Normal Estimation via Shifted Neighborhood for Point Cloud

We present a fast and quality normal estimator based on neighborhood shift. Instead of using the neighborhood centered at the point, we wish to locate a neighborhood containing the point but clear of sharp features, which is usually not centering at the point.

Two specific neighborhood shift techniques are designed in view of the complex structure of sharp features and the characteristic of raw point clouds.

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Recommended citation: Junjie Cao, Anka He Chen, Jie Zhang, Yujiao Li, Xiuping Liu, Changqing Zou. (2017). "Normal Estimation via Shifted Neighborhood for Point Cloud." Journal of Computational and Applied Mathematics.
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