Light Field Rectification Based on Relative Pose Estimation

doi:10.1109/TIM.2023.3328070

Light Field Rectification Based on Relative Pose Estimation

2024-01-01

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DOI：10.1109/TIM.2023.3328070

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Hand-held light field (LF) cameras have unique advantages in computer vision such as 3-D scene reconstruction and depth estimation. However, the related applications are limited by the ultra-small baseline, 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 subaperture 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 row-aligned SAIs and significantly improved depth resolution in 3-D reconstruction demonstrate the effectiveness of the proposed LF rectification.

Keywords:

Cameras

Pose estimation

Three-dimensional displays

Solid modeling

Light fields

Feature extraction

Image reconstruction

Baseline

depth estimation

light field (LF) cameras

LF rectification

relative pose estimation

Light Field Rectification Based on Relative Pose Estimation

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