2025/11/06 by Manuel Weiß, Lukas Baumgärtner, Weiß, Manuel +5
Computer Science · Engineering · #3D Shape Modeling and Analysis #Advanced Numerical Analysis Techniques #Image denoising #Image segmentation #Medical Image Segmentation Techniques #Noise reduction #Normal #Pattern recognition (psychology) #Regularization (linguistics) #Segmentation #Similarity (geometry) #Total variation denoising
paper · pdf · open access · doi:10.48550/arxiv.2511.04848
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/11/06 · openalex created_date 2025/11/11 · openalex updated_date 2026/07/28
We introduce a new paradigm for geometry denoising using prior knowledge about the surface normal vector. This prior knowledge comes in the form of a set of preferred normal vectors, which we refer to as label vectors. A segmentation problem is naturally embedded in the denoising process. The segmentation is based on the similarity of the normal vector to the elements of the set of label vectors. Regularization is achieved by a total variation term. We formulate a split Bregman (ADMM) approach to solve the resulting optimization problem. The vertex update step is based on second-order shape calculus. We present various examples including the denoising of an eroded medieval gravestone inscription.