Neural Network Orbit Generation for One-Shot Pose Recognition
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Solution Overview
Problem
Conventional systems face challenges in jointly recognizing the identity and pose of objects from a single image without additional sensory information, requiring separate models for identity recognition and pose estimation, which limits their robustness and efficiency in real-world applications.
Innovation Solution
The development of a method and system for one-shot joint instance and pose recognition, where a neural network generates an orbit of additional poses for a reference object based on a single instance, allowing recognition of similar objects with different poses from a single image, by disentangling identity and pose information within a single model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for identity recognition and pose estimation, then the system can handle each task independently, but the overall system complexity increases and robustness decreases
Solution Approach 1:
The patent combines identity recognition and pose estimation into a single integrated neural network model. The network simultaneously processes input images to extract both identity features and pose parameters, eliminating the need for separate models and their associated interfaces, thereby reducing system complexity while improving robustness through unified learning.
Solution Approach 2:
The neural network is designed as a universal model that performs multiple functions: it recognizes object identity, estimates pose parameters (rotation and translation), and generates synthetic views. This multi-functional approach replaces multiple specialized models with a single versatile system, reducing overall complexity while maintaining reliability.
2Measurement precision
If additional sensory information is provided, then recognition accuracy improves, but the requirement for additional sensors and processing increases system complexity
Solution Approach 1:
The system generates synthetic view images by copying and transforming the input image through learned pose transformations. Instead of requiring additional physical sensors to capture objects from multiple angles, the network creates virtual copies of the object in different poses, achieving accurate recognition without increasing hardware complexity.
Solution Approach 2:
The patent replaces the mechanical approach of using multiple physical sensors and cameras with a computational approach. The neural network uses learned transformations to synthesize additional views mathematically, substituting physical sensing infrastructure with information processing, thereby maintaining accuracy while reducing system complexity.
3Adaptability or versatility
If the system is trained on multiple environments, then adaptability to new environments improves, but training time and data requirements increase
Solution Approach 1:
The system enables models trained on one environment to serve themselves in new environments through domain adaptation techniques. The network learns environment-specific transformations and adjustments automatically when deployed, allowing a single training process to benefit multiple environments without requiring separate training campaigns for each setting.
Solution Approach 2:
The patent employs parameter adaptation mechanisms that allow the model to adjust its internal parameters when encountering new environments. By learning environment-specific parameter transformations during deployment, the system achieves environmental adaptability without requiring retraining from scratch, significantly reducing the time loss associated with extensive multi-environment training.
Data Source
AI summary
A method of one-shot joint instance and pose recognition in an artificial neural network includes receiving a first instance of a reference object from a reference image. The reference object has a first identity and a first pose in the first instance. The method also includes generating a first orbit of the reference object comprising additional poses including a second pose for the reference object. The method further includes recognizing a second instance of an example object from an example image. The example object has the first identity and the second pose in the second instance.