Neural Image Reconstruction for Low-Cost 3D Ray Tracing
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Solution Overview
Problem
Existing 3D rendering techniques face challenges in achieving high-quality image reconstruction with reduced computational cost, often resulting in artifacts due to insufficient or excessive light rays, which increases computational load.
Innovation Solution
Utilizing a neural sampling map generation model and a neural reconstruction model to generate a sampling map and perform denoising and super sampling, thereby reducing artifacts and improving image resolution while minimizing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional ray tracing is performed for all pixels to ensure high image quality, then image resolution and quality are improved, but computational cost increases significantly
Solution Approach 1:
The patent applies local quality by differentiating treatment between different pixel regions based on their reliability scores. High-reliability pixels from previous frames are reused without ray tracing, while low-reliability pixels undergo full ray tracing. This selective approach maintains overall image quality while reducing computational cost by avoiding redundant processing in stable regions.
Solution Approach 2:
The patent performs preliminary action by pre-calculating reliability scores and confidence maps for pixels before determining the rendering strategy. The neural network predicts pixel reliability in advance, allowing the system to prepare a plan for ray tracing allocation before actual rendering, thus optimizing computational resource distribution efficiently.
2Productivity
If ray tracing is reduced to lower computational cost, then computational efficiency is improved, but image quality deteriorates due to artifacts
Solution Approach 1:
The patent implements feedback by using neural networks to analyze rendered images and generate reliability scores that indicate pixel quality. These scores feed back into the rendering process to guide subsequent ray tracing decisions. The confidence map generated from previous frame results provides continuous feedback to adjust rendering efforts, ensuring quality maintenance while optimizing computational usage.
Solution Approach 2:
The patent replaces the traditional mechanical ray tracing approach with a neural network-based prediction system. Instead of uniformly applying ray tracing mechanics to all pixels, the system uses machine learning models to predict which pixels require processing, substituting computational mechanics with intelligent decision-making that reduces overall computational load while maintaining image quality.
3Reliability
If full ray tracing is performed to avoid artifacts, then image reliability is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by performing ray tracing only on necessary pixels rather than all pixels. The system identifies and processes only those pixels with low reliability scores or high confidence of change, applying partial rendering effort where needed while skipping redundant processing in stable regions, thus reducing processing time while maintaining sufficient image reliability.
Solution Approach 2:
The patent discards redundant rendering information from previous frames for high-reliability pixels and recovers it by reusing those pixel values in the current frame. The confidence map mechanism allows the system to discard full ray tracing for stable pixels while recovering the visual quality through intelligent pixel reuse, significantly reducing processing time without compromising overall image reliability.
Data Source
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AI summary
A method and apparatus with image processing is provided. The processor-implemented method includes generating a warped image frame by warping a first reconstructed image frame of a first time point based on first change data corresponding to a change between a first rendered image frame of the first time point and a second rendered image frame of a second time point that is different from the first time point; generating, using a neural reconstruction model and based on the second rendered image frame and the warped image frame, a confidence map representing a second reconstructed image frame of the second time point and confidence scores of pixels of the second reconstructed image frame; and generating a third rendered image frame of a third time point, different from the first and second time points, by ray tracing for each of a plurality of pixels of the third rendered image frame based on the confidence map.