Attribute Compression

Attribute compression for point clouds aims to efficiently represent the non-geometric data (e.g., color, intensity) associated with 3D points, minimizing storage and transmission costs while preserving visual quality. Current research heavily utilizes deep learning, employing architectures like autoencoders, convolutional neural networks (especially those adapted for irregular point distributions), and graph neural networks to learn efficient representations and predictive models for attribute data. These advancements are crucial for handling the ever-increasing volume of 3D data in applications such as virtual reality, autonomous driving, and 3D modeling, enabling more efficient storage, transmission, and processing of rich 3D scenes.