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Sim, Jae-Young
Visual Information Processing Lab (VIP Lab)
Research Interests
  • Image processing, computer vision, 3D visual processing, signal processing

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Large-Scale 3D Point Cloud Compression Using Adaptive Radial Distance Prediction in Hybrid Coordinate Domains

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Title
Large-Scale 3D Point Cloud Compression Using Adaptive Radial Distance Prediction in Hybrid Coordinate Domains
Author
Ahn, Jae-KyunLee, Kyu-YulSim, Jae-YoungKim, Chang-Su
Issue Date
2015-04
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, v.9, no.3, pp.422 - 434
Abstract
An adaptive range image coding algorithm for the geometry compression of large-scale 3D point clouds (LS3DPCs) is proposed in this work. A terrestrial laser scanner generates an LS3DPC by measuring the radial distances of objects in a real world scene, which can be mapped into a range image. In general, the range image exhibits different characteristics from an ordinary luminance or color image, and thus the conventional image coding techniques are not suitable for the range image coding. We propose a hybrid range image coding algorithm, which predicts the radial distance of each pixel using previously encoded neighbors adaptively in one of three coordinate domains: range image domain, height image domain, and 3D domain. We first partition an input range image into blocks of various sizes. For each block, we apply multiple prediction modes in the three domains and compute their rate-distortion costs. Then, we perform the prediction of all pixels using the optimal mode and encode the resulting prediction residuals. Experimental results show that the proposed algorithm provides significantly better compression performance on various range images than the conventional image or video coding techniques
URI
https://scholarworks.unist.ac.kr/handle/201301/11534
URL
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6955830
DOI
10.1109/JSTSP.2014.2370752
ISSN
1932-4553
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AI_Journal Papers
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