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 can result in incomplete denoised images during distributed rendering.
Innovation Solution
A distributed denoising algorithm that utilizes machine learning, specifically a convolutional neural network, continuously trained and updated 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 capacity and speed are improved, but access to all rendered pixels for denoising becomes limited
Solution Approach 1:
The patent divides the rendering system into multiple distributed devices (render nodes), each independently rendering portions of the final image. This segmentation enables parallel processing and improved rendering capacity while maintaining the ability to share necessary data through the defined interface protocol.
Solution Approach 2:
The patent introduces an intermediary data exchange mechanism that allows render nodes to share minimal necessary information (such as denoising parameters and partial pixel data) without requiring full access to all rendered pixels across the distributed system. This mediator approach resolves the information access limitation while preserving distributed rendering benefits.
2Loss of information
If existing denoising frameworks operate on a single machine, then all rendered pixels are accessible for denoising, but rendering scalability is limited
Solution Approach 1:
The patent implements a dynamic denoising framework where the denoising process can adapt to distributed rendering environments. The system dynamically adjusts data exchange requirements and denoising parameters based on the distributed architecture, enabling scalability while maintaining denoising effectiveness through conditional information sharing.
Solution Approach 2:
The patent changes the parameters of the denoising operation to work within distributed constraints. Instead of requiring access to all rendered pixels, the system modifies the denoising approach to use locally available data combined with selectively exchanged parameters, enabling the framework to scale across multiple devices while preserving image quality.
3Speed
If ray tracing is performed in real-time, then performance requirements are improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by performing denoising operations on previously rendered frames or partial results before final image assembly. This allows the system to prepare denoised data in advance, reducing real-time computational burden while maintaining high performance through pre-computed denoising parameters and cached results.
Solution Approach 2:
The patent implements partial action by applying denoising selectively to specific regions or pixel types rather than processing the entire image uniformly. This reduces computational complexity by focusing resources on areas requiring denoising while skipping already-clean regions, enabling real-time performance with reduced computational overhead.
Data Source
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AI summary
Apparatus and method for ray tracing acceleration using a grid primitive. For example, one embodiment of an apparatus comprises: a grid primitive generator to generate a grid primitive comprising a plurality of adjacent interconnected primitives; a bitmask generator to generate a bitmask associated with the grid primitive, the bitmask comprising a plurality of bitmask values, each mask value associated with a primitive of the grid primitive; a ray tracing engine comprising traversal and intersection hardware logic to perform traversal and intersection operations in which rays are traversed through a hierarchical acceleration data structure and intersections between the rays and one or more of the adjacent interconnected primitives identified, wherein the ray tracing engine is to read the bitmask to determine a first set of primitives from the grid primitive on which to perform the traversal and intersection operations and a second set of primitives from the grid primitive on which the traversal and intersection operations will not be performed.