Point Cloud Compression Using Position-Dependent Error Constraints
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Solution Overview
Problem
Current data compression methods for point clouds, such as basis pursuit and Morse description, either discard important information or fail to utilize the available freedom provided by the uncertainty profile, leading to inefficient compression and error control in bandwidth-constrained environments.
Innovation Solution
A lossy compression method that constructs a surface for each point cloud to minimize memory usage, using point-wise error constraints and an over-complete dictionary of orthonormal bases, specifically Fourier and Delta bases, to achieve a sparse representation while adhering to specified error tolerances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression methods are used to reduce data size for bandwidth-constrained environments, then compression ratio is improved, but error control and information accuracy deteriorate
Solution Approach 1:
The patent applies local quality by allowing different error tolerances at different spatial locations. Instead of uniform error control, the method uses position-dependent error constraints where each point in the point cloud has its own tolerance level, enabling aggressive compression in low-precision regions while maintaining accuracy where needed.
Solution Approach 2:
The patent changes the error control parameter from a uniform global tolerance to a spatially varying tolerance profile. By parameterizing the error constraints as position-dependent functions, the system optimizes the balance between compression ratio and accuracy control across different regions of the point cloud.
2Quantity of substance
If traditional compression methods discard information to achieve better compression ratios, then data size is reduced, but important features are lost
Solution Approach 1:
The patent implements feedback through an iterative optimization process that continuously monitors reconstruction error against the original point cloud. The algorithm adjusts the sparse representation coefficients to minimize deviation from important features while maintaining the desired compression ratio, effectively learning which features are most important based on the error profile.
Solution Approach 2:
The patent replaces traditional mechanical information discarding with a controlled approximation process. Instead of arbitrarily removing data, the system substitutes a mathematical optimization framework that selectively approximates features based on their importance and the allowed error tolerance, replacing brute-force data removal with intelligent feature preservation.
3Device complexity
If uniform error constraints are applied across all points, then error control is simplified, but compression efficiency deteriorates
Solution Approach 1:
The patent applies local quality by allowing different error tolerances at different spatial locations. Instead of uniform error control, the method uses position-dependent error constraints where each point in the point cloud has its own tolerance level, enabling aggressive compression in low-precision regions while maintaining accuracy where needed.
Solution Approach 2:
The patent introduces dynamics by making the error constraints adaptive rather than static. The error tolerance profile can be adjusted based on the specific application requirements and the importance of different spatial regions, allowing the compression system to dynamically optimize its behavior for different scenarios.
Data Source
AI summary
A method for compressing a cloud of points with imposed error constraints at each point is disclosed. Surfaces are constructed that approach each point to within the constraint specified at that point, and from the plurality of surfaces that satisfy the constraints at all points, a surface is chosen which minimizes the amount of memory required to store the surface on a digital computer.