Synthetic 3D Image Generation for Computer Vision Training Data
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Solution Overview
Problem
Computer vision models require large volumes of pre-labeled empirical imagery for effective pattern detection and classification, which is a challenge when such examples are scarce or limited in spatial, environmental, spectral, or depth perspectives.
Innovation Solution
A method and system for generating synthetic three-dimensional images of objects, combining them with background images, simulating radiant energy reflection, and producing two-dimensional images from different perspectives, enabling the creation of diverse training data for computer vision models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If empirical imagery is used for training computer vision models, then the models can achieve accurate pattern detection and classification, but the availability of sufficient pre-labeled empirical data is limited
Solution Approach 1:
The patent creates synthetic copies of empirical imagery through 3D modeling and rendering. Virtual representations of objects are generated with controlled parameters, allowing unlimited replication of training samples without requiring additional physical objects or manual labeling efforts
Solution Approach 2:
The system performs preliminary actions by pre-defining 3D object models, material properties, and scene configurations before generating training images. This allows the system to rapidly produce diverse training samples by varying parameters rather than capturing and labeling each image individually
2Adaptability or versatility
If diverse perspectives and environmental conditions are incorporated in training data, then the model's adaptability improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent implements dynamic parameter control in the rendering system, allowing real-time adjustment of lighting conditions, camera angles, object positions, and environmental factors. This enables comprehensive coverage of diverse scenarios through systematic parameter variation rather than manual data collection
Solution Approach 2:
The 3D rendering system serves multiple functions: generating diverse training images, controlling environmental conditions, varying perspectives, and creating labeled data automatically. This multi-functional approach consolidates what would otherwise require separate data collection systems into a unified platform
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
This approach allows computer vision systems to accurately detect physical assets in real-world operations by training models with a broader range of synthetic images, addressing the limitations of real-world data scarcity and improving detection and classification performance.
Implementation Method 1
simulating, in the processing device: reflection of at least one type of radiant energy from the surface of the object and/or from the background according to a set of parameters
Data Source
AI summary
Exemplary systems and methods are directed to generating synthetic data for computer vision. A processing device generates a synthetic three-dimensional (3D) image of an object. A background image is selected, and a composite image is generated by combining the 3D image of the object and the background image. The processing device simulates: reflection or emission of at least one type of radiant energy from the surface of the object and/or the background according to a set of parameters associated with at least one of the object and the background image; and a reflectance or emittance measurement of the at least one type of radiant energy from the surface of the object by a sensor device configured for detecting the at least one type of radiant energy. The processing device generates a plurality of two-dimensional (2D) simulated images of different perspectives of the object based on simulation data.