Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering

TL;DR

This paper presents a novel point cloud filtering method that simultaneously preserves geometric features and achieves a more uniform point distribution, outperforming existing methods in accuracy and efficiency.

Contribution

It introduces a combined energy minimization approach with a repulsion term and data term to improve feature preservation and point distribution in filtering.

Findings

Achieves a 5.8×10^{-5} Chamfer Distance on average.

Handles models with fine-scale and sharp features.

Operates efficiently in seconds.

Abstract

As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distribution in the filtered output. To address this problem, this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. The key idea is to incorporate a repulsion term with a data term in energy minimization. The repulsion term is responsible for the point distribution, while the data term is to approximate the noisy surfaces while preserving the geometric features. This method is capable of handling models with fine-scale features and sharp features. Extensive experiments show that our method yields better results with a more uniform point distribution ($5.8\times10^{-5}$ Chamfer Distance on average) in seconds.