Transform Coding for Point Clouds Using a Gaussian Process Model.

Area of Science:

• Computer Vision
• Signal Processing
• Machine Learning

Background:

• 3D point clouds are complex data structures requiring efficient compression techniques.
• Existing methods struggle with accurately modeling the statistical properties of signals on point clouds.
• Gaussian processes (GPs) offer a powerful framework for modeling data with spatial correlations.

Purpose of the Study:

• To develop a novel transform coding method for 3D point cloud color compression.
• To leverage Gaussian processes (GPs) and Gaussian process transforms (GPTs) for enhanced compression.
• To achieve superior compression performance compared to existing methods.

Main Methods:

• Modeling point cloud signal statistics using stationary Gaussian processes (GPs).
• Utilizing Gaussian process transforms (GPTs), Karhunen-Loève transforms of GPs, as the transform coding basis.
• Implementing a transform coder that blocks point clouds, applies GPTs, and uses entropy coding for quantized coefficients.
• Deriving GPTs from GP covariance functions and point locations, with parameters transmitted as side information.
• Employing arithmetic coding with bin-dependent Laplacian models for quantized coefficients.

Main Results:

• The proposed GPT-based transform coding method demonstrates superior compression performance on various datasets.
• Effective modeling of signal statistics on 3D point clouds using stationary GPs.
• Efficient encoding of quantized coefficients through sorting by eigenvalues and arithmetic coding.

Conclusions:

• Gaussian process transforms (GPTs) provide an effective basis for transform coding 3D point cloud colors.
• The proposed method achieves significant improvements in compression efficiency for 3D point cloud data.
• This approach offers a promising direction for future research in point cloud compression.