Parallel point cloud compression using truncated octree
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/146239 |
| _version_ | 1811693292892454912 |
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| author | Koh, Naimin Jayaraman, Pradeep Kumar Zheng, Jianmin |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Koh, Naimin Jayaraman, Pradeep Kumar Zheng, Jianmin |
| author_sort | Koh, Naimin |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:49:22Z |
| format | Conference Paper |
| id | ntu-10356/146239 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:49:22Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1462392021-02-03T05:58:59Z Parallel point cloud compression using truncated octree Koh, Naimin Jayaraman, Pradeep Kumar Zheng, Jianmin School of Computer Science and Engineering 2020 International Conference on Cyberworlds (CW) Computer Graphics Geometric Modeling Unstructured point cloud Compression Octree Morton code Parallel processing 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. Ministry of Education (MOE) National Research Foundation (NRF) Accepted version This research was supported by National Research Foundation (NRF) Singapore, under its Virtual Singapore (VSG) Programme (Award No. NRF2015VSG-AA3DCM001-018). It was also supported by the Ministry of Education, Singapore, under its MoE Tier-2 Grant (MoE 2017-T2-1-076). 2021-02-03T05:58:59Z 2021-02-03T05:58:59Z 2020 Conference Paper 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 https://hdl.handle.net/10356/146239 10.1109/CW49994.2020.00009 1 8 en NRF2015VSG-AA3DCM001-018 MoE 2017-T2-1-076 © 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. application/pdf |
| spellingShingle | Computer Graphics Geometric Modeling Unstructured point cloud Compression Octree Morton code Parallel processing Koh, Naimin Jayaraman, Pradeep Kumar Zheng, Jianmin Parallel point cloud compression using truncated octree |
| title | Parallel point cloud compression using truncated octree |
| title_full | Parallel point cloud compression using truncated octree |
| title_fullStr | Parallel point cloud compression using truncated octree |
| title_full_unstemmed | Parallel point cloud compression using truncated octree |
| title_short | Parallel point cloud compression using truncated octree |
| title_sort | parallel point cloud compression using truncated octree |
| topic | Computer Graphics Geometric Modeling Unstructured point cloud Compression Octree Morton code Parallel processing |
| url | https://hdl.handle.net/10356/146239 |
| work_keys_str_mv | AT kohnaimin parallelpointcloudcompressionusingtruncatedoctree AT jayaramanpradeepkumar parallelpointcloudcompressionusingtruncatedoctree AT zhengjianmin parallelpointcloudcompressionusingtruncatedoctree |