  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.
