Relative Pose Estimation for Light Field Cameras Based on LF-Point-LF-Point Correspondence Model

Saiping Zhang, Dongyang Jin, Yuchao Dai, Fuzheng Yang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In this paper, we propose a relative pose estimation algorithm for micro-lens array (MLA)-based conventional light field (LF) cameras. First, by employing the matched LF-point pairs, we establish the LF-point-LF-point correspondence model to represent the correlation between LF features of the same 3D scene point in a pair of LFs. Then, we employ the proposed correspondence model to estimate the relative camera pose, which includes a linear solution and a non-linear optimization on manifold. Unlike prior related algorithms, which estimated relative poses based on the recovered depths of scene points, we adopt the estimated disparities to avoid the inaccuracy in recovering depths due to the ultra-small baseline between sub-aperture images of LF cameras. Experimental results on both simulated and real scene data have demonstrated the effectiveness of the proposed algorithm compared with classical as well as state-of-art relative pose estimation algorithms.

Original languageEnglish
Pages (from-to)1641-1656
Number of pages16
JournalIEEE Transactions on Image Processing
Volume31
DOIs
StatePublished - 2022

Keywords

  • LF cameras
  • LF features
  • non-linear optimization on manifold
  • Relative pose estimation

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