Point Cloud Attribute Compression

Point cloud attribute compression focuses on efficiently encoding the non-geometric data (e.g., color, intensity) associated with 3D point clouds, aiming to minimize storage and transmission costs while preserving data fidelity. Current research emphasizes developing learned compression methods, employing architectures like autoregressive models, high-dimensional convolutions, and feedforward networks with geometric attention, often within a volumetric or multiscale framework. These advancements improve compression ratios and coding speed compared to traditional methods, impacting applications such as 3D scene reconstruction, virtual reality, and autonomous driving by enabling more efficient handling of large point cloud datasets.