Distributed Denoising via Ghost Region Data Sharing
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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 efficient distributed denoising of graphics frames.
Innovation Solution
A distributed denoising algorithm that collects 'ghost region' data from neighboring nodes to facilitate denoising across multiple devices, allowing for the composition of a final image by sharing necessary data for smooth denoising operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If denoising is performed on a single machine, then all rendered pixels are accessible for computing denoised portions, but the system cannot scale to distributed rendering across multiple devices
Solution Approach 1:
The rendering system is segmented into multiple independent rendering nodes that each render specific portions of the final image. Each node maintains access to all rendered pixels through shared memory or inter-node communication, enabling distributed rendering while preserving full pixel accessibility for denoising operations.
Solution Approach 2:
A communication intermediary or shared memory space is introduced between rendering nodes to enable exchange of rendered pixel data. This intermediary allows nodes to access pixels rendered by other nodes, solving the information accessibility problem in distributed rendering.
2Productivity
If rendering is distributed across multiple devices, then rendering workload is parallelized, but not all rendered pixels are accessible for computing denoised portions
Solution Approach 1:
The patent merges the pixel buffers from multiple rendering nodes into a unified accessible space, either through shared memory or by assembling complete frame data from partial renders. This combining approach maintains full pixel accessibility while preserving the parallel rendering benefits of distributed computation.
Solution Approach 2:
The system adds a communication and data aggregation dimension to the distributed rendering architecture. Instead of purely spatial distribution, a temporal and communicative dimension is introduced where nodes exchange pixel data through defined protocols, enabling both parallel processing and full data accessibility.
3Reliability
If existing denoising frameworks are used, then denoising can be performed on single-machine renders, but they cannot operate efficiently in distributed rendering environments
Solution Approach 1:
The denoising framework is designed with universal interfaces and data structures that work consistently across single-machine and distributed-rendering environments. By using standardized pixel buffer formats and communication protocols, the same denoising algorithms can operate reliably whether pixels come from one node or multiple nodes.
Data Source
AI summary
Apparatus and method for denoising of images generated by a rendering engine such as a ray tracing engine. For example, one embodiment of a system or apparatus comprises: A system comprising: a plurality of nodes to perform ray tracing operations; a dispatcher node to dispatch graphics work to the plurality of nodes, each node to perform ray tracing to render a region of an image frame; at least a first node of the plurality comprising: a ray-tracing renderer to perform ray tracing to render a first region of the image frame; and a denoiser to perform denoising of the first region using a combination of data associated with the first region and data associated with a region outside of the first region, at least some of the data associated with the region outside of the first region to be retrieved from at least one other node.