This is a repository for compiling the performances of all computer vision / machine learning / reote sensing papers, published or preprints. This page doubles as a community-sourced bibliography.
There are so many papers on the subject these days that it can be hard to keep track. So help me out! Add missing papers/banchmarks or correct mistakes through pull requests.
OA : overall
mAcc : mean interclass accuracy
mIoU : mean interclass accuracy
WS : workshops
By default algorithms are sorted by decreasing mIoU.
| Shorthand | Reference | Paper | Published | Code | mIoU | OA | mAcc |
|---|---|---|---|---|---|---|---|
| SSP + SPG | arXiv | CVPR2019 | pytorch | 68.4 | 87.9 | 78.3 | |
| PointCNN | [6] | arXiv | Neurips2018 | tensorflow/pytorch | 65.4 | 88.1 | 75.6 |
| SPG | [5] | arXiv | CVPR2018 | pytorch | 62.1 | 85.5 | 73.0 |
| Engelmann2018 | [4] | arXiv | ECCV2018 WS | - | 58.3 | 84.0 | 67.8 |
| PointNet++ | [3] | nips.cc | Neurips2017 | tensorflow | 54.5 | 81.0 | 67.1 |
| Engelmann2017 | [2] | arXiv | ICCV2017 WS | - | 49.7 | 81.1 | 66.4 |
| PointNet | [1] | arXiv | CVPR2017 | tensorflow | 47.6 | 78.5 | 66.2 |
| Shorthand | Reference | Paper | Published | Code | mIoU | OA | mAcc |
|---|---|---|---|---|---|---|---|
| SSP + SPG | arXiv | CVPR2019 | pytorch | 61.7 | 87.9 | 68.2 | |
| PCCN | thecvf.com | CVPR2018 | - | 58.3 | - | 67.0 | |
| SPG | [5] | arXiv | CVPR2018 | pytorch | 58.0 | 86.4 | 66.5 |
| PointCNN | [6] | arXiv | Neurips2018 | tensorflow/pytorch | 57.3 | 85.9 | 63.9 |
| Engelmann2018 | [4] | arXiv | ECCV2018 WS | - | 52.2 | 84.2 | 61.8 |
| PointNet | [1] | arXiv | CVPR2017 | tensorflow | 41.1 | - | 49.0 |
| Shorthand | Reference | Paper | Code | Published | mIoU | OA | mAcc |
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| Shorthand | Reference | Paper | Code | Published | mIoU | OA | mAcc |
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| Shorthand | Reference | Paper | Code | Published | mIoU | OA | mAcc |
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| Shorthand | Reference | Paper | Code | Published | mIoU | OA | mAcc |
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[1] C. R. Qi, H. Su, K. Mo, and L. J. Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In CVPR, 2017
[2] F. Engelmann, T. Kontogianni, A. Hermans, and B. Leibe. Exploring spatial context for 3d semantic segmentation of point clouds. In ICCV Workshops, 2017.
[3] C. R. Qi, L. Yi, H. Su, and L. J. Guibas. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In NIPS, 2017
[4] F. Engelmann, T. Kontogianni, J. Schult, and B. Leibe. Know what your neighbors do: 3d semantic segmentation of point clouds. In GMDL Workshop, ECCV, 2018.
[5] L. Landrieu and M. Simonovsky. Large-scale point cloud semantic segmentation with superpoint graphs. In CVPR. IEEE, 2018.
[6] Y. Li, R. Bu, M. Sun, and B. Chen. Pointcnn: Convolution on X-transformed points. In Neurips, 2018.
[7] S. Wang, S. Suo, W.-C. M. A. Pokrovsky, and R. Urtasun. Deep parametric continuous convolutional neural networks. In CVPR, 2018.
[8] M. Jiang and Y. Wu and T. Zhao and Z. Zhao and C. Lu. PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation. arXiv, 2018.