Variable Rate Compression for Raw 3D Point Clouds

TL;DR

This paper introduces a new deep learning-based variable rate compression method for raw 3D point clouds that maintains data integrity, adapts to different bitrates, and outperforms existing techniques.

Contribution

It presents a novel architecture capable of compressing raw point clouds at multiple rates without multiple models or data loss from voxelization.

Findings

Achieves state-of-the-art compression performance.

Operates efficiently directly on raw point cloud data.

Handles varying densities without performance degradation.

Abstract

In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.