BVH Refit in Reverse DFS Order for Real-Time Ray Tracing
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Solution Overview
Problem
Existing ray tracing techniques are resource-intensive and lack efficient methods for real-time performance, particularly in distributed systems where denoising frameworks operate on a single machine, limiting access to rendered pixels across multiple devices.
Innovation Solution
The implementation of a bounding volume hierarchy (BVH) refit mechanism that allows for distributed denoising operations across multiple nodes, optimizing ray-scene intersection computations and enhancing real-time ray tracing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ray tracing is implemented with physically-based rendering, then image quality and rendering accuracy are improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the rendering process into two distinct phases: a rendering phase that generates raw rendered pixels with full computational accuracy, and a denoising phase that processes these pixels to produce final clean images. This segmentation allows the computationally intensive ray tracing to be separated from the post-processing denoising operations, enabling efficient resource utilization across distributed systems.
Solution Approach 2:
The patent introduces rendered pixels as an intermediary data structure that bridges the rendering phase and denoising phase. These rendered pixels serve as a compressed representation of the scene that can be efficiently transmitted and processed across distributed devices, mediating between the high computational cost of ray tracing and the need for efficient distributed processing.
2Productivity
If denoising is performed on distributed systems across multiple devices, then rendering scalability is improved, but data accessibility and coordination complexity increase
Solution Approach 1:
The patent performs preliminary rendering actions on individual devices to generate rendered pixels before the denoising process begins. By pre-computing the rendered pixels and making them available for subsequent denoising operations, the system establishes a clear data flow sequence that simplifies coordination across distributed devices and enables efficient parallel processing.
Solution Approach 2:
The patent discards the need for complex inter-device coordination during the rendering phase by allowing each device to independently render pixels that are then recovered and processed collectively during the denoising phase. This approach simplifies the distributed system architecture by separating independent rendering operations from coordinated denoising operations.
3Ease of operation
If existing single-machine denoising frameworks are used, then implementation simplicity is maintained, but access to rendered pixels across multiple devices is limited
Solution Approach 1:
The patent creates a universal rendering framework that can operate both on single machines and across distributed systems. The rendered pixels serve as a universal data format that can be processed by denoising frameworks regardless of whether the underlying system is single-machine or multi-device, maintaining framework simplicity while enabling distributed scalability.
Data Source
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AI summary
Apparatus and method for acceleration data structure refit. For example, one embodiment of an apparatus comprises: a ray generator to generate a plurality of rays in a first graphics scene; a hierarchical acceleration data structure generator to construct an acceleration data structure comprising a plurality of hierarchically arranged nodes including inner nodes and leaf nodes stored in a memory in a depth-first search (DFS) order; traversal hardware logic to traverse one or more of the rays through the acceleration data structure; intersection hardware logic to determine intersections between the one or more rays and one or more primitives within the hierarchical acceleration data structure; a node refit unit comprising circuitry and/or logic to read consecutively through at least the inner nodes in the memory in reverse DFS order to perform a bottom-up refit operation on the hierarchical acceleration data structure.