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 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 across multiple devices, and continuously trains a machine learning engine during runtime using a sub-region of the image, updating weights dynamically to improve denoising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rendering is done across multiple devices, then rendering capability and image quality are improved, but existing denoising frameworks cannot access all rendered pixels for computing denoised portions
Solution Approach 1:
The patent divides the rendered image into multiple sub-regions distributed across different rendering devices. Each device processes and denoises only its local sub-region independently, while sharing boundary information with neighboring devices. This segmentation enables distributed processing while maintaining access to necessary pixel data through inter-device communication of ghost regions.
Solution Approach 2:
The patent introduces ghost regions as intermediary data structures that store pixel information from neighboring sub-regions. These ghost regions act as mediators between distributed rendering devices, allowing each device to access rendered pixels from other devices without requiring direct access to all rendered pixels across the entire distributed system.
2Reliability
If extensive training data is used for denoising, then denoising performance is improved, but the framework cannot generalize well to new scenarios
Solution Approach 1:
The patent implements dynamic training where the machine learning model continuously adapts during runtime by processing actual rendered sub-regions from the current scene. Instead of relying on static pre-computed training data, the system dynamically updates model weights using real-time rendering information, enabling the model to generalize to new scenarios while maintaining denoising performance.
Solution Approach 2:
The patent incorporates feedback mechanisms where denoised sub-regions and their corresponding rendered pixels are fed back into the machine learning model for continuous weight updates. This feedback loop enables the model to learn from actual rendering results and adapt to new scenarios, improving both denoising performance and generalization capability.
Data Source
AI summary
An apparatus comprising a sorting unit to sort primitives of a graphics image, the primitives to be grouped, each group to form a first level node of a hierarchical acceleration structure; a parallel reconfigurable clustering array to construct the hierarchical acceleration structure, the parallel reconfigurable clustering array comprising a plurality of processing clusters, each cluster comprising: parallel efficiency analysis circuitry to evaluate different groupings of the first level nodes for a next level of the hierarchical acceleration structure to determine efficiency values for the different groupings; and node merge circuitry to merge the first level nodes based on the efficiency values to form second level nodes.