3D point cloud denoising using anisotropic neighborhoods and a novel sharp feature detection algorithm

3D point cloud denoising is a fundamental task in a geometry-processing pipeline, where feature preservation is essential for various applications. The literature presents several methods to overcome the denoising problem; however, most of them focus on denoising smooth surfaces and not on handling sharp features correctly. This paper proposes a new sharp feature-preserving method for point cloud denoising that incorporates solutions for normal estimation and feature detection. The denoising method consists of four major steps. First, we compute the per-point anisotropic neighborhoods by solving local quadratic optimization problems that penalize normal variation. Second, we estimate a piecewise smooth normal field that enhances sharp feature regions using these anisotropic neighborhoods. This step includes bilateral filtering and a novel corrector procedure to obtain more reliable normals for the subsequent steps. Third, we employ a novel sharp feature detection algorithm to select the feature points precisely. Finally, we update the point positions to fit them to the computed normals while retaining the sharp features that were detected. These steps are repeated until the noise is minimized. We evaluate our method using qualitative and quantitative comparisons with state-of-the-art denoising, normal estimation, and feature detection procedures. Our experiments show that our approach is competitive and, in most test cases, outperforms all other methods.