Stochastic Normal Orientation for Point Clouds

doi:10.1145/3687944

Stochastic Normal Orientation for Point Clouds

2024-11-19

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DOI：10.1145/3687944

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Abstract

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We propose a simple yet effective method to orient normals for point clouds. Central to our approach is a novel optimization objective function defined from global and local perspectives. Globally, we introduce a signed uncertainty function that distinguishes the inside and outside of the underlying surface. Moreover, benefiting from the statistics of our global term, we present a local orientation term instead of a global one. The optimization problem can be solved by the commonly used numerical optimization solver, such as L-BFGS. The capability and feasibility of our approach are demonstrated over various complex point clouds. We achieve higher practical robustness and normal quality than the state-of-the-art methods.

Keywords:

normal orientation

Gaussian processes

surface reconstruction

Stochastic Normal Orientation for Point Clouds

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