Open AccessDEPTH MAP ESTIMATION IN LIGHT FIELDS USING AN STEREO-LIKE TAXONOMY
Author(s) -
Francisco Calderón,
Carlos Parra,
Cesar L. Niño
Publication year - 2016
Publication title -
revista de investigaciones universidad del quindio
Language(s) - English
Resource type - Journals
eISSN - 2500-5782
pISSN - 1794-631X
DOI - 10.33975/riuq.vol28n1.37
Subject(s) - computer vision , artificial intelligence , ground truth , computer science , light field , augmented reality , depth map , matching (statistics) , computer graphics (images) , mathematics , image (mathematics) , statistics
The light field or LF is a function that describes the amount of light traveling in every direction (angular) through every point (spatial) in a scene, this LF can be captured in several ways, using arrays of cameras, or more recently using a single camera with an special lens, that allows the capture of angular and spatial information of light rays of a scene (LF). This recent camera implementation gives a different approach to find the dept of a scene using only a single camera. In order to estimate the depth, we describe a taxonomy, similar to the one used in stereo Depth-map algorithms. That consist in the creation of a cost tensor to represent the matching cost between different disparities, then, using a support weight window, aggregate the cost tensor, finally, using a winner-takes-all optimization algorithm, search for the best disparities. This paper explains in detail the several changes made to an stereo-like taxonomy, to be applied in a light field, and evaluate this algorithm using a recent database that for the first time, provides several ground-truth light fields, with a respective ground-truth depth map.