Radius Inter Prediction for Irregular Point Cloud Geometry Coding
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Solution Overview
Problem
Inter prediction for predictive geometry coding in point cloud compression is complicated due to irregular sampling grids, leading to increased complexity and inefficiency.
Innovation Solution
The use of radius interpolation to determine reference points and obtain inter predictors for current points in point cloud frames, simplifying the inter prediction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional inter prediction methods are used with irregular sampling grids, then point cloud compression can be performed, but the complexity of interpolation increases significantly
Solution Approach 1:
The patent transforms the irregular 3D point cloud sampling problem into a regular 2D grid sampling problem by changing the coordinate parameter representation. This allows standard 2D interpolation techniques to be applied, significantly reducing computational complexity while maintaining compression effectiveness.
Solution Approach 2:
The patent projects 3D point cloud data onto a 2D plane, effectively reducing dimensionality from three dimensions to two. This dimensionality reduction enables the use of conventional 2D interpolation algorithms instead of complex 3D irregular grid interpolation, resolving the technical contradiction between compression capability and computational complexity.
2Manufacturing precision
If traditional inter prediction methods are used for point cloud compression, then compression can be achieved, but coding accuracy is reduced due to varying spacings between samples
Solution Approach 1:
By changing the parameter representation from irregular 3D coordinates to regular 2D grid indices, the patent enables precise interpolation calculations using standard algorithms, thereby improving coding accuracy while avoiding the complexity of handling irregular spacings directly.
Solution Approach 2:
The patent creates a virtual regular 2D grid representation that copies the essential spatial relationships of the original 3D point cloud. This virtual copy allows accurate interpolation to be performed on the simplified structure, which can then be mapped back to the original coordinate system for high-accuracy coding.
3Measurement precision
If complex interpolation methods are used to handle irregular sampling grids, then interpolation accuracy may improve, but power consumption increases
Solution Approach 1:
The patent reduces computational power requirements by projecting 3D interpolation problems onto a 2D plane. This dimensionality reduction allows the use of efficient 2D interpolation algorithms that consume less power while maintaining sufficient accuracy for point cloud compression applications.
Solution Approach 2:
By transforming the problem parameters from irregular 3D spacing to regular 2D grid positioning, the patent enables the use of computationally efficient interpolation methods that require less processing power and energy consumption while achieving the necessary interpolation accuracy.
Data Source
AI summary
Example devices and techniques for coding point cloud data are described. An example device includes memory configured to store the point cloud data and one or more processors communicatively coupled to the memory. The one or more processors are configured to determine least two reference points in a reference point cloud frame of the point cloud data. The one or more processors are configured to apply radius interpolation to the at least two reference points to obtain at least one radius inter predictor for at least one current point in a current point cloud frame of the point cloud data. The one or more processors are configured to code the current point cloud frame based on the at least one radius inter predictor for the at least one current point in the current point cloud frame.