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Light Field Rectification Based on Relative Pose Estimation
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DOI：10.1109/TIM.2023.3328070.png)
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Abstract
En 中文
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
Journal
IF:
5.9
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1.9W
Citations:
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