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Global scale integral volumes

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Details

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2016 Workshops, Proceedings
PublisherSpringer International Publishing
Pages192-204
Number of pages13
Volume9913
ISBN (Print)9783319466033
DOIs
Publication statusPublished - 2016
Publication typeA4 Article in a conference publication
EventEuropean Conference on Computer Vision -
Duration: 1 Jan 1900 → …

Publication series

NameLecture Notes in Computer Science
Volume9913
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Computer Vision
Period1/01/00 → …

Abstract

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 O(log n), here n is the number of nodes in the octree. We evaluate our proposed method on multiresolution 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.

Keywords

  • Integral volume, LiDAR, Octree, Point cloud

Publication forum classification

Field of science, Statistics Finland