Dense light field depth estimation remains challenging due to sparse angular sampling, occlusion boundaries, textureless regions, and the cost of exhaustive multi-view matching. We propose \emph{Deep Spectral Epipolar Representation} (DSER), a geometry-aware framework that introduces spectral regularization in the epipolar domain for dense disparity reconstruction. DSER models frequency-consistent EPI structure to constrain correspondence estimation and couples this prior with a hybrid inference pipeline that combines least squares gradient initialization, plane-sweeping cost aggregation, and multiscale EPI refinement. An occlusion-aware directed random walk further propagates reliable disparity along edge-consistent paths, improving boundary sharpness and weak-texture stability. Experiments on benchmark and real-world light field datasets show that DSER achieves a strong accuracy-efficiency trade-off, producing more structurally consistent depth maps than representative classical and hybrid baselines. These results establish spectral epipolar regularization as an effective inductive bias for scalable and noise-robust light field depth estimation.

翻译：稠密光场深度估计因角度采样稀疏、遮挡边界、无纹理区域以及穷举多视图匹配的高计算成本而具有挑战性。本文提出深度频谱对极表示（DSER），一种在对极域中引入频谱正则化以进行稠密视差重建的几何感知框架。DSER建模频率一致的EPI结构以约束对应估计，并将该先验与混合推理流水线耦合，结合最小二乘梯度初始化、平面扫描代价聚合和多尺度EPI细化。遮挡感知的有向随机游走进一步沿边缘一致性路径传播可靠视差，提升边界锐度与弱纹理稳定性。在基准和真实光场数据集上的实验表明，DSER实现了精度与效率的强权衡，生成的深度图在结构一致性上优于代表性经典与混合基线方法。这些结果确立了频谱对极正则化作为可扩展且鲁棒的光场深度估计的有效归纳偏置。