Logistic Regression of Point Matches for Accurate Transformation Estimation

Yonghuai Liu, Yitian Zhao, Yanquan Zhou, Yongjun Wang, Wei Huang, Jiwan Han, Wanneng Yang, Yiguang Liu

Research output: Contribution to conferencePaperpeer-review


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
Original languageEnglish
Number of pages9
Publication statusPublished - 15 Oct 2018
Event2018 International Conference on 3D Vision: (3DV) - Università di Verona, Verona, Italy
Duration: 05 Sept 201808 Sept 2018


Conference2018 International Conference on 3D Vision
Period05 Sept 201808 Sept 2018


  • Accurate underlying transformation
  • Feature extraction and matching
  • Logistic regression
  • Partial overlapping shapes
  • Weight


Dive into the research topics of 'Logistic Regression of Point Matches for Accurate Transformation Estimation'. Together they form a unique fingerprint.

Cite this