Synthetic 3D Image Generation for Computer Vision Training
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Solution Overview
Problem
Existing machine learning systems for computer vision applications require large, varied image databases that are inefficient and costly to create, often relying on manual capture or web crawlers, which are unsatisfactory for scaling and maintaining robust object recognition systems.
Innovation Solution
The system generates synthetic 3D object images with varying backgrounds, poses, and illumination, using a 3D model to produce RGB-D images that can be used to train and test object recognition classifiers, reducing the need for manual data capture and enabling efficient database creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual capture techniques are used to obtain image data, then the quality and control of image database can be maintained, but the time and cost required to build and maintain the database increases significantly
Solution Approach 1:
The patent uses a 3D model as a digital copy of the physical object to generate synthetic images. This virtual copy can be rendered repeatedly without additional capture time, eliminating the need to physically recapture the object from multiple angles and lighting conditions while maintaining consistent quality control.
Solution Approach 2:
The patent replaces the mechanical process of physical image capture with a computational rendering process. Instead of using cameras and physical objects, the system uses software to generate images from 3D models, substituting mechanical capture operations with digital synthesis operations that are faster and more controllable.
2Quantity of substance
If web crawler software is used to gather image data, then the quantity of images can be increased, but the control and quality assurance of the database deteriorates
Solution Approach 1:
The system generates synthetic images from 3D model copies, allowing unlimited quantity generation without relying on web crawling. Each rendered image is a controlled synthesis from the digital model, ensuring quality consistency while providing unlimited quantity of training data.
Solution Approach 2:
The system is self-sufficient in generating image data without needing to externally scrape or crawl the web. The 3D model serves as a self-contained source that can generate unlimited variations independently, eliminating the need for external data collection processes that compromise quality control.
3Measurement precision
If thousands of images per object are captured to support robust recognition, then the recognition accuracy improves, but the complexity and cost of data collection and maintenance increases
Solution Approach 1:
The patent uses a single 3D model copy to generate all required image variations through rendering. This eliminates the complexity of coordinating multiple cameras, lighting setups, and manual capture processes while still producing thousands of diverse training images for robust recognition.
Solution Approach 2:
The system achieves image diversity by systematically varying rendering parameters such as lighting conditions, camera angles, and background environments rather than physically capturing each variation. This parameter-based approach simplifies the data collection system while maintaining the quantity and variety needed for accurate recognition.
Data Source
AI summary
Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.