Fast Disparity Estimation from a Single Compressed Light Field Measurement

TL;DR

This paper introduces a rapid disparity estimation method directly from a single compressed light field measurement, bypassing the need for full light field reconstruction, thus significantly reducing computational time.

Contribution

It jointly optimizes an optical architecture and a CNN for direct disparity estimation from compressed measurements, offering a faster alternative to traditional reconstruction-based methods.

Findings

Estimates disparity maps comparable to deep learning reconstructed light fields.

Achieves 20 times faster training and inference than existing methods.

Eliminates the need for full light field recovery before disparity estimation.

Abstract

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.