Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Gauss-Markov Measure Field Models for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normal vector voting: crease detection and curvature estimation on large, noisy meshes
Graphical Models - Special issue: Processing on large polygonal meshes
Suggestive contours for conveying shape
ACM SIGGRAPH 2003 Papers
Ridge-valley lines on meshes via implicit surface fitting
ACM SIGGRAPH 2004 Papers
Estimating Curvatures and Their Derivatives on Triangle Meshes
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Fast and robust detection of crest lines on meshes
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Point-based multiscale surface representation
ACM Transactions on Graphics (TOG)
Extracting 3D Shape Features in Discrete Scale-Space
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Apparent ridges for line drawing
ACM SIGGRAPH 2007 papers
Fast and Faithful Geometric Algorithm for Detecting Crest Lines on Meshes
PG '07 Proceedings of the 15th Pacific Conference on Computer Graphics and Applications
Demarcating curves for shape illustration
ACM SIGGRAPH Asia 2008 papers
Vertex-Based Diffusion for 3-D Mesh Denoising
IEEE Transactions on Image Processing
3D digitization and its applications in cultural heritage
EuroMed'10 Proceedings of the Third international conference on Digital heritage
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Motivated by multi-scale edge detection in images, a novel multi-scale approach is presented to detect creases on 3D meshes. In this paper, we propose a probabilistic method to select local scales in the discrete 3D scale space. The likelihood function of local scale at each vertex is defined based on the minimum description length (MDL) principle. By introducing some prior knowledge, the optimal local scales are selected using Bayes rule. Therefore, the distribution of selected local scales is piecewise constant and discontinuity adaptive. The discrete 3D multi-scale representation of a given mesh can be constructed using an anisotropic diffusion method. With the selected scales, creases are traced by connecting the curvature extrema points detected on the mesh edges. Experimental results show that geometrically salient creases are well detected on noisy meshes using our method.