Point Cloud Compression Using Space-Filling Curve Dictionaries
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Solution Overview
Problem
Traditional point cloud data management solutions face scalability issues and lack declarative power, struggling to efficiently store and query massive amounts of data while maintaining space efficiency.
Innovation Solution
The use of a Space-Filling Curve Dictionary-Based Compression (SFC-DBC) method, which maps point cloud data to a dictionary space and a space-filling curve data structure, allowing for efficient storage and querying by transforming data into a one-dimensional domain and using indexing capabilities without additional storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional file-based solutions are used to store point cloud data, then space efficiency is improved, but scalability deteriorates when dealing with massive amounts of data
Solution Approach 1:
The patent segments massive point cloud data into manageable blocks organized in a hierarchical structure. The data is divided into super-blocks and blocks, allowing the system to handle large datasets by processing them in smaller, organized units. This segmentation enables scalability while maintaining efficient storage through compression at each hierarchical level.
Solution Approach 2:
The patent introduces a hierarchical dimension to traditional flat file storage by organizing data in multiple levels (super-blocks containing blocks). This dimensional transformation allows the system to scale to massive datasets while maintaining space efficiency through multi-level compression, resolving the contradiction between storage footprint and scalability.
2Loss of substance
If traditional file-based solutions are used to store point cloud data, then space efficiency is improved, but query performance deteriorates
Solution Approach 1:
The patent performs preliminary organization of data into a hierarchical block structure with associated metadata during the data loading phase. This preliminary action includes creating block headers, organizing super-blocks, and establishing the hierarchical framework before queries are executed. This pre-organization enables fast query performance by allowing the system to quickly navigate to relevant data blocks without scanning entire datasets, while maintaining compression for space efficiency.
3Loss of substance
If data is compressed to minimize storage footprint, then space efficiency is improved, but query execution time increases
Solution Approach 1:
The patent applies different compression strategies and organization methods to different parts of the data structure. Highly repetitive data regions use aggressive compression, while regions requiring frequent access maintain optimized structures. The hierarchical block structure allows local optimization where compression is applied at multiple levels, and query processing can selectively access compressed or uncompressed data based on specific needs, balancing space efficiency with query performance.
4Quantity of substance
If point cloud data is stored in massive volumes, then data completeness is improved, but management complexity increases
Solution Approach 1:
The patent segments massive point cloud datasets into hierarchical blocks and super-blocks, each with manageable sizes and associated metadata. This segmentation transforms the management of massive data volumes into the management of many small, organized units. The hierarchical structure provides clear organization levels that simplify management operations such as data loading, querying, and maintenance, reducing complexity while preserving complete data storage capacity.
Data Source
AI summary
Embodiments of the present disclosure pertain to point cloud data. In one embodiment, the present disclosure includes a computer implemented method comprising for compressing and storing point cloud data using a space-filling curve data structure and a dictionary space data structure. In another embodiment, the present disclosure includes applying a query against the data structures to retrieve point cloud data within a range.