AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature
This addresses denoising for point cloud data, likely in computer vision or graphics, but appears incremental as it builds on curvature-based methods.
The paper tackles point cloud denoising by introducing AWCD, an approach using Wasserstein curvature to preserve data structures and handle noise, showing advantages over traditional algorithms in experiments.
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.