Abstract
3D point cloud has been widely applied in virtual reality and augmented reality. A complex 3D scene always needs a large number of the point cloud to represent and demands a lot of space to store. Thus, point cloud compression becomes a crucial issue to research. In this paper, we propose a novel lossy geometric compression method of autoencoder based on DCGAN optimization. This method can reconstruct a high-quality point cloud and solves a large area of missing points in the process of compression and decompression. To improve the point cloud codec performance, we propose a multi-scale 3D deconvolution hopping connection structure to obtain a better-quality reconstructed point cloud under low bit rates. Our approach is the first GAN-based point cloud compression algorithm to our knowledge. Compared with state-of-the-art methods on the MVUB dataset, our approach achieves a better rate-distortion performance and visual quality.