Total Variation-Based Dense Depth from Multi-Camera Array

Multi-Camera arrays are increasingly employed in both consumer and industrial
applications, and various passive techniques are documented to estimate depth
from such camera arrays. Current depth estimation methods provide useful
estimations of depth in an imaged scene but are often impractical due to
significant computational requirements. This paper presents a novel framework
that generates a high-quality continuous depth map from multi-camera
array/light field cameras. The proposed framework utilizes analysis of the
local Epipolar Plane Image (EPI) to initiate the depth estimation process. The
estimated depth map is then processed using Total Variation (TV) minimization
based on the Fenchel-Rockafellar duality. Evaluation of this method based on a
well-known benchmark indicates that the proposed framework performs well in
terms of accuracy when compared to the top-ranked depth estimation methods and
a baseline algorithm. The test dataset includes both photorealistic and
non-photorealistic scenes. Notably, the computational requirements required to
achieve an equivalent accuracy are significantly reduced when compared to the
top algorithms. As a consequence, the proposed framework is suitable for
deployment in consumer and industrial applications.