.png){kind=icon}
.png){kind=icon}
Return
Vision-Based Pose Estimation From Points With Unknown Correspondences
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2014.2329765.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Pose estimation from points with unknown correspondences currently is still a difficult problem in the field of computer vision. To solve this problem, the SoftSI algorithm is proposed, which can simultaneously obtain pose and correspondences. The SoftSI algorithm is based on the combination of the proposed PnP algorithm (the SI algorithm) and two singular value decomposition (SVD)-based shape description theorems. Other main contributions of this paper are: 1) two SVD-based shape description theorems are proposed; 2) by analyzing the calculation process of the SI algorithm, the method to avoid pose ambiguity is proposed; and 3) an acceleration method to quickly eliminate bad initial values for the SoftSI algorithm is proposed. The simulation results show that the SI algorithm is accurate while the SoftSI algorithm is fast, robust to noise, and has large convergence radius.
Keywords:
Pose estimation
correspondence determination
numerical optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W