.png){kind=icon}
返回
Keypoints Filtrating Nonlinear Refinement in Spatial Target Pose Estimation with Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2024.3417226.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Spatial target pose estimation with deep learning has garnered increasing attention in recent years. However, the existing methods in this field suffer from poor generalization. In this study, we propose a robust and reliable pose estimation method for spatial targets. The method aims to achieve keypoints filtrating. It involves a detection network tasked with identifying the target area, while the subsequent stage employs a classification network to regress keypoints from the detected target area. To improve the accuracy of pose estimation, we leverage spatial target geometric constraints to formulate 2-D-3-D keypoints equations for an initial pose. Then, we create a nonlinear optimization equation based on the confidence of 2-D keypoints and accomplish nonlinear refinement. We conduct extensive experiments on commonly used datasets and demonstrate the effectiveness of the proposed method. Furthermore, thanks to the effectiveness of keypoints filtrating and nonlinear refinement, the proposed method is robust with challenging scenarios and domain bias.
Keyword:
Domain bias
keypoints filtrating
nonlinear refinement
pose estimation
spatial target
Domain bias
keypoints filtrating
nonlinear refinement
pose estimation
spatial target
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
机构
引用论文
Impact of climate variability on human health: A pilot study in tertiary care hospital of Eastern Uttar Pradesh, India.png)
MAUSAM
OAAI
MAUSAMIF0
OAAI分享收藏
EUV emission spectra in collisions of multiply charged Sn ions with He and Xe多电荷Sn离子与He和Xe碰撞中的EUV发射光谱
PREAI
分享收藏分享收藏分享收藏
查看更多内容