 0.268 26 | 0.186 20 | 0.238 27 | 0.204 29 | 0.407 27 | 0.506 30 | 0.449 24 | 0.667 26 | 0.620 28 | 0.462 25 | |
Tangent Convolutions | ![]() | 0.438 28 | 0.437 30 | 0.646 20 | 0.474 27 | 0.369 25 | 0.645 26 | 0.353 19 | 0.258 29 | 0.282 31 | 0.279 26 | 0.918 23 | 0.298 24 | 0.147 25 | 0.283 24 | 0.294 23 | 0.487 21 | 0.562 22 | 0.427 27 | 0.619 28 | 0.633 27 | 0.352 28 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 | ||||||||||||||||||||||
SPLAT Net | ![]() | 0.393 29 | 0.472 29 | 0.511 29 | 0.606 19 | 0.311 30 | 0.656 24 | 0.245 28 | 0.405 24 | 0.328 29 | 0.197 31 | 0.927 19 | 0.227 30 | 0.000 34 | 0.001 34 | 0.249 25 | 0.271 33 | 0.510 27 | 0.383 30 | 0.593 29 | 0.699 22 | 0.267 30 |
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+FTSDF | 0.383 30 | 0.297 32 | 0.491 30 | 0.432 29 | 0.358 27 | 0.612 29 | 0.274 25 | 0.116 31 | 0.411 24 | 0.265 28 | 0.904 27 | 0.229 29 | 0.079 29 | 0.250 25 | 0.185 30 | 0.320 31 | 0.510 27 | 0.385 29 | 0.548 30 | 0.597 31 | 0.394 27 | |
PointNet++ | ![]() | 0.339 31 | 0.584 21 | 0.478 31 | 0.458 28 | 0.256 32 | 0.360 33 | 0.250 26 | 0.247 30 | 0.278 32 | 0.261 29 | 0.677 33 | 0.183 31 | 0.117 26 | 0.212 31 | 0.145 32 | 0.364 29 | 0.346 33 | 0.232 33 | 0.548 30 | 0.523 32 | 0.252 31 |
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space. | ||||||||||||||||||||||
SSC-UNet | ![]() | 0.308 32 | 0.353 31 | 0.290 33 | 0.278 33 | 0.166 33 | 0.553 31 | 0.169 33 | 0.286 28 | 0.147 33 | 0.148 33 | 0.908 25 | 0.182 32 | 0.064 30 | 0.023 33 | 0.018 34 | 0.354 30 | 0.363 31 | 0.345 31 | 0.546 32 | 0.685 23 | 0.278 29 |
ScanNet | ![]() | 0.306 33 | 0.203 33 | 0.366 32 | 0.501 25 | 0.311 30 | 0.524 32 | 0.211 32 | 0.002 34 | 0.342 28 | 0.189 32 | 0.786 32 | 0.145 33 | 0.102 28 | 0.245 26 | 0.152 31 | 0.318 32 | 0.348 32 | 0.300 32 | 0.460 33 | 0.437 33 | 0.182 33 |
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17 | ||||||||||||||||||||||
ERROR | 0.054 34 | 0.000 34 | 0.041 34 | 0.172 34 | 0.030 34 | 0.062 34 | 0.001 34 | 0.035 33 | 0.004 34 | 0.051 34 | 0.143 34 | 0.019 34 | 0.003 33 | 0.041 32 | 0.050 33 | 0.003 34 | 0.054 34 | 0.018 34 | 0.005 34 | 0.264 34 | 0.082 34 | |