Point Cloud Compression with Implicit Neural Representations: A Unified Framework

Understand

Point clouds have become increasingly vital across various applications thanks to their ability to realistically depict 3D objects and scenes.

• Nevertheless, effectively compressing unstructured, high-precision point cloud data remains a significant challenge.
• In this paper, we present a pioneering point cloud compression framework capable of handling both geometry and attribute components.
• Unlike traditional approaches and existing learning-based methods, our framework utilizes two coordinate-based neural networks to implicitly represent a voxelized point cloud.