Multi-view Point Cloud Registration Using Affine Shape Distributions

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• 作者：Jia Du (17)
  Wei Xiong (17)
  Wenyu Chen (17)
  Jierong Cheng (17)
  Yue Wang (17)
  Ying Gu (17)
  Shue-Ching Chia (17)
  
  17. Visual Computing Department ; Institute for Infocomm Research ; Singapore ; Singapore
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9004
• 期：1
• 页码：147-161
• 全文大小：566 KB
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• 作者单位：Computer Vision -- ACCV 2014
• 丛书名：978-3-319-16807-4
• 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

Registration is crucial for the reconstruction of multi-view single plane illumination microscopy. By using fluorescent beads as fiduciary markers, this registration problem can be reduced to the problem of point clouds registration. We present a novel method for registering point clouds across views. This is based on a new local geometric descriptor - affine shape distribution - to represent the random spatial pattern of each point and its neighbourhood. To enhance its robustness and discriminative power against the missing data and outliers, a permutation and voting scheme based on affine shape distributions is developed to establish putative correspondence pairs across views. The underlying affine transformations are estimated based on the putative correspondence pairs via the random sample consensus. The proposed method is evaluated on three types of datasets including 3D random points, benchmark datasets and datasets from multi-view microscopy. Experiments show that the proposed method outperforms the state-of-the-arts when both point sets are contaminated by extremely large amount of outliers. Its robustness against the anisotropic z-stretching is also demonstrated in the registration of multi-view microscopy data.