Minimal Octree Spatial Database for Dynamic Scene Resizing
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Solution Overview
Problem
Existing spatial databases face inefficiencies in managing dynamic environments due to the need for resizing operations when objects move, scale, or update, leading to performance issues and resource wastage, particularly in hyperscale scenarios.
Innovation Solution
A spatial database using a minimally-sized octree structure that dynamically adjusts bounding volumes in alternating directions to ensure efficient storage and querying of dynamic content across multiple machines, minimizing unnecessary space and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a bounding volume hierarchy (BVH) is used to store volumetric data, then operations such as ray tracing are optimized, but resize operations needed when primitives move or scale result in performance issues
Solution Approach 1:
The patent implements a dynamic octree structure that automatically adapts its bounding volumes when primitives move or scale. Instead of requiring full rebuild operations like static BVH structures, the octree dynamically adjusts node boundaries and reorganizes spatial partitions in response to changing primitive extents, maintaining performance stability without sacrificing ray tracing optimization
Solution Approach 2:
The system changes the fundamental parameter of spatial organization from BVH's hierarchical bounding boxes to octree's uniform grid-based cells. This parameter change allows the structure to handle primitive transformations more efficiently, as the fixed-grid octree naturally accommodates moving and scaling primitives without requiring costly reorganization operations
2Adaptability or versatility
If traditional spatial databases are used to manage dynamic environments, then data can be stored, but resizing operations when objects move or scale lead to resource wastage and inefficiency
Solution Approach 1:
The octree structure performs preliminary spatial partitioning into fixed grid cells before primitives are added or modified. This preliminary organization allows dynamic content to be stored efficiently without requiring subsequent resizing operations, as new primitives can be directly inserted into appropriate pre-defined cells without triggering costly reorganization of the entire spatial database
Solution Approach 2:
The patent segments the spatial database into discrete octree nodes representing fixed volumetric cells. This segmentation allows independent management of different spatial regions, enabling efficient storage of dynamic content where each primitive is associated with specific grid cells rather than requiring global restructuring when objects move or change scale
3Quantity of substance
If spatial databases scale to hyperscale environments, then more data can be processed, but the complexity of managing growth and maintaining performance increases
Solution Approach 1:
The octree structure implements nested hierarchical organization where large-scale spatial data is divided into parent nodes that contain child nodes representing progressively smaller spatial subdivisions. This nesting allows hyperscale environments to be managed by recursively applying the same organizational pattern at different scales, from global environment boundaries down to individual primitive locations, simplifying the management of massive data quantities through self-similar structural repetition
Data Source
AI summary
Approaches presented herein provide for the generation and maintenance of a minimally-sized octree (or other spatial representation) for an arbitrary or dynamic dataset. An octree representation allows for efficient real-time querying of dynamic content, where such a spatial database can scale from a single machine to multiple machines. Objects in a scene can be stored to the spatial database using a set of spatial primitives. When a first object (or set of objects) in a scene is determined, an initial octree and root can be determined based at least in part upon the size and location of the object(s). An additional object may be added that is outside the existing root of the octree. A new bounding volume is generated that surrounds this new object, and the bounding volume is grown in alternating directions until the bounding volume also includes the other objects in the environment, and this new volume boundary becomes the new root of an updated octree for this environment.