Synthetic Data Generation for Instance Segmentation
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Solution Overview
Problem
The existing methods for generating annotated training data for machine learning models, such as neural networks, are time-consuming and costly due to the need for manual annotation, which hinders efficient and cost-effective training processes.
Innovation Solution
The system generates annotated training data by supplementing imaging data with synthetic or real-world objects, using three-dimensional models rendered and overlaid onto the data, allowing for automated extraction of masks or silhouettes that can be used to train machine learning models, thereby reducing the reliance on manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation processes are used to create labeled imaging data, then training data accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent uses synthetic data generation by rendering three-dimensional models of objects onto background images to create realistic training data copies. This copying approach generates labeled training images automatically without manual annotation, maintaining accuracy while dramatically reducing time and cost requirements.
Solution Approach 2:
The patent introduces an intermediary synthetic data generation system that bridges the gap between raw imaging data and manually annotated training data. The system uses three-dimensional models and rendering processes as intermediaries to automatically generate labeled training data with accurate object masks and annotations.
2Measurement precision
If manual annotation processes are used to create labeled imaging data, then training data accuracy is improved, but cost increases
Solution Approach 1:
The patent uses synthetic data generation by rendering three-dimensional models of objects onto background images to create realistic training data copies. This copying approach generates labeled training images automatically without manual annotation, maintaining accuracy while dramatically reducing time and cost requirements.
Solution Approach 2:
The system performs self-service annotation by automatically generating labels, masks, and training data annotations through the synthetic rendering process. The three-dimensional model rendering system autonomously creates accurate object boundaries and labels without requiring human annotators, eliminating manual labor costs.
3Extent of automation
If synthetic objects are rendered and overlaid on imaging data, then automation is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal three-dimensional modeling system that can handle multiple object types and scenarios through a single rendering platform. This multi-functional approach consolidates various annotation tasks into one automated system, managing complexity while maintaining high automation levels across different training data generation needs.
Data Source
AI summary
Systems and methods to automatically generate training data for machine learning models may include an imaging device to capture imaging data, an image processing or rendering system to receive the imaging data and render a three-dimensional model of an object of interest overlaying the imaging data, an automatic mask extraction or generation system to extract or determine a mask, label, or annotation associated with the three-dimensional model and a plurality of pixels associated with the object of interest from a perspective of the imaging device, and a machine learning model to receive the imaging data and the mask as training data.