Distributed Denoising via Ghost Region Data Exchange
Find Innovative SolutionsGenerate Solutions
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, specifically a convolutional neural network, which continuously trains and updates its weights during runtime using a sub-region of the image, and a distributed processing method where nodes exchange 'ghost region' data to perform denoising operations across multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing denoising frameworks operate on a single machine, then they can access all rendered pixels for computing denoised images, but they cannot scale to distributed rendering across multiple devices
Solution Approach 1:
The patent divides the rendering workload and data processing across multiple devices or nodes in a distributed system. Each node processes a portion of the rendering task and maintains local data structures (BVH, transform matrices) that can operate independently while contributing to the overall denoising result, enabling distributed rendering without losing access to necessary pixel information
Solution Approach 2:
The patent introduces a distributed data structure and communication mechanism that acts as an intermediary between multiple rendering nodes. This allows nodes to share and access rendering data (such as ghost region data) across the distributed system, maintaining information accessibility while enabling multi-device operation
2Measurement precision
If extensive training data is used for denoising model training, then denoising quality may improve, but the system requires large amounts of pre-computed training data that may not generalize well to new scenarios
Solution Approach 1:
The patent implements a denoising model that can dynamically adapt and update during runtime based on actual rendering data. Instead of relying solely on static pre-trained models, the system continuously learns from new scenarios and adjusts its parameters, enabling both high denoising quality and adaptability to new situations without requiring extensive pre-computed training data for each specific scenario
Solution Approach 2:
The denoising model performs self-training or self-adjustment using the rendering data it processes during operation. The system uses its own operational data to improve its denoising capabilities, eliminating the need for separate extensive training phases with scenario-specific data while maintaining both quality and generalization
3Speed
If ray tracing is performed in real-time, then performance requirements increase, but traditional ray tracing is too resource-intensive for real-time performance
Solution Approach 1:
The patent changes key parameters of the ray tracing process, including using compressed transform matrices with quantized values, optimized bounding volume hierarchy structures, and efficient traversal algorithms. These parameter changes reduce the computational complexity and resource requirements of ray tracing operations while maintaining real-time performance capability
Solution Approach 2:
The patent extracts and separates computationally intensive operations from the main ray tracing pipeline. By identifying and removing bottleneck operations or pre-computing certain data structures offline, the system reduces real-time computational requirements while maintaining image quality and performance
Data Source
AI summary
Apparatus and method for context-aware compression. For example, one embodiment of an apparatus comprises: ray traversal/intersection circuitry to traverse rays through a hierarchical acceleration data structure to identify intersections between rays and primitives of a graphics scene; matrix compression circuitry/logic to compress hierarchical transformation matrices to generate compressed hierarchical transformation matrices by quantizing N-bit floating point data elements associated with child transforms of the hierarchical transformation matrices to variable-bit floating point numbers or integers comprising offsets from a parent transform of the child transform; and an instance processor to generate a plurality of instances of one or more base geometric objects in accordance with the compressed hierarchical transformation matrices.