Sub-Pixel Ray Tracing for Scalable Synthetic Vision Data
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Solution Overview
Problem
Existing real capture data for machine learning and computer vision tasks is less scalable and less accurate due to manual image capture and post-processing, while synthetic data offers scalability and accuracy advantages.
Innovation Solution
A computer device simulates virtual environments using ray tracing to generate sub-pixel data for each ray, storing it in image files for scalable and accurate training of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real capture data is used for machine learning tasks, then ground truth data can be obtained through post-processing, but scalability is reduced due to manual capture and labeling requirements
Solution Approach 1:
The patent creates synthetic images by rendering virtual copies of real-world scenes using ray tracing algorithms. Instead of capturing real images manually, the system generates synthetic representations that preserve ground truth information inherently through the rendering process, eliminating the need for manual capture and post-processing labeling
Solution Approach 2:
The system performs preliminary rendering of synthetic images with embedded ground truth data before any machine learning tasks. By pre-computing the synthetic scenes with known ground truth during the rendering phase, the system eliminates the need for subsequent manual labeling post-processing steps
2Measurement precision
If real capture data with post-processing ground truth is used, then labeled data can be obtained, but accuracy may be reduced compared to synthetic data
Solution Approach 1:
The system creates synthetic copies of real scenes through physically accurate rendering, where the ground truth is inherently known from the virtual scene parameters. This synthetic copying approach provides more accurate ground truth than real captured images, as the rendering process precisely controls all scene parameters without measurement errors
3Measurement precision
If synthetic data is generated through rendering, then scalability and accuracy are improved, but computational complexity increases
Solution Approach 1:
The rendering process is segmented into multiple independent stages: scene setup, ray generation, intersection testing, shading, and image composition. Each stage processes independent data structures and can be parallelized, reducing the computational complexity burden of the overall rendering process while maintaining high accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides scalable and accurate synthetic data for computer vision tasks, improving processes like background replacement and lens distortion by leveraging sub-pixel data for enhanced training and evaluation.
Implementation Method 1
perform ray tracing to render a view of the simulated virtual environment. The ray tracing may include generating a plurality of rays for one or more pixels of the rendered view
Data Source
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AI summary
A computer device is provided that includes a processor configured to simulate a virtual environment based on a set of virtual environment parameters, and perform ray tracing to render a view of the simulated virtual environment. The ray tracing includes generating a plurality of rays for one or more pixels of the rendered view of the simulated virtual environment. The processor is further configured to determine sub-pixel data for each of the plurality of rays based on intersections between the plurality of rays and the simulated virtual environment, and store the determined sub-pixel data for each of the plurality of rays in an image file.