Orienting Point Clouds with Dipole Propagation

• URL: http://arxiv.org/abs/2105.01604v1
• Date: Tue, 4 May 2021 16:25:36 GMT
• Title: Orienting Point Clouds with Dipole Propagation
• Authors: Gal Metzer, Rana Hanocka, Denis Zorin, Raja Giryes, Daniele Panozzo, Daniel Cohen-Or
• Abstract summary: We introduce a novel approach for establishing a globally consistent normal orientation for point clouds. In the local phase, we train a neural network to learn a coherent normal direction per patch. In the global phase, we propagate the orientation across all coherent patches using a dipole propagation.
• Score: 105.4057234622909
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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