Stereo Feature Fusion for Mobile 3D Pose Estimation
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Solution Overview
Problem
Existing devices struggle with accurate and mobile-friendly 3D pose estimation, particularly in human-device interaction scenarios, due to limitations in processing stereo images.
Innovation Solution
A system combining a feature extraction backbone network with a convolutional-neural-network-based feature fusion module and cross-feature-attention module for improved 3D pose estimation, utilizing stereo gray images and sensor parameters to enhance accuracy and reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pose estimation methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system divides the pose estimation task into separate processing streams for left and right images, with dedicated feature extraction backbones for each view. This segmentation allows independent optimization of each stream while maintaining overall system accuracy, resolving the contradiction by organizing complexity in a structured manner that improves measurement precision without overwhelming device resources.
Solution Approach 2:
The system transitions from 2D image processing to 3D pose estimation by fusing features from both left and right images through a feature fusion module. This dimensional transformation enables accurate 3D hand joint position prediction while managing computational complexity through efficient feature representation and fusion strategies.
2Measurement precision
If high-accuracy pose estimation is achieved, then measurement precision improves, but use of energy increases
Solution Approach 1:
The system processes only the necessary features from stereo images required for accurate 3D pose estimation, rather than performing exhaustive analysis of all image data. The feature fusion module selectively combines relevant features from left and right views, achieving high measurement precision while avoiding excessive energy consumption through targeted processing.
3Measurement precision
If conventional feature processing is used, then device complexity is low, but measurement precision deteriorates
Solution Approach 1:
The system merges features from both left and right images through a feature fusion module that combines extracted features from dual-view image processing. This merging of multi-source features significantly improves hand joint position accuracy while the modular architecture manages processing complexity through organized feature integration rather than chaotic complexity.
Solution Approach 2:
The feature fusion module acts as an intermediary between the separate feature extraction backbones for left and right images. It mediates the combination of features from both views, enabling accurate 3D pose estimation while managing the complexity of integrating multiple feature sources through a dedicated intermediate processing stage.
Data Source
AI summary
A method and system are disclosed for estimating a 3-dimensional (3D) pose. The method includes receiving by a computing device a first input generated based on first features associated with first image data from a first sensor associated with the computing device and based on second image data from a second sensor associated with the computing device, and a second input generated based on second features associated with the first image data and based on the second image data, based on the first input and the second input, generating, by the computing device, 3D pose-estimation data associated with an object represented in the first image data and represented in the second image data, and transmitting the 3D pose-estimation data.