  Hand-held light field (LF) cameras have unique advantages in computer vision
such as 3D scene reconstruction and depth estimation. However, the related
applications are limited by the ultra-small baseline, e.g., leading to the
extremely low depth resolution in reconstruction. To solve this problem, we
propose to rectify LF to obtain a large baseline. Specifically, the proposed
method aligns two LFs captured by two hand-held LF cameras with a random
relative pose, and extracts the corresponding row-aligned sub-aperture images
(SAIs) to obtain an LF with a large baseline. For an accurate rectification, a
method for pose estimation is also proposed, where the relative rotation and
translation between the two LF cameras are estimated. The proposed pose
estimation minimizes the degree of freedom (DoF) in the LF-point-LF-point
correspondence model and explicitly solves this model in a linear way. The
proposed pose estimation outperforms the state-of-the-art algorithms by
providing more accurate results to support rectification. The significantly
improved depth resolution in 3D reconstruction demonstrates the effectiveness
of the proposed LF rectification.
