Online 3D Pose Estimation Using Simplified Constellations
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Solution Overview
Problem
Current methods for 3D pose estimation in six degrees of freedom (6DoF) tracking face challenges such as the need for large amounts of training data, high computational requirements, and limitations in detecting objects with wide view ranges, especially in offline training scenarios, which are not suitable for real-time applications like augmented reality headsets.
Innovation Solution
The implementation of online training methods that acquire and verify 2D features from images captured by a camera, storing them in a training dataset to enable efficient object detection and pose estimation across a wider view range, even when the object is lost out of the trained view, using techniques like edge detection and feature matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline training with large view-range is performed, then detection accuracy across wide azimuth and elevation is improved, but training data requirements and computational resources increase significantly
Solution Approach 1:
The system transitions from static offline training to dynamic online training that continuously adapts during runtime. The trainer component updates the pose estimation model using real-time observed poses and image data, allowing the system to learn new viewpoints and object variations without requiring pre-collected datasets covering all possible views.
Solution Approach 2:
The system performs self-training by utilizing its own operational data. The pose tracker provides observed poses that are verified and used to train the model further, creating a self-improving loop where the system learns from its own performance during deployment rather than relying on external training data sources.
2Adaptability or versatility
If offline training covers wide view-range, then object detection across all viewpoints is improved, but the system cannot adapt to new viewpoints outside trained range
Solution Approach 1:
The pose estimation model evolves dynamically during runtime through online training. As the system encounters new viewpoints and object variations, the trainer continuously updates the model parameters, enabling adaptive expansion of the effective view-range without requiring a complete retraining process or increasing structural model complexity.
Solution Approach 2:
The system implements a feedback loop where the pose tracker provides observed poses that are verified against the model predictions. This feedback mechanism allows the system to identify gaps in its current knowledge and target specific areas for improvement through online training, efficiently expanding view-range coverage where needed.
3Measurement precision
If deep networks are used for pose estimation, then accuracy is improved, but inference time increases for mobile hardware
Solution Approach 1:
The system performs lightweight online training during runtime using the observed poses and image data. This self-service training approach allows the model to improve accuracy progressively without requiring heavy computational resources during inference, as the training occurs incrementally in the background using available hardware capabilities.
Solution Approach 2:
The system adjusts model parameters dynamically through online training rather than relying on fixed pre-trained deep networks. This allows the model to achieve high accuracy with simpler architectures by adapting parameters to specific deployment conditions and hardware constraints, reducing inference time while maintaining precision.
4Productivity
If template matching is used for pose estimation, then inference speed is improved, but view-range is limited to trained templates
Solution Approach 1:
The system combines fast template matching with dynamic online training. The template matching provides quick initial pose estimates, while the online training component continuously expands the effective view-range by learning from new observations. This hybrid approach maintains high inference speed while progressively improving adaptability to new viewpoints.
Solution Approach 2:
The system performs preliminary template matching to obtain initial pose estimates quickly, then uses these estimates to guide online training. This preliminary action allows the system to maintain fast inference performance while setting the stage for subsequent adaptive expansion of view-range coverage through learned updates.
Data Source
AI summary
A non-transitory computer readable medium storing instructions to cause one or more processors to acquire an image data sequence containing images of an object in a scene along time and track a pose of the object through an object pose tracking algorithm. The processor may further, acquire a first pose of the object in a first image of the image data sequence, the first pose being a result of tracking a pose of the object through an object pose tracking algorithm, verify the first pose, extract 2D features of the object from the first image when the first pose is verified, and store a training dataset containing the extracted 2D features and the corresponding verified first pose in the one or more memories or other one or more memories.