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 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 trained during runtime, and AI-based techniques to refine denoising by exchanging 'ghost region' data between nodes, enabling efficient denoising across multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed rendering is used across multiple devices, then rendering capability and productivity are improved, but access to all rendered pixels for denoising computation is lost
Solution Approach 1:
The patent divides the rendering system into multiple independent nodes that each render specific portions of the image. Each node maintains local rendering data while communicating essential information to other nodes through ghost region data exchange, enabling distributed processing without complete data centralization.
Solution Approach 2:
The patent introduces ghost region data as an intermediary mechanism that allows nodes to access necessary rendering information from neighboring nodes without requiring direct access to all rendered pixels across the distributed system. This mediator enables denoising computation in a distributed environment.
2Loss of information
If single-machine denoising is used, then access to all rendered pixels is maintained, but rendering performance and productivity are limited
Solution Approach 1:
The patent transitions from single-machine denoising to multi-node distributed denoising by adding the network dimension. Nodes exchange ghost region data through network communication, enabling denoising across multiple devices while maintaining access to necessary rendering information.
3Measurement precision
If traditional ray tracing is used, then rendering accuracy is improved, but computational cost and processing time increase
Solution Approach 1:
The patent performs preliminary denoising computations using ghost region data from neighboring nodes before final image rendering. By pre-computing denoised values and caching them, the system reduces the computational burden during real-time rendering while maintaining accuracy.
Solution Approach 2:
Each node performs self-service denoising by utilizing its own local rendering data combined with ghost region data from neighboring nodes. This distributed self-service approach eliminates the need for centralized denoising computation, reducing processing time while maintaining rendering accuracy.
Data Source
AI summary
Multi-pass apparatus and method for ray tracing shading. For example, one embodiment of an apparatus comprises: graphics processing circuitry to execute a sequence of visibility testing operations related to texels within a texture domain to generate visibility results; a register or memory to store a texel mask; texel mask update circuitry/logic to update the texel mask based on the visibility results, the texel mask comprising a plurality of bits to indicate visibility of the texels within the texture domain, the texel mask update circuitry/logic to set a first bit to indicate whether any bits in the texel mask indicate a visible texel; a shader dispatcher to initiate conditional dispatch operations only if the first bit is set to indicate that at least one bit in the texel mask indicates a visible texel, wherein to perform the conditional dispatch operations, the shader dispatcher is to dispatch texel shaders for only those texels that the texel mask indicates may be visible; and a plurality of execution units (EUs) to execute the shaders dispatched by the shader dispatcher.