Autoencoder Domain Adaptation for Object Pose Detection
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Solution Overview
Problem
Training deep neural networks for object pose detection requires large labeled real images, which are expensive and time-consuming to obtain, and synthetic images may not achieve desired performance in real environments due to the gap between synthetic and real images.
Innovation Solution
The method involves training two variational autoencoders to generate similar synthetic output images from both synthetic and real input images, and using these outputs to train a neural network or multi-layer perceptron for object pose detection in simulated and actual environments, allowing for improved performance with a small real image dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large labeled real image datasets are used for training deep neural networks, then object pose detection performance is improved, but time consumption and cost increase
Solution Approach 1:
The patent creates a synthetic copy of the real-world environment using a simulation engine. Instead of collecting and labeling numerous real images, the system generates synthetic images from simulated 3D models and physics engines, copying the essential visual and physical characteristics of the real environment while avoiding the time-consuming process of real image acquisition and annotation
Solution Approach 2:
The patent performs preliminary action by pre-training the neural network on synthetic images generated from simulated environments before deploying it to real-world scenarios. This pre-training phase using synthetic data prepares the model in advance, reducing the need for extensive real image datasets and accelerating the overall deployment timeline
2Loss of time
If synthetic images are used for training, then time consumption and cost are reduced, but performance in real environments deteriorates due to gap between synthetic and real images
Solution Approach 1:
The patent introduces domain adaptation techniques as an intermediary layer between synthetic and real image domains. The domain adaptation module learns to align the feature distributions of synthetic and real images, bridging the domain gap and enabling the model trained on synthetic data to perform effectively on real images without requiring extensive real image datasets
Solution Approach 2:
The patent employs parameter changes by adjusting rendering parameters, lighting conditions, and physical parameters in the simulation engine to make synthetic images more closely match real image characteristics. By varying these parameters during synthetic data generation, the system creates a more diverse and realistic training set that improves transferability to real environments
Data Source
AI summary
Methods and a system are provided for detecting object pose. A method includes training, by a processor, a first autoencoder (AE) to generate synthetic output images based on synthetic input images. The method further includes training, by the processor, a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images. The method also includes training, by the processor, a neural network (NN) to detect the object pose using the synthetic output images generated by the first and second AEs. The method additionally includes detecting and outputting, by the processor, a pose of an object in a real input test image by inputting the real input test image to the second AE to generate a synthetic image therefrom, and inputting the synthetic image to the NN to generate an NN output indicative of the pose of the object.