Temporal BVH Structures for Efficient Motion Blur Ray Tracing
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Solution Overview
Problem
Ray tracing in dynamic scenes with moving objects is computationally intensive, particularly when rendering motion blur, as it requires tracing rays at different time stamps and identifying intersections with objects that may change significantly during the camera's shutter time.
Innovation Solution
Utilizing spatial and temporal data structures to optimize ray tracing by efficiently managing and intersecting rays with objects in dynamic scenes, incorporating temporal data to account for object movement during the shutter time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Monte Carlo path tracing is used to render motion blur by integrating over camera shutter time, then rendering quality is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the continuous time interval of the camera shutter into discrete time samples. Instead of integrating over the entire shutter time continuously, the system divides it into multiple discrete time points where ray tracing is performed separately for each sample, then combines the results to achieve motion blur. This segmentation transforms an intractable continuous problem into manageable discrete steps.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing temporal data structures that capture object positions and transformations at multiple time points before the actual ray tracing occurs. This pre-computation prepares the data in advance so that during rendering, the system can quickly access pre-prepared temporal information rather than computing object positions on-the-fly for each ray, significantly reducing computational complexity.
2Manufacturing precision
If ray tracing is performed at multiple time stamps to capture motion blur, then motion blur accuracy is improved, but time consumption increases
Solution Approach 1:
The patent implements dynamics by creating a temporal data structure that dynamically adapts to the motion characteristics of objects in the scene. The system identifies objects with significant motion during the shutter time and applies temporal sampling specifically to those objects, rather than uniformly processing all objects. This dynamic approach concentrates computational effort where it is most needed, improving motion blur accuracy for moving objects while avoiding unnecessary computations for static or slowly moving objects.
Solution Approach 2:
The patent changes parameters by adjusting the number and distribution of time samples based on object velocity and motion characteristics. For objects moving quickly during the shutter time, the system uses more time samples with finer temporal resolution. For objects with minimal motion, fewer samples are used. This parameter adaptation optimizes the balance between motion blur accuracy and rendering time consumption.
3Manufacturing precision
If temporal data structures are used to manage object positions during shutter time, then intersection accuracy is improved, but data structure complexity increases
Solution Approach 1:
The patent applies the nested doll principle by organizing temporal data structures in a hierarchical manner. The overall scene graph is nested with temporal subdivisions, where each object's spatial data structure is further nested with temporal information. This nested organization allows the system to maintain complex temporal relationships while using standard spatial data structures at each level, managing complexity through hierarchical decomposition rather than requiring a completely new complex structure.
Solution Approach 2:
The patent adds another dimension to the traditional spatial data structure by incorporating the time dimension. Instead of organizing objects only in spatial hierarchy (bounding volume hierarchies), the system extends this to spatio-temporal hierarchies where objects are organized both spatially and temporally. This dimensional extension allows efficient querying of object positions at specific time points while maintaining the benefits of hierarchical spatial organization, managing the complexity of temporal data through structured dimensional expansion.
Data Source
AI summary
A graphics processing apparatus comprising bounding volume hierarchy (BVH) construction circuitry to perform a spatial analysis and temporal analysis related to a plurality of input primitives and responsively generate a BVH comprising spatial, temporal, and spatial-temporal components that are hierarchically arranged, wherein the spatial components include a plurality of spatial nodes with children, the spatial nodes bounding the children using spatial bounds, and the temporal components comprise temporal nodes with children, the temporal nodes bounding their children using temporal bounds and the spatial-temporal components comprise spatial-temporal nodes with children, the spatial-temporal nodes bounding their children using spatial and temporal bounds; and ray traversal/intersection circuitry to traverse a ray or a set of rays through the BVH in accordance with the spatial and temporal components.