Orienting Point Clouds with Dipole Propagation
This addresses a challenging geometry processing problem for applications like 3D modeling and computer vision, but it appears incremental as it builds on existing concepts with a novel propagation technique.
The paper tackles the problem of establishing a globally consistent normal orientation for point clouds, which is difficult due to local and global shape characteristics, by introducing a method that separates local and global components, resulting in a stable and robust propagation.
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.