| --- |
| license: mit |
| tags: |
| - point-cloud-registration |
| - 3d-vision |
| - 3dmatch |
| - 3dlomatch |
| library_name: pytorch |
| --- |
| |
| # OCFNet -- Overlap-guided Coarse-to-fine Correspondence Prediction |
|
|
| Weights for the spconv port of OCFNet, trained on 3DMatch for 150 epochs. |
| Code: https://github.com/gfmei/OCFNet |
|
|
| ## Model |
|
|
| Coarse-to-fine registration over stride-8 super-points and their disjoint Voronoi patches, |
| with log-domain Sinkhorn at both levels. This checkpoint adds, over the published model: |
|
|
| - three rounds of interleaved self/cross attention at the coarse level (the published port |
| used one), with a 3D rotary position embedding on the super-point voxel indices; |
| - an overlap-aware circle loss on the super-point features, alongside the transport loss. |
|
|
| Uniform transport marginals with the dustbin on at both levels; the overlap head is trained |
| with the coarse and fine overlap losses but does not drive the marginals. |
|
|
| 10.11 M parameters. |
|
|
| ## Results |
|
|
| 3DMatch and 3DLoMatch, 1000 sampled correspondences, correspondence-based RANSAC. RR is |
| registration recall, IR the inlier ratio, FMR the feature match recall (percentages); RRE is |
| the mean median rotation error in degrees, RTE the mean median translation error in metres. |
|
|
| | benchmark | RR | IR | FMR | RRE | RTE | |
| | --- | --- | --- | --- | --- | --- | |
| | 3DMatch | 89.1 | 69.8 | 96.4 | 2.25 | 0.071 | |
| | 3DLoMatch | 58.6 | 35.1 | 78.0 | 3.31 | 0.098 | |
|
|
| For reference, the published numbers are 90.2 / 58.7 / 98.5 on 3DMatch and 66.7 / 29.5 / |
| 84.0 on 3DLoMatch. Inlier ratio here is well above the published model on both benchmarks; |
| 3DLoMatch registration recall is below it. Two reasons, neither hidden: the backbone is |
| spconv rather than MinkowskiEngine and the two engines build sparse-convolution kernel maps |
| and handle submanifold layers differently, so this is not the same function even at |
| identical weights; and 21% of 3DLoMatch pairs fail at coarse patch selection, producing |
| almost no correct correspondences (coarse inlier ratio 0.008 against 0.477 for the rest). |
| Substituting ground-truth patch pairs takes that fraction to 0 and registration recall to |
| 78.9%, so the limit is coarse selection rather than the fine features. The repository |
| documents this and the interventions that did not move it. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| state = torch.load('ocfnet_3dmatch.pth', map_location='cpu', weights_only=False) |
| model.load_state_dict(state['state_dict']) # 166 tensors, epoch 149 |
| ``` |
|
|
| Or point a test config at it and run the repository's evaluation: |
|
|
| ```shell |
| # configs/test/ocfnet_geo.yaml, field misc.pretrain |
| BENCH=3DLoMatch sbatch scripts/slurm_eval_sweep.sh configs/test/ocfnet_geo.yaml |
| ``` |
|
|
| `config.yaml` is the training configuration this checkpoint was produced with. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{mei2022overlap, |
| title = {Overlap-guided Coarse-to-fine Correspondence Prediction for Point Cloud Registration}, |
| author = {Mei, Guofeng and Huang, Xiaoshui and Zhang, Juan and Wu, Qiang}, |
| booktitle = {IEEE International Conference on Multimedia and Expo (ICME)}, |
| year = {2022} |
| } |
| ``` |
|
|
