Learned Point Cloud Geometry Compression

TL;DR

This paper introduces a deep learning-based framework for compressing point cloud geometry efficiently, outperforming traditional methods in both objective metrics and visual quality, with a compact model suitable for embedded devices.

Contribution

The paper proposes a novel end-to-end learned point cloud geometry compression method using variational autoencoders, achieving significant performance improvements over MPEG standard algorithms.

Findings

Exceeds MPEG G-PCC with at least 60% BD-Rate gains

Produces smoother surface reconstruction and better visual quality

Requires only about 2.5MB model size

Abstract

This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE). In our approach, PCG is first voxelized, scaled and partitioned into non-overlapped 3D cubes, which is then fed into stacked 3D convolutions for compact latent feature and hyperprior generation. Hyperpriors are used to improve the conditional probability modeling of latent features. A weighted binary cross-entropy (WBCE) loss is applied in training while an adaptive thresholding is used in inference to remove unnecessary voxels and reduce the distortion. Objectively, our method exceeds the geometry-based point cloud compression (G-PCC) algorithm standardized by well-known Moving Picture Experts Group (MPEG) with a significant performance margin, e.g., at least 60% BD-Rate (Bjontegaard Delta Rate) gains, using common test datasets. Subjectively, our method has presented better visual quality with smoother surface reconstruction and appealing details, in comparison to all existing MPEG standard compliant PCC methods. Our method requires about 2.5MB parameters in total, which is a fairly small size for practical implementation, even on embedded platform. Additional ablation studies analyze a variety of aspects (e.g., cube size, kernels, etc) to explore the application potentials of our learned-PCGC.