A parameter-free method for laser point cloud denoising based on texture information

Point cloud denoising is a crucial step in the processing of 3D data. Although model-based methods for point cloud denoising have seen some success, their performance often remains inconsistent due to the complex parameter selection required for objects with varying shapes. To address this challenge, we propose a new method called TPDn, which takes full advantage of the object geometry to select parameters automatically. After converting point clouds into a triangular mesh, TPDn defines two key textures: the normalized mesh size and the mesh normal. By fusing these two texture features, a global distribution function is established. TPDn adaptively determines the appropriate threshold by deriving it from a simple approximation function.