Multi-Dimensional Binning for Memory-Efficient BVH Construction
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Solution Overview
Problem
The slow building process of Bounding Volume Hierarchies (BVH) for light transport simulation is bottlenecked by significant memory bandwidth consumption due to the need to read and project primitives into bins for each frame change, especially with large quantities of scene primitives.
Innovation Solution
Spatial elements are projected into multi-dimensional bins to determine hierarchical partitionings, using assignments and split planes to reduce memory bandwidth by minimizing the need for repeated reads during partitioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a recursive partitioning process is used to build BVH for each frame, then the BVH structure is updated to reflect viewport changes, but memory bandwidth consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-sorting primitives into bins based on their spatial coordinates before the recursive partitioning process begins. This pre-organization of data allows the subsequent partitioning operations to proceed without repeatedly reading and re-sorting primitives, thereby reducing memory bandwidth consumption while maintaining adaptability to viewport changes.
Solution Approach 2:
The patent segments the scene geometry into multiple bins along different axes (x, y, z) before partitioning. By dividing the primitive set into spatial segments and organizing them into bin structures, the system reduces the amount of data that needs to be accessed during each partitioning step, thus lowering memory bandwidth requirements while preserving the ability to adapt to changing viewports.
2Measurement precision
If primitives are read and projected into bins for each axis during partitioning, then accurate split plane candidates are obtained, but the building speed of BVH is greatly reduced
Solution Approach 1:
The patent performs preliminary binning of primitives along all three axes before the recursive partitioning process. This pre-computation of bin assignments stores the spatial distribution information that would otherwise need to be recalculated during each partitioning step, thereby maintaining split plane candidate accuracy while significantly accelerating the BVH building process.
Solution Approach 2:
The patent creates copy structures (bin assignments and sorted primitive lists) that replicate the spatial organization information needed for partitioning. Instead of repeatedly accessing and processing the original primitive data during partitioning, the system uses these pre-created copies, reducing memory access overhead while preserving the precision required for accurate split plane selection.
3Reliability
If a new BVH is built when scene geometry changes, then the BVH reflects updated scene conditions, but the slow building process becomes a bottleneck
Solution Approach 1:
The patent applies preliminary binning and sorting of primitives into bin structures before the recursive partitioning process. This pre-organization of geometric data allows the BVH building algorithm to quickly process updated scene geometry without repeatedly accessing the original primitive data, thereby maintaining BVH accuracy for updated geometry while significantly reducing the building time bottleneck.
Data Source
AI summary
In various examples, spatial elements of a scene may be projected into multi-dimensional bins that correspond to a spatial partitioning of the scene to determine assignments between the spatial elements and the multi-dimensional bins. A partition of the spatial elements may be determined using the assignments and a spatial element may be assigned to a node corresponding to a hierarchical partitioning of the spatial elements based on the partition. To determine the partition, candidate split planes may be determined with respect to the multi-dimensional bins, and a split plane that defines the partition may be selected from the candidate split planes. The assignments and the multi-dimensional bins may also be used to determine subpartitions of the partition. For example, the assignments may be used to determine the subpartitions with respect to a subset of the multi-dimensional bins that corresponds to the partition.