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Stochastic Normal Orientation for Point Clouds
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AI
DOI：10.1145/3687944.png)
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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.
Keyword:
normal orientation
Gaussian processes
surface reconstruction
期刊
IF:
9.5
论文数:
4.8K
被引数:
3.6W
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