TY - JOUR
T1 - Relative Pose Estimation for Light Field Cameras Based on LF-Point-LF-Point Correspondence Model
AU - Zhang, Saiping
AU - Jin, Dongyang
AU - Dai, Yuchao
AU - Yang, Fuzheng
N1 - Publisher Copyright:
© 1992-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - LF cameras
KW - LF features
KW - non-linear optimization on manifold
KW - Relative pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85123969071&partnerID=8YFLogxK
U2 - 10.1109/TIP.2022.3144891
DO - 10.1109/TIP.2022.3144891
M3 - 文章
C2 - 35081028
AN - SCOPUS:85123969071
SN - 1057-7149
VL - 31
SP - 1641
EP - 1656
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
ER -