DeepCompress: Efficient Point Cloud Geometry Compression

TL;DR

DeepCompress introduces a novel deep learning architecture for point cloud geometry compression that improves efficiency and reduces computational costs while maintaining comparable quality to existing methods.

Contribution

The paper presents a new encoder architecture for point cloud compression that incorporates learned activation functions and efficient convolutional blocks, outperforming baseline methods in efficiency.

Findings

Reduces encoder convolution operations by 8%

Lowers total encoder parameters by 20%

Maintains similar quality with minimal rate-distortion penalty

Abstract

Point clouds are a basic data type that is increasingly of interest as 3D content becomes more ubiquitous. Applications using point clouds include virtual, augmented, and mixed reality and autonomous driving. We propose a more efficient deep learning-based encoder architecture for point clouds compression that incorporates principles from established 3D object detection and image compression architectures. Through an ablation study, we show that incorporating the learned activation function from Computational Efficient Neural Image Compression (CENIC) and designing more parameter-efficient convolutional blocks yields dramatic gains in efficiency and performance. Our proposed architecture incorporates Generalized Divisive Normalization activations and propose a spatially separable InceptionV4-inspired block. We then evaluate rate-distortion curves on the standard JPEG Pleno 8i Voxelized Full Bodies dataset to evaluate our model's performance. Our proposed modifications outperform the baseline approaches by a small margin in terms of Bjontegard delta rate and PSNR values, yet reduces necessary encoder convolution operations by 8 percent and reduces total encoder parameters by 20 percent. Our proposed architecture, when considered on its own, has a small penalty of 0.02 percent in Chamfer's Distance and 0.32 percent increased bit rate in Point to Plane Distance for the same peak signal-to-noise ratio.