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|a 10.1109/TPAMI.2024.3462938
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|a eng
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1 |
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|a Wang, Jianqiang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding - Part I
|b Geometry
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|c 2024
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|a Text
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|a Date Revised 20.09.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a A universal multiscale conditional coding framework, Unicorn, is proposed to compress the geometry and attribute of any given point cloud. Geometry compression is addressed in Part I of this paper, while attribute compression is discussed in Part II. We construct the multiscale sparse tensors of each voxelized point cloud frame and properly leverage lower-scale priors in the current and (previously processed) temporal reference frames to improve the conditional probability approximation or content-aware predictive reconstruction of geometry occupancy in compression. Unicorn is a versatile, learning-based solution capable of compressing static and dynamic point clouds with diverse source characteristics in both lossy and lossless modes. Following the same evaluation criteria, Unicorn significantly outperforms standard-compliant approaches like MPEG G-PCC, V-PCC, and other learning-based solutions, yielding state-of-the-art compression efficiency while presenting affordable complexity for practical implementations
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Xue, Ruixiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Jiaxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ding, Dandan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lin, Yi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ma, Zhan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g PP(2024) vom: 17. Sept.
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|u http://dx.doi.org/10.1109/TPAMI.2024.3462938
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