PUGeo-Net : A Geometry-Centric Network for 3D Point Cloud Upsampling

Yue QIAN, Junhui HOU, Sam KWONG, Ying HE

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

69 Citations (Scopus)

Abstract

In this paper, we propose a novel deep neural network based method, called PUGeo-Net, for upsampling 3D point clouds. PUGeo-Net incorporates discrete differential geometry into deep learning elegantly by learning the first and second fundamental forms that are able to fully represent the local geometry unique up to rigid motion. Specifically, we encode the first fundamental form in a 3×3 linear transformation matrix T for each input point. Such a matrix approximates the augmented Jacobian matrix of a local parameterization that encodes the intrinsic information and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane, so that we can lift the adaptively distributed 2D samples learned from the input to 3D space. After that, we use the learned second fundamental form to compute a normal displacement for each generated sample and project it to the curved surface. As a by-product, PUGeo-Net can compute normals for the original and generated points, which is highly desired for surface reconstruction algorithms. We evaluate PUGeo-Net on a wide range of 3D models with sharp features and rich geometric details and observe that PUGeo-Net consistently outperforms state-of-the-art methods in terms of both accuracy and efficiency for upsampling factor 4∼16. We also verify the geometry-centric nature of PUGeo-Net quantitatively. In addition, PUGeo-Net can handle noisy and non-uniformly distributed inputs well, validating its robustness. The code is publicly available at https://github.com/ninaqy/PUGeo.
Original languageEnglish
Title of host publicationComputer Vision: ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIX
EditorsAndrea VEDALDI, Horst BISCHOF, Thomas BROX, Jan-Michael FRAHM
PublisherSpringer, Cham
Pages752-769
Number of pages8
ISBN (Electronic)9783030585297
ISBN (Print)9783030585280
DOIs
Publication statusPublished - Aug 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China under Grant 61871342, and in part by the Hong Kong Research Grants Council under grants 9048123 (CityU 21211518) and 9042955 (CityU 11202320).

Keywords

  • Deep learning
  • Discrete differential geometry
  • Local parameterization
  • Point clouds
  • Surface reconstruction
  • Upsampling

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