Light-Field Dataset for Disparity Based Depth Estimation
Suresh Nehra, Aupendu Kar, Jayanta Mukhopadhyay, Prabir Kumar Biswas

TL;DR
This paper introduces a comprehensive publicly available light field dataset, including real and synthetic images, to facilitate disparity-based depth estimation research and address limitations of existing datasets.
Contribution
The paper provides a new, diverse light field dataset with real and synthetic images, and analyzes the impact of focal position on disparity estimation.
Findings
The dataset includes 285 real LF images and 13 synthetic images.
Focal position significantly affects disparity accuracy.
Synthetic data mimics real LF disparity characteristics.
Abstract
A Light Field (LF) camera consists of an additional two-dimensional array of micro-lenses placed between the main lens and sensor, compared to a conventional camera. The sensor pixels under each micro-lens receive light from a sub-aperture of the main lens. This enables the image sensor to capture both spatial information and the angular resolution of a scene point. This additional angular information is used to estimate the depth of a 3-D scene. The continuum of virtual viewpoints in light field data enables efficient depth estimation using Epipolar Line Images (EPIs) with robust occlusion handling. However, the trade-off between angular information and spatial information is very critical and depends on the focal position of the camera. To design, develop, implement, and test novel disparity-based light field depth estimation algorithms, the availability of suitable light field image…
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Taxonomy
TopicsAdvanced Vision and Imaging · Optical measurement and interference techniques · Image Processing Techniques and Applications
