Hierarchical Point Cloud Compression with Multi-Level Tile Reordering
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Solution Overview
Problem
Existing point cloud compression technologies are inefficient, requiring significant storage and lacking support for error propagation, which is crucial for applications like On-Board Navigation and Targeting.
Innovation Solution
A system and method for point cloud compression that involves quantizing point cloud data to remove noise, generating multi-level tiles, reordering these tiles to optimize compression, bit packing to minimize header data, and applying a first compression algorithm to achieve efficient storage and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed using existing technologies, then storage requirements are reduced, but compression efficiency is insufficient and significant storage is still required
Solution Approach 1:
The point cloud data is divided into multiple tiles, where each tile represents a spatial subset of the complete point cloud. This segmentation allows independent processing and compression of individual tiles, improving overall compression efficiency while reducing the storage requirements for the entire dataset.
Solution Approach 2:
The patent introduces a hierarchical tile structure with multiple levels of organization. Point cloud data is organized into tiles at different hierarchy levels, creating a multi-dimensional storage and processing framework that enhances compression efficiency beyond traditional single-level approaches.
2Measurement precision
If quantization is applied to remove noise from point cloud data, then data quality is improved, but compression ratio is reduced
Solution Approach 1:
Different quantization strategies are applied to different tiles based on their local characteristics. Each tile can have its own quantization parameters optimized for its specific data properties, allowing high data quality in regions requiring precision while maintaining better compression ratios in regions where aggressive quantization is acceptable.
Solution Approach 2:
The patent employs variable quantization parameters across different tiles and hierarchy levels. By dynamically adjusting quantization parameters based on local data characteristics and compression requirements, the system achieves an optimal balance between data quality and compression ratio.
3Productivity
If multi-level tiles are generated and reordered to optimize compression, then compression rate is improved, but processing complexity increases
Solution Approach 1:
Tiles are pre-ordered and organized into a hierarchical structure before the actual compression process. This preliminary organization optimizes the data layout for compression, allowing standard compression algorithms to achieve better rates without requiring complex real-time processing during compression.
Solution Approach 2:
The tile hierarchy and ordering can be dynamically adjusted based on the specific characteristics of the point cloud data being processed. The system adapts the organizational structure to match the data distribution, improving compression rates while keeping processing complexity manageable through automated adaptation.
Data Source
AI summary
Systems and methods are provided for point cloud compression. An exemplary method includes: receiving point cloud data; quantizing the point cloud data to remove noise to produce quantized point cloud data; generating, using the quantized point cloud data, a plurality of multi-level tiles; performing reordering within the multi-level tiles to optimize a compression rate producing a plurality of reordered multi-level tiles; bit packing the reordered multi-level tiles minimizing bits required to store per tile header data producing a bit packed multi-level tile data; and performing additional compression on the bit-packed multi-level tile data using a first compression algorithm.