Physics-Based Synthetic Datasets for Object Detection Training
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Solution Overview
Problem
Existing methods for creating training datasets for object detection neural networks in robotic manipulation tasks are time-consuming and inefficient, requiring manual labeling of real-world images, which is not scalable, and suffer from the reality gap between physics simulators and the real world.
Innovation Solution
A synthetic dataset is created using a physics-based simulator to render images with randomized parameters, generating a variety of simulated environments, and training a deep learning model with domain randomization and active learning to adapt to real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of real-world images is used to create training datasets, then the quality and realism of training data is improved, but the time required and scalability deteriorate
Solution Approach 1:
The patent creates synthetic copies of real-world images through physics-based simulation. Instead of manually labeling real images, the system renders synthetic images that replicate real-world scenarios, including object appearances, lighting, and camera perspectives. These synthetic images serve as training data without requiring manual annotation, thus maintaining data quality while eliminating time-consuming manual labeling processes.
Solution Approach 2:
The patent performs preliminary actions by pre-rendering large quantities of synthetic training images before actual model training begins. The system pre-generates diverse scenarios with various objects, lighting conditions, and camera angles, creating a comprehensive training dataset in advance. This preliminary generation of training data eliminates the need for time-consuming manual labeling during the model development phase.
2Productivity
If physics-based simulators are used to generate synthetic images, then the time required for dataset creation is reduced, but the reality gap between simulation and real world worsens
Solution Approach 1:
The patent systematically varies multiple parameters in the physics-based simulator to generate diverse synthetic images. These parameters include lighting conditions (intensity, direction, color temperature), camera settings (position, angle, focal length), object positions and orientations, and environmental factors. By changing these parameters across many simulations, the system creates a comprehensive distribution of scenarios that better represents real-world variability, reducing the reality gap while maintaining high productivity.
Solution Approach 2:
The patent introduces dynamic elements to the simulation by randomly varying object poses, positions, and configurations across different rendered images. Instead of static scenes, the system generates dynamic variations where objects appear in different orientations, locations, and arrangements. This dynamic approach creates more realistic training data that helps the model generalize better to real-world scenarios while maintaining efficient synthetic generation.
3Quantity of substance
If transfer learning with pretrained networks is used, then the number of images required per object class is reduced, but the total time for collecting and labeling images remains significant
Solution Approach 1:
The patent replaces the need to collect and label real images (even in reduced quantities) with synthetic image generation. Instead of using transfer learning to reduce the number of real images needed, the system generates synthetic copies of images for all object classes through physics-based rendering. This approach eliminates the manual image collection and labeling process entirely, reducing both the quantity of real images needed and the associated time investment.
Data Source
AI summary
A computer-implemented method for building an object detection module uses mesh representations of objects belonging to specified object classes of interest to render images by a physics-based simulator. Each rendered image captures a simulated environment containing objects belonging to multiple object classes of interest placed in a bin or on a table. The rendered images are generated by randomizing a set of parameters by the simulator to render a range of simulated environments. The randomized parameters include environmental and sensor-based parameters. A label is generated for each rendered image, which includes a two-dimensional representation indicative of location and object classes of objects in that rendered image frame. Each rendered image and the respective label constitute a data sample of a synthetic training dataset. A deep learning model is trained using the synthetic training dataset to output object classes from an input image of a real-world physical environment.