Octree Hierarchy for Point Cloud Data Processing
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Solution Overview
Problem
Handling and processing very large point cloud datasets are challenging due to their size and computational resource limitations, leading to storage and processing inefficiencies, especially when analyzing and sharing detailed data sets like national topography or LIDAR scans.
Innovation Solution
The method involves organizing point cloud data into an octree hierarchy, allowing for efficient storage and processing by subdividing the data into manageable sectors, which can be retrieved and assembled based on user-defined viewing perspectives, utilizing a network and internet-accessible storage systems to facilitate collaborative and streamlined data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is stored in conventional formats, then data completeness is maintained, but storage efficiency and processing speed deteriorate due to the sheer size of the datasets
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into hierarchical levels (e.g., Level of Detail 0, 1, 2, 3) where each level contains progressively fewer and more aggregated points. This allows the system to store all detailed data while enabling rapid retrieval of coarser representations when full detail is not needed, thus improving processing speed without losing data completeness.
Solution Approach 2:
The patent implements nesting through its hierarchical data structure where higher Level of Detail representations are nested within lower detail levels. Each LOD level contains the previous level's data plus additional detail, allowing the system to efficiently retrieve only the necessary detail level for a given operation, reducing processing time while maintaining access to complete data when needed.
2Measurement precision
If detailed point cloud data is shared between users, then data accuracy is maintained, but data collaboration efficiency deteriorates due to storage and transfer challenges
Solution Approach 1:
The patent applies dynamics by implementing a dynamic Level of Detail selection mechanism that automatically adjusts the detail level of shared data based on the user's specific needs, distance from objects, and computational constraints. This allows the system to maintain high data accuracy when needed while improving collaboration efficiency by transmitting and storing only appropriate detail levels for different use cases.
3Productivity
If conventional computing equipment is used to process large point clouds, then hardware availability is maintained, but processing capability deteriorates due to memory and computational limitations
Solution Approach 1:
The patent applies segmentation by processing point cloud data in manageable hierarchical levels rather than attempting to load and process all billion-scale points simultaneously. The system divides the massive dataset into LOD levels that can be processed sequentially or in parallel using conventional memory, enabling high processing capability without requiring specialized supercomputing hardware.
Data Source
AI summary
One embodiment is directed to a method for presenting views of a very large point data set, comprising: storing data on a storage system that is representative of a point cloud comprising a very large number of associated points; automatically and deterministically organizing the data into an octree hierarchy of data sectors, each of which is representative of one or more of the points at a given octree mesh resolution; receiving a command from a user of a user interface to present an image based at least in part upon a selected viewing perspective origin and vector; and assembling the image based at least in part upon the selected origin and vector, the image comprising a plurality of data sectors pulled from the octree hierarchy.