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RSB-Pose: Robust Short-Baseline Binocular 3D Human Pose Estimation with Occlusion Handling
Summary
This study introduces a novel method for 3D Human Pose Estimation using a short-baseline binocular system. The approach enhances accuracy and handles occlusions effectively, improving 3D reconstruction robustness.
Area of Science:
- Computer Vision
- 3D Human Pose Estimation
Background:
- Growing demand for convenient 3D Human Pose Estimation equipment.
- Short-baseline binocular setups offer portability but face challenges in robustness and occlusion handling.
Purpose of the Study:
- To develop a robust 3D Human Pose Estimation method for short-baseline binocular systems.
- To address challenges of 2D error robustness and frequent occlusions.
Main Methods:
- Introduced Stereo Co-Keypoints Estimation module using disparity and Stereo Volume Feature (SVF) for improved 2D keypoint consistency and 3D robustness.
- Employed a Pre-trained Pose Transformer module to refine 3D poses by perceiving pose coherence and handling occlusions.
Main Results:
- Validated effectiveness on H36M and MHAD datasets.
- Demonstrated significant improvements in 3D reconstruction robustness against 2D errors.
- Successfully handled occlusions in short-baseline binocular 3D Human Pose Estimation.
Conclusions:
- The proposed method effectively enhances 3D Human Pose Estimation in short-baseline binocular systems.
- The approach provides a robust solution for occlusion handling and improves overall accuracy.
