Overview
Inter-LPCM is a proposed learning-based inter-frame predictive coding method specifically designed for LiDAR point cloud compression. This method aims to improve compression efficiency by addressing limitations in how current techniques handle complex motion patterns and structural dependencies within point cloud data, particularly in the spherical coordinate system. It integrates distinct strategies for azimuth, radius, and elevation angle prediction, alongside a specialized quantization approach and entropy coding models.
Research Context
LiDAR sensors generate point clouds with a fixed angular resolution, which allows for systematic parameterization and efficient compression within the spherical coordinate system. Traditional methods for spherical coordinate-based point cloud compression, such as the predictive geometry coding (PredGeom) method within the geometry-based point cloud compression (G-PCC) standard, have demonstrated notable rate-distortion (RD) performance. However, PredGeom's inter-frame prediction mode relies on a linear model, which restricts its capacity to capture complex motion and structural dependencies. Concurrently, existing learning-based compression methods operating in the spherical domain typically do not utilize inter-frame correlations to reduce geometric redundancy.
Approach
Inter-LPCM was developed to address the identified limitations in LiDAR point cloud compression. The approach involves several distinct components designed to optimize inter-frame prediction and compression in the spherical coordinate system:
- Azimuth Prediction: A delta coding strategy is employed for azimuth prediction, which is based on the predefined angular resolution.
- Radius Prediction: An inter-frame radius predictive (Inter-RP) model is introduced. This model estimates the radius of the current point by referencing neighboring points from both the current frame and a registered reference frame.
- Elevation Angle Prediction: A lightweight attention-based prediction (LAEP) model is designed for predicting elevation angles. This model is engineered to capture long-range geometric correlations across different coordinates.
- Quantization: An RD-optimized method is proposed for selecting quantization steps specifically within the spherical coordinate system.
- Entropy Coding: Distinct entropy models are designed for each component of the spherical coordinate system. These models are adapted to the statistical priors unique to each coordinate, which contributes to more accurate probability estimation.
Findings
The development of Inter-LPCM resulted in a learning-based inter-frame predictive coding method for LiDAR point cloud compression. Key findings include:
- The introduction of a delta coding strategy for azimuth prediction based on predefined angular resolution.
- The development of an Inter-RP model capable of estimating current point radii using neighboring points from both current and registered reference frames.
- The creation of a LAEP model for predicting elevation angles by capturing long-range geometric correlations.
- The formulation of an RD-optimized quantization method for spherical coordinates.
- The design of distinct entropy coding models tailored to the statistical priors of each spherical coordinate component.
Why This Matters
The method directly targets the challenge of efficiently compressing LiDAR point cloud data, which is fundamental for applications relying on 3D spatial information. By improving inter-frame prediction and addressing geometric redundancy, Inter-LPCM could contribute to more effective data management and transmission for LiDAR systems.