Meta-Learning 6D Pose Estimation for Multi-Object Adaptation
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Solution Overview
Problem
Conventional algorithms for estimating the 6D pose of an object require laborious retraining for different object categories, leading to increased resource consumption and inefficiency in detecting new objects.
Innovation Solution
A method utilizing a meta-learning algorithm with image data, including target and labeled comparison images, to ascertain the 6D pose, allowing flexible adaptation to various object categories without retraining, using features extraction, key point determination, and offset calculation to achieve precise pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional algorithms are used for 6D pose estimation, then pose accuracy can be achieved for specific object categories, but laborious retraining is required for different object categories leading to increased resource consumption
Solution Approach 1:
The patent applies universality by training a single pose estimation model on diverse object categories simultaneously using multi-object pose estimation datasets. This allows the model to generalize across different object types without requiring separate training for each category, thereby reducing resource consumption while maintaining pose estimation accuracy for various objects including tools, household items, and industrial components
Solution Approach 2:
The patent employs parameter changes by utilizing multi-scale image processing and adjusting network parameters to handle varying object sizes and categories. The model processes images at multiple scales and adapts parameters dynamically to estimate poses for different object categories without retraining, optimizing resource usage while preserving measurement precision
2Measurement precision
If conventional algorithms are used for 6D pose estimation, then pose accuracy can be achieved for specific object categories, but retraining is required for new object categories reducing operational efficiency
Solution Approach 1:
The patent achieves operational efficiency by creating a universal pose estimation model that can handle multiple object categories simultaneously. The model processes diverse objects including tools, household items, and industrial components without requiring retraining, thereby maintaining high productivity and measurement precision across different application scenarios
Solution Approach 2:
The patent applies preliminary action by pre-training the model on comprehensive multi-object datasets that include various categories and viewing angles. This preliminary training enables the model to immediately process new objects without additional training steps, maintaining both accuracy and operational efficiency when encountering previously unseen object categories
3Measurement precision
If category-specific models are used, then accurate detection for trained categories is achieved, but the system complexity increases due to multiple models required for different categories
Solution Approach 1:
The patent reduces system complexity by implementing a single universal pose estimation model that replaces multiple category-specific models. The model uses shared feature extraction networks and unified loss functions to handle diverse object categories, thereby maintaining detection accuracy while significantly simplifying the overall system architecture and reducing computational overhead
Solution Approach 2:
The patent applies merging by combining multiple object category processing capabilities into a single integrated model. The model processes different object types including tools, household items, and industrial components through unified neural network architectures, eliminating the need for separate models and reducing system complexity while preserving detection precision
Data Source
AI summary
A method for ascertaining a 6D pose of an object. The method includes the following steps: providing image data, wherein the image data include target image data showing the object and labeled comparison image data relating to the object, and ascertaining the 6D pose of the object based on the provided image data using a meta-learning algorithm.