AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature

TL;DR

This paper introduces AWCD, a novel point cloud denoising method that leverages Wasserstein curvature to improve structure preservation and noise stability, supported by theoretical analysis and experimental validation.

Contribution

The paper presents the first application of Wasserstein curvature for point cloud denoising, offering a new approach with theoretical insights and superior performance over traditional methods.

Findings

AWCD effectively denoises point clouds with high noise levels.

AWCD outperforms traditional algorithms in preserving data structure.

Theoretical analysis confirms stability and effectiveness of Wasserstein curvature.

Abstract

In this paper, we introduce the adaptive Wasserstein curvature denoising (AWCD), an original processing approach for point cloud data. By collecting curvatures information from Wasserstein distance, AWCD consider more precise structures of data and preserves stability and effectiveness even for data with noise in high density. This paper contains some theoretical analysis about the Wasserstein curvature and the complete algorithm of AWCD. In addition, we design digital experiments to show the denoising effect of AWCD. According to comparison results, we present the advantages of AWCD against traditional algorithms.