Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds

TL;DR

This paper introduces a novel autoencoder-based method for lossy geometry compression of point clouds, directly processing raw point cloud data and outperforming existing codecs in efficiency and adaptability.

Contribution

It is the first autoencoder-based geometry compression codec that directly uses point clouds, offering improved flexibility and competitive performance over traditional handcrafted codecs.

Findings

Outperforms MPEG-3DG TMC13 model on categories 1 and 3

Achieves an average 73.15% BD-rate gain

Outperforms existing codecs in efficiency and adaptability

Abstract

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. To the best of our knowledge, it is the first autoencoder-based geometry compression codec that directly takes point clouds as input rather than voxel grids or collections of images. Compared with handcrafted codecs, this approach adapts much more quickly to previously unseen media contents and media formats, meanwhile achieving competitive performance. Our architecture consists of a pointnet-based encoder, a uniform quantizer, an entropy estimation block and a nonlinear synthesis transformation module. In lossy geometry compression of point cloud, results show that the proposed method outperforms the test model for categories 1 and 3 (TMC13) published by MPEG-3DG group on the 125th meeting, and on average a 73.15\% BD-rate gain is achieved.