Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement

Sensors
Esmeide LealJohn W Branch

Abstract

Denoising the point cloud is fundamental for reconstructing high quality surfaces with details in order to eliminate noise and outliers in the 3D scanning process. The challenges for a denoising algorithm are noise reduction and sharp features preservation. In this paper, we present a new model to reconstruct and smooth point clouds that combine L1-median filtering with sparse L1 regularization for both denoising the normal vectors and updating the position of the points to preserve sharp features in the point cloud. The L1-median filter is robust to outliers and noise compared to the mean. The L1 norm is a way to measure the sparsity of a solution, and applying an L1 optimization to the point cloud can measure the sparsity of sharp features, producing clean point set surfaces with sharp features. We optimize the L1 minimization problem by using the proximal gradient descent algorithm. Experimental results show that our approach is comparable to the state-of-the-art methods, as it filters out 3D models with a high level of noise, but keeps their geometric features.

References

Aug 11, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Kostadin DabovKaren Egiazarian
Oct 30, 2015·IEEE Transactions on Neural Networks and Learning Systems·Qingshan Liu, Jun Wang
Jan 4, 2020·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jin ZengCheng Yang

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Software Mentioned

MATLAB
BM3D
GLR
MRPCA
EAR
RIMLS
APSS

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