Mesh Denoising Using Multi-scale Curvature-Based Saliency

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• 作者：Somnath Dutta (15)
  Sumandeep Banerjee (15)
  Prabir K. Biswas (15)
  Partha Bhowmick (16)
  
  15. Department of Electronics and Electrical Communication Engineering ; Indian Institute of Technology ; Kharagpur ; India
  16. Department of Computer Science and Engineering ; Indian Institute of Technology ; Kharagpur ; India
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9009
• 期：1
• 页码：507-516
• 全文大小：2,292 KB
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• 作者单位：Computer Vision - ACCV 2014 Workshops
• 丛书名：978-3-319-16630-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

3D mesh data acquisition is often afflicted by undesirable measurement noise. Such noise has an aversive impact to further processing and also to human perception, and hence plays a pivotal role in mesh processing. We present here a fast saliency-based algorithm that can reduce the noise while preserving the finer details of the original object. In order to capture the object features at multiple scales, our mesh denoising algorithm estimates the mesh saliency from Gaussian weighted curvatures for vertices at fine and coarse scales. The proposed algorithm finds wide application in digitization of archaeological artifacts, such as statues and sculptures, where it is of paramount importance to capture the 3D surface with all its details as accurately as possible. We have tested the algorithm on several datasets, and the results exhibit its speed and efficiency.