Density-based Denoising of Point Cloud

TL;DR

This paper introduces a density-based method for denoising point clouds that combines particle-swarm optimization, mean-shift clustering, and bilateral mesh filtering to effectively remove noise and outliers, improving surface reconstruction quality.

Contribution

It presents a novel combination of optimization, clustering, and filtering techniques for robust point cloud denoising, with automatic bandwidth selection.

Findings

Robust removal of outliers and noise demonstrated.

Efficient denoising with improved surface reconstruction.

Method outperforms existing approaches in experiments.

Abstract

Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean-shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.