Octree Point Cloud Data Hierarchy for Efficient Storage
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Solution Overview
Problem
Processing and sharing of very large point cloud datasets are challenging due to their size and computational requirements, leading to storage and collaboration issues, even with modern computing equipment.
Innovation Solution
The method involves organizing data into an octree hierarchy of data sectors, allowing for multi-resolution views by storing and retrieving data sectors based on their proximity to the viewing origin, with higher resolution for closer points and lower resolution for farther points, facilitating efficient processing and storage using a network and user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If very large point cloud datasets are stored and processed using conventional techniques, then complete data representation is achieved, but storage requirements and computational burden increase significantly
Solution Approach 1:
The patent divides the point cloud data into hierarchical levels, where the entire point cloud is segmented into multiple level-zero data sets, which are further segmented into level-one data sets, and so on. This segmentation allows the system to store and process only the necessary portions of data at each resolution level, reducing overall storage requirements while maintaining complete representational capability when needed.
Solution Approach 2:
The patent introduces a resolution dimension to the data storage structure by organizing point cloud data into multiple resolution levels (level zero, level one, etc.). This dimensional approach allows the system to store compressed representations at coarser levels while preserving detailed information at finer levels, effectively reducing storage requirements without losing the ability to represent complete data when necessary.
2Measurement precision
If high-resolution data is processed for the entire point cloud, then detailed analysis is enabled, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by processing and displaying data at different resolution levels in different regions of the visualization. Points closer to the viewing origin are rendered at higher resolution levels for detailed analysis, while points farther away are rendered at lower resolution levels. This allows detailed analysis where needed while reducing overall processing time and computational resources.
Solution Approach 2:
The patent implements partial action by selectively processing and rendering only the necessary portions of the point cloud at high resolution. Instead of processing the entire data set at maximum detail, the system processes level-zero data sets for overall structure and only processes specific level-one, level-two, or deeper data sets for regions requiring detailed analysis, significantly reducing processing time.
3Quantity of substance
If data is organized into hierarchical levels with multiple resolution datasets, then storage efficiency improves, but data structure complexity increases
Solution Approach 1:
The patent implements a nested hierarchical structure where level-one data sets are nested within level-zero data sets, level-two data sets are nested within level-one data sets, and so on. Each higher-level data set contains or references multiple lower-level data sets. This nesting organization improves storage efficiency by allowing progressive refinement while managing complexity through a systematic hierarchical framework.
4Measurement precision
If all points are rendered at maximum resolution, then visualization accuracy is maximized, but system performance and retrieval speed decrease
Solution Approach 1:
The patent implements dynamic resolution rendering where the resolution level of displayed points adapts based on their distance from the viewing origin. Points closer to the viewer are dynamically rendered at higher resolution levels for accurate visualization, while points farther away are dynamically rendered at lower resolution levels. This dynamic approach maintains visualization accuracy where needed while improving retrieval speed overall.
Data Source
AI summary
One method embodiment comprises storing data on a storage system that is representative of a point cloud comprising a very large number of associated points; 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, the plurality of data sectors being assembled such that sectors representative of points closer to the selected viewing origin have a higher octree mesh resolution than that of sectors representative of points farther away from the selected viewing origin.