Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

@article{Hermosilla2019TotalDU,
  title={Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning},
  author={P. Hermosilla and T. Ritschel and T. Ropinski},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages={52-60}
}
  • P. Hermosilla, T. Ritschel, T. Ropinski
  • Published 2019
  • Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. [...] Key Method Overcoming this, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on a manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.Expand Abstract
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