Light field Rectification based on relative pose estimation

TL;DR

This paper introduces a method to rectify hand-held light field cameras to achieve a larger baseline, improving depth resolution in 3D reconstruction by accurate pose estimation and LF alignment.

Contribution

It proposes a novel LF rectification technique based on relative pose estimation that outperforms existing algorithms in accuracy.

Findings

Enhanced depth resolution in 3D reconstruction

More accurate relative pose estimation

Effective LF rectification with large baseline

Abstract

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.