Grid Traversal Unit Adaptive Spatial Subdivision
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Solution Overview
Problem
Current ray tracing technologies face inefficiencies in accelerating random ray traversal due to high computational costs and data movement latency, particularly when dealing with large polygon scenes and millions to billions of rays intersecting millions to billions of primitive objects.
Innovation Solution
The implementation of a Grid Traversal Unit (GTU) with adaptive spatial subdivision, absolute/relative position indexing, and ultra-fine grain 3D adaptive spatial subdivision, which reduces empty space in data structures, manages large polygon scenes, and tightens ray/polygon proximity before intersection tests, using a hierarchy of nested grids and digital logic circuits for efficient hardware-based processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional ray tracing acceleration structures are used, then ray traversal can be performed, but computational cost and data movement latency are excessively high
Solution Approach 1:
The patent segments the scene into a hierarchy of nested grids (coarse grids at higher levels, fine grids at lower levels). Each grid level partitions space independently, allowing rays to traverse only relevant portions of the scene. This segmentation eliminates the need to process entire large-scale scenes, dramatically reducing data movement latency and computational cost while maintaining traversal accuracy.
Solution Approach 2:
The patent introduces a hierarchical dimension to traditional spatial partitioning by organizing grids across multiple levels. Instead of a single flat grid structure, rays traverse through levels from coarse to fine, adding a temporal/organizational dimension to spatial partitioning. This hierarchical dimensioning reduces the search space at each level, improving ray traversal speed without increasing per-level complexity.
2Productivity
If high-resolution spatial subdivision is used to reduce empty space, then traversal efficiency improves, but data structure complexity increases
Solution Approach 1:
The patent implements nested grids where fine grids are contained within coarse grids, and each grid level is recursively subdivided. This nesting allows the data structure to represent complex scenes at multiple resolutions simultaneously. The hierarchical nesting reduces empty space processing by allowing rays to skip coarse empty regions without examining fine-grained details, improving traversal efficiency while organizing complexity in a manageable hierarchical manner.
3Measurement precision
If adaptive spatial subdivision is applied to tighten ray/polygon proximity, then intersection test accuracy improves, but computational overhead increases
Solution Approach 1:
The patent applies adaptive spatial subdivision that creates different grid resolutions in different regions of the scene. Areas with high object density or complex geometry receive finer grid subdivision, while empty or simple regions use coarser grids. This local quality adjustment tightens ray/polygon proximity measurements where needed (improving intersection accuracy) while avoiding unnecessary computational overhead in regions where high precision is not required.
Data Source
AI summary
A new hardware architecture defines an indexing and encoding method for accelerating incoherent ray traversal. Accelerating multiple ray traversal may be accomplished by organizing the rays for minimal movement of data, hiding latency due to external memory access, and performing adaptive binning. Rays may be binned into coarse grain and fine grain spatial bins, independent of direction.