Synthetic Virtual Scene Generation for ML Training Data
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Solution Overview
Problem
The effort and resources required to train machine learning models to accurately recognize real-world objects are substantial due to the need for large quantities of varied and labeled training data, which is time-consuming and inefficient to collect and annotate manually.
Innovation Solution
A system and method for generating virtual scene variations based on a 3D model of a real-world object, allowing for automated creation of synthetic training data with machine labeling, enabling faster and more efficient training of machine learning models to identify real-world objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection and annotation of training data is used, then data quality and representativeness are improved, but time consumption and resource effort increase substantially
Solution Approach 1:
The patent generates synthetic training data by creating virtual copies of real-world objects and scenes through 3D modeling and rendering. These synthetic images replicate real-world conditions while being automatically annotated, eliminating the need for manual annotation of numerous real-world images and significantly reducing time consumption.
Solution Approach 2:
The system varies multiple parameters of the virtual scene including lighting conditions, camera angles, object positions, and environmental factors to generate diverse training data. This parameter variation allows comprehensive coverage of real-world scenarios without requiring physical collection of each variant, maintaining data quality while reducing acquisition time.
2Adaptability or versatility
If large quantities of varied training data are collected, then model accuracy and comprehensiveness are improved, but the complexity and effort of data preparation increase
Solution Approach 1:
The patent segments the data generation process into distinct controllable parameters and scene components. By dividing the complex task of creating varied training data into manageable segments (object models, scene elements, lighting conditions, camera parameters), the system achieves comprehensive data coverage while simplifying the preparation process through automated control of each segment.
Solution Approach 2:
The synthetic data generation system is self-annotating, automatically producing labeled training data without requiring external human annotation services. This self-service capability reduces data preparation complexity while enabling unlimited variation in training data quantities and types, as the system can independently generate diverse scenarios and automatically label them.
3Productivity
If automated synthetic data generation is used, then training speed and efficiency are improved, but the challenge of creating realistic and varied scenes increases
Solution Approach 1:
The patent employs a universal 3D modeling framework that can represent multiple object types, scene environments, and lighting conditions within a single integrated system. This multi-functional approach enables the generation of realistic and varied scenes across different domains (indoor, outdoor, natural lighting, artificial lighting) using the same automated pipeline, maintaining high productivity while achieving scene generation realism.
Data Source
AI summary
The disclosure herein describes training a machine learning model to recognize a real-world object based on generated virtual scene variations associated with a model of the real-world object. A digitized three-dimensional (3D) model representing the real-world object is obtained and a virtual scene is built around the 3D model. A plurality of virtual scene variations is generated by varying one or more characteristics. Each virtual scene variation is generated to include a label identifying the 3D model in the virtual scene variation. A machine learning model may be trained based on the plurality of virtual scene variations. The use of generated digital assets to train the machine learning model greatly decreases the time and cost requirements of creating training assets and provides training quality benefits based on the quantity and quality of variations that may be generated, as well as the completeness of information included in each generated digital asset.