Point Cloud Compression

Point cloud compression aims to reduce the size of 3D point cloud data while preserving its quality for both human viewing and machine processing. Current research focuses on developing efficient compression algorithms, often leveraging deep learning models such as autoencoders, diffusion models, and transformers, and incorporating techniques like octree structures, implicit neural representations, and context-aware prediction to improve compression ratios and reconstruction fidelity. This field is crucial for enabling the efficient storage, transmission, and processing of large-scale 3D data in applications ranging from augmented reality and autonomous driving to digital twin systems and scientific visualization.