Distributed Denoising via Ghost Region Data Exchange
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Solution Overview
Problem
Existing denoising frameworks for real-time ray tracing operate on a single machine, limiting their ability to access all rendered pixels for computing a denoised image when rendering is done across multiple devices, and they require extensive training data that may not adapt well to new scenarios.
Innovation Solution
A distributed denoising algorithm that uses machine learning, where nodes exchange 'ghost region' data to perform denoising operations, and a machine learning engine is continually trained and updated during runtime using a sub-region of the image, allowing it to improve its denoising capabilities dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rendering is done across multiple devices, then productivity and image quality improve, but existing denoising frameworks cannot access all rendered pixels for computing denoised portions
Solution Approach 1:
The image is divided into multiple tiles that are rendered and processed across different devices. Each device handles a subset of tiles, and the system coordinates data exchange between devices to maintain access to rendered pixel information across the distributed rendering frontier, enabling denoising operations despite the distributed architecture.
2Device complexity
If a fixed denoising model is used, then device complexity is reduced, but adaptability to new rendering scenarios deteriorates
Solution Approach 1:
The denoising model transitions from a fixed, pre-trained configuration to a dynamic system that continuously learns from rendered image data during execution. The model adapts its parameters based on the specific rendering scene and tile data processed by each device, enabling automatic adaptation to new scenarios without manual reconfiguration.
Solution Approach 2:
The system implements feedback loops where rendered tile data is fed back into the denoising model to refine and update model parameters. This continuous feedback mechanism allows the model to improve its performance on the current rendering task and adapt to variations in the scene, maintaining high adaptability while managing complexity through automated learning processes.
Data Source
AI summary
Apparatus and method for bottom-up BVH refit. For example, one embodiment of an apparatus comprises: a hierarchical acceleration data structure generator to construct an acceleration data structure comprising a plurality of hierarchically arranged nodes; traversal hardware logic to traverse one or more 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 unit comprising circuitry and/or logic to perform refit operations on nodes of the hierarchical acceleration data structure, the refit operations to adjust spatial dimensions of one or more of the nodes; and an early termination evaluator to determine whether to proceed with refit operations or to terminate refit operations for a current node based on refit data associated with one or more child nodes of the current node.