Distributed Denoising Algorithm 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 generalize 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 adapt to new data and improve denoising quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rendering is done across multiple devices, then rendering capacity and speed are improved, but access to all rendered pixels for denoising operations becomes limited
Solution Approach 1:
The patent divides the rendering system into multiple distributed devices, each handling a portion of the rendering workload. Each device processes its local pixels independently while maintaining the ability to contribute to the overall denoising process through selective data exchange, thus preserving functionality while enabling parallel processing.
Solution Approach 2:
The patent introduces an intermediary mechanism where devices exchange only the necessary 'ghost region' data rather than all rendered pixels. This intermediary data transfer allows distributed devices to collaborate on denoising operations without requiring full access to all pixels across the distributed system, resolving the information access limitation.
2Loss of information
If existing denoising frameworks operate on a single machine, then access to all rendered pixels is ensured, but scalability to distributed systems is limited
Solution Approach 1:
The patent transforms the static single-machine denoising framework into a dynamic distributed system. The denoising algorithm adapts to operate across multiple devices by dynamically determining which pixels to process locally versus which ghost region pixels to import from other devices, enabling scalability while maintaining denoising quality.
Solution Approach 2:
The patent creates a universal denoising framework that can operate in both single-machine and distributed configurations. The same core algorithm functions across different system architectures, allowing the system to scale from one device to many while maintaining consistent denoising performance through the ghost region import mechanism.
3Measurement precision
If extensive training data is used for denoising, then denoising quality may improve, but the system cannot generalize well to new scenarios
Solution Approach 1:
The patent implements a feedback mechanism where the denoising system continuously learns from the ghost region data it imports and processes. By using the actual rendered pixels from other devices as training data during runtime, the system adapts to new scenarios and maintains denoising quality without requiring extensive pre-collected training datasets.
Solution Approach 2:
The patent performs preliminary denoising operations on imported ghost region pixels before integrating them into the final image. This preliminary processing allows the system to adapt to new rendering scenarios in advance, improving generalization by pre-processing and validating data from unknown or changing environments.
Data Source
AI summary
Apparatus and method for general ray tracing queries. 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 associated with a graphics scene; traversal/intersection hardware logic to traverse one or more rays through the acceleration data structure to determine intersections between the one or more rays and one or more primitives within the hierarchical acceleration data structure; shape processing hardware logic to specify three dimensional (3D) shape data indicating one or more 3D shapes to be used to perform queries with respect to the hierarchical acceleration data structure; query processing hardware logic to execute queries comprising comparisons between nodes of the hierarchical acceleration data structure and the 3D shape data to generate a result indicating overlap between the 3D shapes and the nodes.