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Three-dimensional point cloud data is one of the most extensively used data representation today, favored in various fields for its realistic and lifelike visual effects. However, the substantial volume of data poses significant challenges for storage and transmission. To advance point cloud compression (PCC) technology, we develop a learning-based PCC algorithm library, namely LearningPCC. To our knowledge, this is the first comprehensive set of algorithms that is compatible with all types of point cloud data. This PyTorch library incorporates eleven learning-based algorithms that address both geometry and attribute compression of point cloud data. We categorize the existing methods into six main classes and thoroughly introduce and analyze the principles of these algorithms. Moreover, we conduct performance evaluations using point clouds with various densities, offering detailed test results on several compression metrics, such as RD curves, BD-BR gains, compression ratio improvements, and encoding times. We will provide researchers with convenient access to these methods, replicate codes, and experiment results. Our commitment includes maintaining and updating these algorithms to offer researchers the latest in compression technologies.
Xie et al. (Sat,) studied this question.