Global Scale Integral Volumes

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• 关键词：Integral volume ; Octree ; Point cloud ; LiDAR
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9913
• 期：1
• 页码：192-204
• 全文大小：1,369 KB
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• 作者单位：Sounak Bhattacharya (15)
  Lixin Fan (15)
  Pouria Babahajiani (15)
  Moncef Gabbouj (16)
  
  15. Nokia Technologies, Tampere, Finland
  16. Tampere University of Technology, Tampere, Finland
  
• 丛书名：Computer Vision – ECCV 2016 Workshops
• ISBN：978-3-319-46604-0
• 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
• 卷排序：9913

文摘

Integral volume is an important image representation technique, which is useful in many computer vision applications. Processing integral volumes for large scale 3D datasets is challenging due to high memory requirements. The difficulties lie in efficiently computing, storing, querying and updating the integral volume values. In this work, we address the above problems and present a novel solution for processing integral volumes for large scale 3D datasets efficiently. We propose an octree-based method where the worst-case complexity for querying the integral volume of arbitrary regions is \(\mathcal {O}(\log {}n)\), here n is the number of nodes in the octree. We evaluate our proposed method on multi-resolution LiDAR point cloud data. Our work can serve as a tool to fast extract features from large scale 3D datasets, which can be beneficial for computer vision applications.