Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anisotropic diffusion of surfaces and functions on surfaces
ACM Transactions on Graphics (TOG)
A simple algorithm for surface denoising
Proceedings of the conference on Visualization '01
Theoretical Foundations of Anisotropic Diffusion in Image Processing
Proceedings of the 7th TFCV on Theoretical Foundations of Computer Vision
Non-iterative, feature-preserving mesh smoothing
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2003 Papers
Geometric surface processing via normal maps
ACM Transactions on Graphics (TOG)
Smart Nonlinear Diffusion: A Probabilistic Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smoothing by Example: Mesh Denoising by Averaging with Similarity-Based Weights
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Processing textured surfaces via anisotropic geometric diffusion
IEEE Transactions on Image Processing
Surface mesh denoising with normal tensor framework
Graphical Models
Mesh denoising via L0 minimization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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In this paper, we present a probabilistic approach for 3D object's smoothing. The core idea behind the proposed method is to relate the problem of smoothing objects to that of tracking the transition probability density functions of an underlying random process. We show that such an approach allows for additional insight and sufficient flexibility compared with existing standard smoothing techniques. In particular, we are able to propose a newer, faster, and simpler smoothing approach that retains and enhances important manifold features. Furthermore, it is demonstrated to improve performance over existing smoothing techniques.