Mutable Tree Data Structure for 3D Environment Access
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Solution Overview
Problem
Existing three-dimensional (3D) image representation using monolithic tree structures, such as octrees, becomes inefficient and memory-intensive as the size of the 3D environment increases, leading to slow data access and processing due to the need for extensive tree traversal and potential memory overflow.
Innovation Solution
A mutable tree data structure (MTDS) is introduced, which subdivides the 3D environment into multiple subtrees organized under branches of a root node, rearranging these subtrees based on distance from the current position to optimize data access and reduce memory usage, allowing for faster searching and processing by calculating a single distance to select the relevant subtree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a monolithic tree structure (octree) is used to represent the entire 3D environment, then the data organization is simplified and searching can be performed systematically, but the tree size grows proportionally with the 3D environment size leading to memory overflow and slow traversal
Solution Approach 1:
The patent divides the monolithic tree structure into multiple smaller subtrees, each representing a portion of the 3D environment. These subtrees are organized in a collection rather than forming a single large tree, allowing the data to be partitioned into manageable chunks that fit within memory constraints while maintaining systematic organization for efficient searching and traversal.
2Adaptability or versatility
If the tree representation grows to encompass larger 3D spaces or more objects, then the coverage and representational capability improve, but the traversal time increases proportionally with the tree size
Solution Approach 1:
By segmenting the large tree into multiple smaller subtrees, the patent enables parallel or selective traversal of only relevant subtrees based on the query parameters, rather than traversing the entire monolithic tree. This significantly reduces traversal time while maintaining the ability to represent large 3D environments through the collection of subtrees.
Solution Approach 2:
The patent applies different processing and traversal strategies to different subtrees based on their local characteristics and relevance to the query. Instead of uniform traversal across the entire environment, the system can focus computational resources on subtrees that are most likely to contain the target data, improving search efficiency.
3Ease of manufacture
If a monolithic tree structure is used, then the representation is unified and simple to implement, but the structure becomes too large to load into memory for processing large datasets
Solution Approach 1:
The patent implements a collection of subtrees that can be loaded into memory incrementally or selectively, rather than requiring the entire monolithic tree to be loaded at once. This segmentation approach maintains implementation simplicity while ensuring that memory capacity is not exceeded, as only the necessary subtrees for the current operation need to be in memory.
Data Source
AI summary
Disclosed is a system and associated methods for generating a mutable tree to efficiently access data within a three-dimensional (“3D”) environment. The system generates the mutable tree with a root node defined at a root node position, a first branch with nodes for each of a first set of subdivided regions that are a first distance from the root node position, and a second branch with nodes for each of a second set of subdivided regions that are a second distance from the root node position. The system sorts the mutable tree in response to a request to access data from a first position within the 3D environment so that the first node in the first branch is the first subtree node that is closest to the first position, and the first node in the second branch is the second subtree node that is closest to the first position.