OpenMVC: an Open-Source Library for Learning-based Multi-view Compression.

Amidst the swift advancement of 3D vision technology, Multi-view Compression (MVC) has become a crucial technique, widely applied in fields such as virtual reality, augmented reality, autonomous driving, telemedicine, and security surveillance. The technology effectively handles views from multiple cameras, utilizing the inter-view correlations to compress data efficiently. It substantially decreases the data transmission and storage requirements, enabling a richer and more realistic visual experience within the same bandwidth constraints. To further enhance compression performance, new methods continue to emerge. However, the absence of a unified benchmark testing library capable of effectively evaluating existing algorithms poses significant challenges to the further development of the field and the practical deployment of algorithms. To address this issue, we introduce OpenMVC, an Open-Source Library for Learning-based Multi-view Compression. We provide a comprehensive description and analysis of the performance advantages of existing algorithms. Furthermore, we conduct extensive and comprehensive benchmark testing of nine typical algorithms in the last five years, evaluating them in a consistent environment across various metrics. The open-source library for OpenMVC is available at https://openi.pcl.ac.cn/OpenAICoding/OpenMVC.