  The abundant spatial and angular information from light fields has allowed
the development of multiple disparity estimation approaches. However, the
acquisition of light fields requires high storage and processing cost, limiting
the use of this technology in practical applications. To overcome these
drawbacks, the compressive sensing (CS) theory has allowed the development of
optical architectures to acquire a single coded light field measurement. This
measurement is decoded using an optimization algorithm or deep neural network
that requires high computational costs. The traditional approach for disparity
estimation from compressed light fields requires first recovering the entire
light field and then a post-processing step, thus requiring long times. In
contrast, this work proposes a fast disparity estimation from a single
compressed measurement by omitting the recovery step required in traditional
approaches. Specifically, we propose to jointly optimize an optical
architecture for acquiring a single coded light field snapshot and a
convolutional neural network (CNN) for estimating the disparity maps.
Experimentally, the proposed method estimates disparity maps comparable with
those obtained from light fields reconstructed using deep learning approaches.
Furthermore, the proposed method is 20 times faster in training and inference
than the best method that estimates the disparity from reconstructed light
fields.
