Anisotropic Denoising of 3D Point Clouds by Aggregation of Multiple Surface-Adaptive Estimates
Abstract 3D point clouds commonly contain positional errors which can be regarded as noise. We propose a point cloud denoising algorithm based on aggregation of multiple anisotropic estimates computed on local coordinate systems. These local estimates are adaptive to the shape of the surface underlying the point cloud, leveraging an extension of the Local Polynomial Approximation (LPA) - Intersection of Confidence Intervals (ICI) technique to 3D point clouds. The adaptivity due to LPA-ICI is further strengthened by the dense aggregation with data-driven weights. Experimental results demonstrate state-of-the-art restoration quality of both sharp features and smooth areas. Along with this denoising algorithm, we also introduce robust estimators of the noise variance and surface sample density of point clouds. Overall, we present a fully automatic denoising algorithm that can adapt to point-cloud data with unknown sampling density and unknown noise variance.
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