Abstract
Light Field (LF) depth estimation is an important research direction in the area of computer vision and computational photography, which aims to infer the depth information of different objects in three-dimensional scenes by capturing LF data. Given this new era of significance, this article introduces a survey of the key concepts, methods, novel applications, and future trends in this area. We summarize the LF depth estimation methods, which are usually based on the interaction of radiance from rays in all directions of the LF data, such as epipolar-plane, multi-view geometry, focal stack, and deep learning. We analyze the many challenges facing each of these approaches, including complex algorithms, large amounts of computation, and speed requirements. In addition, this survey summarizes most of the currently available methods, conducts some comparative experiments, discusses the results, and investigates the novel directions in LF depth estimation.
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