Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds

Understand

Point cloud is a fundamental 3D representation which is widely used in real world applications such as autonomous driving.

• As a newly-developed media format which is characterized by complexity and irregularity, point cloud creates a need for compression algorithms which are more flexible than existing codecs.
• Recently, autoencoders(AEs) have shown their effectiveness in many visual analysis tasks as well as image compression, which inspires us to employ it in point cloud compression.
• In this paper, we propose a general autoencoder-based architecture for lossy geometry point cloud compression.