Wavelet Point Cloud Compression for High-Density LIDAR Data
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Solution Overview
Problem
High-density point clouds generated by LIDAR technology pose challenges for computational, transmissive, and storage systems due to their large volume of data, making efficient compression and processing difficult.
Innovation Solution
A system and method utilizing a time series converter and pipelined wavelet transformer to compress point cloud data by sorting vectors based on ordinal references and performing wavelet transformation in multiple pipeline stages, producing coefficients for compression, and using bit planes and arithmetic coding for efficient data reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR technology is used to generate high-density point clouds, then topographic mapping quality is improved, but data volume increases making computational and storage operations difficult
Solution Approach 1:
The patent segments the point cloud data processing into multiple pipeline stages, where each stage processes a portion of the data independently. The wavelet transformation is applied in a pipelined manner with multiple stages that can process different segments of the point cloud simultaneously, enabling parallel processing and reducing the computational burden on single processing units.
Solution Approach 2:
The patent transforms the point cloud data from spatial coordinates (x, y, z) into wavelet domain parameters through mathematical transformation. This parameter change converts the data representation from raw coordinate values to wavelet coefficients, which have better compression properties and enable more efficient storage and transmission while preserving the essential topographic information.
2Measurement precision
If more point cloud data is collected to improve mapping detail, then measurement precision is improved, but transmission and storage efficiency deteriorates
Solution Approach 1:
The wavelet transformation changes the parameter representation of point cloud data, converting dense spatial coordinates into a transformed domain where energy is concentrated in fewer coefficients. This parameter transformation enables efficient compression by allowing selective retention of important coefficients while discarding less significant ones, thereby improving transmission and storage efficiency without sacrificing essential mapping detail.
Solution Approach 2:
The patent extracts and processes only the most significant wavelet coefficients for storage and transmission, rather than transmitting all original point cloud data. By identifying and retaining only the essential coefficients that contribute most to topographic accuracy, the system achieves efficient data reduction while maintaining mapping quality.
3Quantity of substance
If point cloud data is compressed to reduce data volume, then storage efficiency is improved, but data loss increases
Solution Approach 1:
The wavelet transformation parameter change enables a hierarchical representation of point cloud data where coefficients can be selectively retained or discarded based on their significance. By transforming to the wavelet domain, the system can compress data by removing less significant coefficients while preserving those that contribute most to topographic accuracy, thus achieving compression with minimal information loss.
Solution Approach 2:
The patent applies different levels of compression to different regions of the point cloud based on their local importance. Critical topographic features retain higher fidelity with fewer coefficients discarded, while less critical areas undergo more aggressive compression. This local quality approach ensures that data loss is minimized in important regions while achieving overall compression efficiency.
Data Source
AI summary
Using LIDAR technology, terabytes of data are generated which form massive point clouds. Such rich data is a blessing for signal processing and analysis but also is a blight, making computation, transmission, and storage prohibitive. The disclosed subject matter includes a technique to convert a point cloud into a form that is susceptible to wavelet transformation permitting compression that is nearly lossless.