ML Anthology

Deep Global Registration

Christopher Choy, Wei Dong, Vladlen Koltun
CVPR 2020
doi:10.1109/CVPR42600.2020.00259 /cvpr/2020/choy2020cvpr-deep/

Abstract

We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estimation, and a robust gradient-based SE(3) optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data.

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Cite

Text

Choy et al. "Deep Global Registration." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00259

Markdown

[Choy et al. "Deep Global Registration." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/choy2020cvpr-deep/) doi:10.1109/CVPR42600.2020.00259

BibTeX

@inproceedings{choy2020cvpr-deep,
  title     = {{Deep Global Registration}},
  author    = {Choy, Christopher and Dong, Wei and Koltun, Vladlen},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00259},
  url       = {https://mlanthology.org/cvpr/2020/choy2020cvpr-deep/}
}