Orienting point clouds with dipole propagation

Gal Metzer, Rana Hanocka, Denis Zorin, Raja Giryes, Daniele Panozzo, Daniel Cohen-Or

Research output: Contribution to journalArticlepeer-review

Abstract

Establishing a consistent normal orientation for point clouds is a notoriously difficult problem in geometry processing, requiring attention to both local and global shape characteristics. The normal direction of a point is a function of the local surface neighborhood; yet, point clouds do not disclose the full underlying surface structure. Even assuming known geodesic proximity, calculating a consistent normal orientation requires the global context. In this work, we introduce a novel approach for establishing a globally consistent normal orientation for point clouds. Our solution separates the local and global components into two different sub-problems. In the local phase, we train a neural network to learn a coherent normal direction per patch (i.e., consistently oriented normals within a single patch). In the global phase, we propagate the orientation across all coherent patches using a dipole propagation. Our dipole propagation decides to orient each patch using the electric field defined by all previously orientated patches. This gives rise to a global propagation that is stable, as well as being robust to nearby surfaces, holes, sharp features and noise.

Original languageEnglish
Article number165
JournalACM Transactions on Graphics
Volume40
Issue number4
DOIs
StatePublished - 1 Jul 2021

Keywords

  • geometric deep learning
  • point clouds
  • surface reconstruction

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