J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911253548498944 |
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| author | Talha, Muhammad Yang, Qi Li, Zhu Akhtar, Anique Van Der Auwera, Geert |
| author_facet | Talha, Muhammad Yang, Qi Li, Zhu Akhtar, Anique Van Der Auwera, Geert |
| contents | Point clouds (PC) are essential for AR/VR and autonomous driving but challenge compression schemes with their size, irregular sampling, and sparsity. MPEG's Geometry-based Point Cloud Compression (GPCC) methods successfully reduce bitrate; however, they introduce significant blocky artifacts in the reconstructed point cloud. We introduce a novel multi-scale postprocessing framework that fuses graph-Fourier latent attribute representations with sparse convolutions and channel-wise attention to efficiently deblock reconstructed point clouds. Against the GPCC TMC13v14 baseline, our approach achieves BD-rate reduction of 18.81\% in the Y channel and 18.14\% in the joint YUV on the 8iVFBv2 dataset, delivering markedly improved visual fidelity with minimal overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05047 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking Talha, Muhammad Yang, Qi Li, Zhu Akhtar, Anique Van Der Auwera, Geert Image and Video Processing Point clouds (PC) are essential for AR/VR and autonomous driving but challenge compression schemes with their size, irregular sampling, and sparsity. MPEG's Geometry-based Point Cloud Compression (GPCC) methods successfully reduce bitrate; however, they introduce significant blocky artifacts in the reconstructed point cloud. We introduce a novel multi-scale postprocessing framework that fuses graph-Fourier latent attribute representations with sparse convolutions and channel-wise attention to efficiently deblock reconstructed point clouds. Against the GPCC TMC13v14 baseline, our approach achieves BD-rate reduction of 18.81\% in the Y channel and 18.14\% in the joint YUV on the 8iVFBv2 dataset, delivering markedly improved visual fidelity with minimal overhead. |
| title | J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2511.05047 |