Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146239
Title: Parallel point cloud compression using truncated octree
Authors: Koh, Naimin
Jayaraman, Pradeep Kumar
Zheng, Jianmin
Keywords: Computer Graphics
Geometric Modeling
Issue Date: 2020
Source: Koh, N., Jayaraman, P. K., & Zheng, J. (2020). Parallel point cloud compression using truncated octree. Proceedings of the 2020 International Conference on Cyberworlds (CW), 1-8. doi:10.1109/CW49994.2020.00009
Project: NRF2015VSG-AA3DCM001-018
MoE 2017-T2-1-076
Abstract: Existing methods of unstructured point cloud compression usually exploit the spatial sparseness of point clouds using hierarchical tree data structures for spatial encoding. However, such methods can be inefficient when very deep octrees are applied to sparse point cloud data to maintain low level of geometric error during compression. This paper proposes a novel octree structure called truncated octree that improves the compression ratio by representing the deep octree with a set of shallow sub-octrees which can save storage without losing the original structure. We also propose a variable length addressing scheme, to adaptively choose the length of an octree’s node address based on the truncation level—shorter (resp. longer) address when octree is truncated near the leaf (resp. root) which leads to further compression. The method is able to achieve 40% to 90% compression ratio on our tested models for point clouds of different spatial distributions. For extremely sparse point clouds, the method achieves approximately 7 times higher compression ratio than previous methods. Moreover, the method is designed to run in parallel for octree construction, encoding and decoding.
URI: https://hdl.handle.net/10356/146239
DOI: 10.1109/CW49994.2020.00009
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW49994.2020.00009.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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