用图傅里叶与稀疏卷积融合，有效消除点云压缩后的块状伪影。
J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking
- 结合图傅里叶域特征与通道注意力，多尺度重建点云属性
- 在8iVFBv2数据集上，Y通道BD-rate降低18.81%，联合YUV降低18.14%
- 适合点云压缩后处理，尤其适用于AR/VR和自动驾驶场景
点云在AR/VR和自动驾驶中至关重要，但其规模大、采样不规则且稀疏，给压缩带来挑战。MPEG的基于几何的点云压缩（GPCC）方法虽有效降低码率，却在重建点云中引入显著块状伪影。本文提出一种新型多尺度后处理框架，融合图傅里叶潜在属性表示、稀疏卷积与通道注意力，高效去除重建点云的块状伪影。相较于GPCC TMC13v14基线，在8iVFBv2数据集上，Y通道实现18.81%的BD-rate降低，联合YUV降低18.14%，显著提升视觉保真度，且开销极小。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。