A Feature-Preserving Filtering Algorithm for Point Set Surface and Surface Attributes Based on Robust Statistics
HONG-XING QIN, JIE YANG AND YUE-MIN ZHU+ Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai, 200240 China +INSA-Lyon, Villeurbanne, F-69621, France Universite de Lyon Lyon, F-69003, France
With the increasing use of three-dimensional (3D) scanning tools and corresponding growth in the number and complexity of scanned objects or models, there is an increasing need in the development of robust and efficient processing techniques for scanned raw data, also referred to as point set surface and surface attributes. We present a features-preserving filtering algorithm for point set surface and surface attributes. The proposed approach is based on robust statistics, by constructing robust prediction framework for first-order estimation of points and surface attributes. Experiments show that the proposed approach preserves the sharp features and the edges of surface attributes while smoothing point set surface and corresponding attributes.