Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression

Abstract

Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions. In this paper, we present a novel data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization. We perform joint optimization of both rate and distortion using a trade-off parameter. In addition, we cast the decoding process as a binary classification of the point cloud occupancy map. Our method outperforms the MPEG reference solution in terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset with 51.5% BDBR savings on average. Moreover, while octree-based methods face exponential diminution of the number of points at low bitrates, our method still produces high resolution outputs even at low bitrates. Code and supplementary material are available at https://github.com/mauriceqch/pcc_geo_cnn .

Published in ICIP 2019. The source code can be found at https://github.com/mauriceqch/pcc_geo_cnn and the supplementary material can be found at https://www.mauricequach.com/pcc_geo_cnn_samples

References in corpus (3)

• Variational image compression with a scale hyperprior
• End-to-end Optimized Image Compression
• A Volumetric Approach to Point Cloud Compression

Cited by in corpus (10)

• Learned Point Cloud Geometry Compression
• GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression
• Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model
• HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media
• Attribute Artifacts Removal for Geometry-based Point Cloud Compression
• Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss
• Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
• Geometric Prior Based Deep Human Point Cloud Geometry Compression
• The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machine
• 3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods