光场深度估计方法的对比研究

Comparative Study of Light Field Depth Estimation

为了更有效地利用光场信息实现场景深度的精确估计,文中回顾并深入探讨光场的深度估计问题.通过阐述光场基本理论,将光场深度估计归纳为基于极平面图像、多视角图像及重聚焦的3种方法.在合成数据集上,对比光照变化对不同算法性能的影响,并构建一个更全面且具有挑战性的光场数据集.在该数据集、光场标准数据集及Lytro Dataset上,定性及定量分析不同复杂场景对算法性能的影响,进一步指出该领域的研究方向.

To achieve accurate depth estimation by using light field data effectively, the light field depth estimation is reviewed in this paper. Firstly, the basic theory of light field is expounded, and the light field depth estimation methods are classified into three categories, methods based on epipolar plane image, multiview and refocusing. Next, the effects of illuminant variations on the performance of depth estimation are compared on synthetic datasets. Besides, a more comprehensive and challenging light field dataset is constructed, and the effect of complex scenes on the performance of depth estimation is qualitatively and quantitatively analyzed on the light field benchmark dataset and LytroDataset. Furthermore, the development of this field is pointed out.