Synthetic Image Generation for Machine Learning Training
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Solution Overview
Problem
Current machine learning models for object detection and classification require vast amounts of labeled real images, which are resource-intensive and prone to errors, and training solely on synthetic data can result in a domain gap leading to poor performance when applied to real-world data.
Innovation Solution
The method generates synthetic images by combining foreground and background 3D object models with randomized sizes, rotations, and occlusions, and uses a curriculum strategy to train machine learning models, ensuring they learn geometric and visual appearances of objects effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-assigned labels are used for real images, then training data quality improves, but computational resources and time consumption increase significantly
Solution Approach 1:
The patent creates synthetic copies of training data through 3D rendering and simulation, generating synthetic images with automatically assigned ground truth labels. This copying approach eliminates the need for time-consuming human annotation while maintaining training data quality, as the synthetic data preserves the essential geometric and visual characteristics needed for object detection and classification.
Solution Approach 2:
The synthetic data generation system performs self-service by automatically generating training data with precise ground truth labels through simulation and rendering processes. The system does not require external human annotators, as the simulation environment inherently provides accurate object positions, sizes, and labels, thereby eliminating manual labor while maintaining data quality.
2Productivity
If synthetic training data is used, then resource consumption decreases, but domain gap between synthetic and real images increases
Solution Approach 1:
The patent employs domain randomization by systematically varying rendering parameters such as lighting conditions, camera angles, object positions, and background environments in the synthetic data generation process. This parameter changes approach ensures that the model learns robust features that generalize well to real-world variations, bridging the domain gap while maintaining high data generation efficiency.
Solution Approach 2:
The system introduces dynamic elements in the synthetic data generation by randomizing scene configurations, object poses, and environmental conditions across different training samples. This dynamics approach creates diverse training scenarios that prepare the model for real-world variability, improving model performance on real data while keeping resource consumption low compared to collecting and annotating real images.
3Reliability
If real images are collected and labeled, then training data realism improves, but network resources and client device resources increase
Solution Approach 1:
Instead of transmitting and processing real images across networks and devices, the patent generates local synthetic copies that replicate the essential visual characteristics and geometric properties needed for training. This copying approach maintains training data realism while eliminating the need for resource-intensive network transmission and client-side rendering of real images.
Solution Approach 2:
The system performs self-service by generating all training data locally through simulation, eliminating dependence on external real image sources and the associated network and device resources. The synthetic data generation process is self-contained, requiring minimal external resources while producing realistic training samples.
4Adaptability or versatility
If domain randomization is applied to synthetic data, then model generalization improves, but synthetic image complexity increases
Solution Approach 1:
The patent systematically varies rendering parameters such as lighting, camera angles, and object positions to achieve domain randomization. While this increases the complexity of the generation process, it is managed through automated simulation pipelines that efficiently handle parameter variations, resulting in improved model generalization without requiring manual intervention for each parameter change.
Solution Approach 2:
The synthetic data generation system is designed with multi-functionality to handle various rendering parameters and scene configurations through a single unified pipeline. This universality allows the system to manage the complexity of domain randomization efficiently, applying multiple randomization techniques through one integrated process rather than separate systems, thereby improving model generalization while controlling overall system complexity.
Data Source
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AI summary
Particular techniques for generating synthetic images and/or for training machine learning model(s) based on the generated synthetic images. For example, training a machine learning model based on training instances that each include a generated synthetic image, and ground truth label(s) for the generated synthetic image. After training of the machine learning model is complete, the trained machine learning model can be deployed on one or more robots and/or one or more computing devices.