TR2024-106
Point Cloud Geometry Compression using Parameterized Graph Fourier Transform
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- "Point Cloud Geometry Compression using Parameterized Graph Fourier Transform", ACM SIGCOMM, DOI: 10.1145/3672196.367340, August 2024, pp. 52-57.BibTeX TR2024-106 PDF
- @inproceedings{Kirihara2024aug,
- author = {Kirihara, Hinata and Ibuki, Shoichi and Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi}},
- title = {Point Cloud Geometry Compression using Parameterized Graph Fourier Transform},
- booktitle = {ACM SIGCOMM},
- year = 2024,
- pages = {52--57},
- month = aug,
- publisher = {ACM},
- doi = {10.1145/3672196.367340},
- url = {https://www.merl.com/publications/TR2024-106}
- }
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- "Point Cloud Geometry Compression using Parameterized Graph Fourier Transform", ACM SIGCOMM, DOI: 10.1145/3672196.367340, August 2024, pp. 52-57.
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Abstract:
Existing point cloud coding (PCC) methods based on graph signal processing (GSP) have been proposed for dealing with the irregular structures of 3D points. GSP-based PCC can achieve better rate-distortion performance against the typical tree-based PCC, whereas it requires computational resources for searching hyperparameter sets for graph shift operators and performing eigenvalue decomposition for each parameter set. This paper proposes a novel PCC to reduce the time complexity without the degradation of rate- distortion performance. Specifically, we leverage the properties of the parameterized graph shift operator to realize 1) a reduction in the number of hyperparameters, and 2) a decomposition-free hyperparameter search. Evaluations using ShapeNet point cloud dataset show that the proposed scheme achieves almost the same rate-distortion performance with significant reduction on the computational cost compared to the existing graph-based PCCs.