  Depth estimation is a fundamental problem in light field processing.
Epipolar-plane image (EPI)-based methods often encounter challenges such as low
accuracy in slope computation due to discretization errors and limited angular
resolution. Besides, existing methods perform well in most regions but struggle
to produce sharp edges in occluded regions and resolve ambiguities in
texture-less regions. To address these issues, we propose the concept of
stitched-EPI (SEPI) to enhance slope computation. SEPI achieves this by
shifting and concatenating lines from different EPIs that correspond to the
same 3D point. Moreover, we introduce the half-SEPI algorithm, which focuses
exclusively on the non-occluded portion of lines to handle occlusion.
Additionally, we present a depth propagation strategy aimed at improving depth
estimation in texture-less regions. This strategy involves determining the
depth of such regions by progressing from the edges towards the interior,
prioritizing accurate regions over coarse regions. Through extensive
experimental evaluations and ablation studies, we validate the effectiveness of
our proposed method. The results demonstrate its superior ability to generate
more accurate and robust depth maps across all regions compared to
state-of-the-art methods. The source code will be publicly available at
https://github.com/PingZhou-LF/Light-Field-Depth-Estimation-Based-on-Stitched-EPIs.
