Distributed Denoising for Real-Time Ray Tracing
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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 across multiple devices, which hampers the computation of a denoised image in distributed rendering systems.
Innovation Solution
A distributed denoising algorithm that utilizes machine learning, specifically a convolutional neural network, continuously trains and updates its weights during runtime by gathering ghost region data from neighboring nodes to improve image denoising across multiple devices, allowing for efficient denoising operations even in distributed systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed rendering is used across multiple devices, then rendering capability and image quality are improved, but data accessibility for denoising is lost
Solution Approach 1:
A centralized denoising server acts as an intermediary between distributed rendering nodes, collecting rendered pixel data from multiple devices and providing the necessary information for computing denoised images. This mediator enables data accessibility across distributed systems without compromising rendering capability.
Solution Approach 2:
The denoising computation is segmented and distributed across multiple nodes, with each node responsible for specific portions of the image. The system divides the denoising task into manageable segments that can be processed independently across different devices while maintaining overall data accessibility.
2Loss of information
If existing denoising frameworks operate on a single machine, then data accessibility is maintained, but rendering scalability is limited
Solution Approach 1:
The denoising framework is designed with multi-functionality to operate both on single-machine and distributed rendering configurations. The system can adapt to different deployment scenarios, providing universal applicability while maintaining data accessibility and rendering scalability across various architectures.
3Productivity
If distributed rendering is implemented, then rendering performance is enhanced, but denoising computation complexity increases
Solution Approach 1:
The centralized denoising server simplifies the distributed denoising computation by acting as a mediator that collects data from multiple nodes and performs denoising operations. This intermediary approach reduces the complexity at individual nodes while maintaining enhanced rendering performance across the distributed system.
Data Source
AI summary
Real time ray tracing-based adaptive multi frequency shading. For example, one embodiment of an apparatus comprising: rasterization hardware logic to process input data for an image in a deferred rendering pass and to responsively update one or more graphics buffers with first data to be used in a subsequent rendering pass; ray tracing hardware logic to perform ray tracing operations using the first data to generate reflection ray data and to store the reflection ray data in a reflection buffer; and image rendering circuitry to perform texture sampling in a texture buffer based on the reflection ray data in the reflection buffer to render an output image.