IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
This work addresses the challenge of compressing large point clouds for 3D content representation, offering incremental improvements to existing patch-based methods.
The paper tackles the problem of efficiently compressing point cloud geometry for applications like virtual reality and autonomous driving by proposing improvements to a patch-based deep autoencoder, resulting in state-of-the-art rate-distortion performance on sparse and large-scale point clouds while maintaining short compression times.
Point cloud is a crucial representation of 3D contents, which has been widely used in many areas such as virtual reality, mixed reality, autonomous driving, etc. With the boost of the number of points in the data, how to efficiently compress point cloud becomes a challenging problem. In this paper, we propose a set of significant improvements to patch-based point cloud compression, i.e., a learnable context model for entropy coding, octree coding for sampling centroid points, and an integrated compression and training process. In addition, we propose an adversarial network to improve the uniformity of points during reconstruction. Our experiments show that the improved patch-based autoencoder outperforms the state-of-the-art in terms of rate-distortion performance, on both sparse and large-scale point clouds. More importantly, our method can maintain a short compression time while ensuring the reconstruction quality.