Point Cloud Geometry Compression Based on Multi-Layer Residual Structure.

Area of Science:

• Computer Vision
• 3D Data Processing
• Machine Learning

Background:

• 3D point cloud data is crucial for applications like autonomous driving and augmented reality.
• Efficiently compressing large, sparse, and high-dimensional point cloud data remains a significant challenge.
• Existing geometry-based point cloud compression (G-PCC) methods face limitations in achieving high compression ratios while preserving quality.

Purpose of the Study:

• To develop a novel deep-learning framework for efficient geometric compression of 3D point clouds.
• To address the storage burden associated with detailed 3D point cloud data.
• To improve upon the performance of state-of-the-art point cloud compression techniques.

Main Methods:

• A deep-learning framework utilizing an autoencoder architecture for point cloud geometric compression.
• Implementation of a multi-layer residual module within sparse convolution-based autoencoders.
• Hierarchical down-sampling and reconstruction of point clouds to preserve feature information.

Main Results:

• Achieved over 70-90% BD-Rate gain compared to state-of-the-art G-PCC schemes on object point cloud datasets.
• Demonstrated superior point cloud reconstruction quality.
• Obtained an average BD-Rate gain of approximately 10% compared to PCGCv2.

Conclusions:

• The proposed deep-learning framework offers a highly effective solution for 3D point cloud geometric compression.
• The novel autoencoder architecture significantly reduces data volume while maintaining high reconstruction fidelity.
• This approach represents a substantial advancement in efficient 3D point cloud data handling for various applications.