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arXiv · 2511.05047

J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking

Muhammad Talha·Qi Yang·Zhu Li·Anique Akhtar·Geert Van Der Auwera
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Abstract

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.

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Muhammad Talha, Qi Yang, Zhu Li, Anique Akhtar, Geert Van Der Auwera. 2025-11-07. J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking. https://arxiv.org/abs/2511.05047

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