Anisotropic Denoising of 3D Point Clouds by Aggregation of Multiple Surface-Adaptive Estimates

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.



Point Cloud Denoising Software
for Matlab (R2019b or later)

download zip package

16.1-MB zip file

v1.0.1, released January 2nd, 2020

Read me
Information and instructions

Point Cloud Denoising Softwarenew

for Python (>=3.6)

download zip package

20.0-MB zip file

v1.0.4, released July 5th, 2021
also available on the Python Package Index (PyPI): pip install anisofilter

Read me
Information and instructions

Any unauthorized use of the provided software and files for industrial or profit-oriented activities is expressively prohibited. By downloading any of the files contained in this site, you implicitly agree to all the terms of the TAU limited license. Please read the enclosed legal notice before you proceed with downloading any of the files.

People People

  Zhongwei Xu
  Alessandro Foi

References References

Z. Xu and A. Foi, “Anisotropic Denoising of 3D Point Clouds by Aggregation of Multiple Surface-Adaptive Estimates”, IEEE Trans. Visualization and Computer Graphics, 2019.  DOI

back to main page