Geometric Prior Based Deep Human Point Cloud Geometry Compression
This work addresses the problem of efficient data storage and transmission for digital avatars, which is crucial for applications like virtual reality and gaming, though it is incremental as it builds on existing learning-based compression methods.
The paper tackles the challenge of compressing high-resolution human point clouds with millions of points by leveraging human geometric priors to remove redundancy, resulting in significant compression performance improvements without quality loss.
The emergence of digital avatars has raised an exponential increase in the demand for human point clouds with realistic and intricate details. The compression of such data becomes challenging with overwhelming data amounts comprising millions of points. Herein, we leverage the human geometric prior in geometry redundancy removal of point clouds, greatly promoting the compression performance. More specifically, the prior provides topological constraints as geometry initialization, allowing adaptive adjustments with a compact parameter set that could be represented with only a few bits. Therefore, we can envisage high-resolution human point clouds as a combination of geometric priors and structural deviations. The priors could first be derived with an aligned point cloud, and subsequently the difference of features is compressed into a compact latent code. The proposed framework can operate in a play-and-plug fashion with existing learning based point cloud compression methods. Extensive experimental results show that our approach significantly improves the compression performance without deteriorating the quality, demonstrating its promise in a variety of applications.