Pose Decoupling Prediction Model for Rotation and Translation Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current object pose estimation methods face challenges in accurately predicting rotation and translation amounts due to factors like occlusion, illumination changes, and symmetry, making it difficult to achieve high accuracy in both rotation and translation estimates.
Innovation Solution
A pose decoupling prediction model is introduced, which includes a base network for feature extraction, a rotation amount branch network, and a translation amount branch network, allowing for separate prediction and decoupling of rotation and translation, enabling different strategies for each based on their properties, and improving accuracy through targeted prediction methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a unified pose estimation method is used to predict both rotation and translation amounts, then the device complexity is reduced, but the measurement precision of both rotation and translation amounts deteriorates due to the challenging factors like occlusion, illumination change, and symmetry
Solution Approach 1:
The patent divides the pose estimation task into two separate branch networks: one dedicated to rotation amount prediction and another to translation amount prediction. Each branch network is optimized for its specific function, allowing for higher precision in both measurements while maintaining manageable device complexity through modular architecture.
2Measurement precision
If separate prediction strategies are used for rotation and translation amounts, then the measurement precision of pose estimation is improved, but the device complexity increases due to the need for multiple specialized networks
Solution Approach 1:
The patent merges the rotation prediction branch and translation prediction branch into a single integrated pose decoupling prediction model that shares a common feature extraction backbone. This combining approach allows separate specialized prediction strategies for rotation and translation while avoiding the full complexity of completely independent systems, as both branches utilize shared features from the common backbone network.
Data Source
AI summary
Provided are a pose prediction method and apparatus, and a model training method and apparatus. The pose prediction method includes: performing target identification on a first image to be predicted to determine an area where a target object is located; determining a transformed target image based on the area where the target object is located; inputting the transformed target image into a pose decoupling prediction model for pose prediction; and determining a rotation amount of the target object based on an outputted result of a rotation amount branch network of the pose decoupling prediction model and determining a translation amount of the target object based on an outputted result of rotation amount branch network of the pose decoupling prediction model. By means of decoupling rotation and translation in object pose, the accuracy of pose prediction can be improved.