Paper Info
Title
Logistic Regression of Point Matches for Accurate Transformation Estimation
Abstract
Feature extraction and matching (FEM) has been widely used for the registration of partially overlapping 3D shapes. Due to various factors such as imaging noise, simple geometry, or clutter, it usually introduces false positive ones. To reliably estimate the underlying transformation that brings one partial shape into the best possible alignment with another, it is critical to estimate the extent to which the established point matches are correct. To this end, we propose to use the logit function for the regression of the errors of these point matches. The novel method includes three steps: (i) normalization of the errors of the point matches, (ii) logistic regression of the point matches for the estimation of their reliabilities/weights, and (iii) estimation of the underlying transformation in the weighted least squares sense. These steps are repeated until either the maximum number of iterations has been reached or the weighted average of the errors of the point matches has been below the scanning resolution. A comparative study using real data captured by different range sensors shows that the proposed method outperforms two state-of-the-art ones for more accurate estimation of the underlying transformation.
Year
DOI
Venue
2018
10.1109/3DV.2018.00054
2018 International Conference on 3D Vision (3DV)
Keywords
Field
DocType
Partial overlapping shapes,Feature extraction and matching,Weight,Logistic regression,Accurate underlying transformation
Least squares,Normalization (statistics),Regression,Clutter,Algorithm,Finite element method,Feature extraction,Logistic function,Logistic regression,Mathematics
Conference
ISSN
ISBN
Citations 
2378-3826
978-1-5386-8426-9
0
PageRank 
References 
Authors
0.34
15
8
Name
Order
Citations
PageRank
Yonghuai Liu167561.65
Yitian Zhao224633.15
Yanquan Zhou374.87
Yongjun Wang4279.19
Wei Huang5164.27
Jiwan Han682.23
Wanneng Yang701.35
Yiguang Liu833837.15