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.

翻译：深度估计是轻场处理中的基本问题。基于极平面图像的方法常因离散化误差和有限的角度分辨率而面临斜率计算精度低等挑战。此外，现有方法在大多数区域表现良好，但在遮挡区域难以产生锐利边缘，在无纹理区域也难以解决歧义性。为解决这些问题，我们提出了拼接极平面图像（SEPI）概念以增强斜率计算。SEPI通过移位并拼接来自不同EPI中对应同一三维点的线段来实现这一目标。此外，我们引入了半SEPI算法，该算法仅关注线段的非遮挡部分以处理遮挡情况。同时，我们提出了一种深度传播策略，旨在改善无纹理区域的深度估计。该策略通过从边缘向内部推进，优先处理精确区域而非粗略区域，从而确定此类区域的深度。通过广泛的实验评估和消融研究，我们验证了所提方法的有效性。结果证明，与最先进方法相比，该方法能够在所有区域生成更准确且更鲁棒的深度图。源代码将公开于https://github.com/PingZhou-LF/Light-Field-Depth-Estimation-Based-on-Stitched-EPIs。