Point Cloud Rasterization Using Key-Value Pairs
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Solution Overview
Problem
Point cloud data storage is inefficient due to large file sizes, making it difficult to query and combine with other geospatial layers, and existing rasterization methods do not provide sufficient storage savings.
Innovation Solution
The method involves rasterizing point cloud data into layers based on classes, converting these layers into key-value pairs, and storing them in a key-value store, which reduces storage space and enables efficient querying and combination with other data layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is stored in traditional formats, then data completeness is maintained, but storage space requirements become excessively large
Solution Approach 1:
The patent extracts only the essential attributes of point cloud data (coordinates and class information) and stores them as key-value pairs, removing redundant data structures and formatting overhead. This extraction approach maintains the core geometric and semantic information while dramatically reducing storage requirements.
Solution Approach 2:
The patent changes the data representation parameters by converting traditional point cloud formats into a simplified key-value pair structure where keys represent spatial coordinates and values represent class labels. This parameter transformation enables more efficient storage while preserving the essential point cloud information.
2Productivity
If point cloud data is stored in traditional formats, then data integrity is preserved, but querying efficiency deteriorates
Solution Approach 1:
The patent segments point cloud data by class categories, organizing points into separate collections based on their semantic labels. This segmentation enables targeted queries on specific classes without processing the entire point cloud, significantly improving querying efficiency while maintaining data integrity through structured organization.
Solution Approach 2:
The patent introduces a key-value store as an intermediary layer between the raw point cloud data and query operations. This intermediary structure provides efficient indexing and retrieval capabilities while preserving the original data characteristics, enabling fast queries without compromising data integrity.
3Quantity of substance
If point cloud data is rasterized into layers, then data organization is improved, but storage savings are insufficient
Solution Approach 1:
The patent extracts only the essential information from rasterized layers - the coordinates and class labels - and stores them as sparse key-value pairs. This extraction eliminates the need to store complete raster matrices with numerous zero or null values, achieving significant storage savings while maintaining organized data structure through the key-value format.
Data Source
AI summary
A computer implemented method rasterizes point cloud data. A number of processor units rasterizes the point cloud data into rasterized layers based on classes in which each rasterized layer in the rasterized layers corresponds to a class in the classes. The number of processor units creates key value pairs from the rasterized layers. The number of processor units store the key value pairs in a key value store. According to other illustrative embodiments, a computer system and a computer program product for rasterizing point cloud data are provided.