## 1 Introduction  
With the explosive interest in implementing and improving augmented reality (AR) and virtual reality (VR) systems, there is an increasing demand to acquire information on real three-dimensional (3D) objects to produce contents for realistic displays. Efforts to develop core applications of VR and AR displays, such as head mounted display (HMD) and head-up display (HUD), are widely researched [1; 2; 3; 4; 5]. In order to display natural real 3D scene, obtaining 3D information of the object is necessary. There are several different ways to acquire 3D information, such as structured light, time-of-flight, and light field [6; 7; 8; 9; 10]. Among them, the light field camera (LFC) is attracting much attention as a next generation camera that can record depth and color information of object, while it is similar to a conventional camera in appearance and it has an advantage in portability [10]. An LFC records four-dimensional (4D) light field (two-dimensional (2D) spatial information and 2D angular information) of given scene in the form of a 2D image. Since it was proposed as a device for taking plenoptic function, there have been a large number of studies on ways to handle and reconstruct the captured light field [11; 12; 13; 14; 15]. They also explored how to get more precise information from a LFC and what optical information a LFC gets exactly--in other words, where a ray starts and where it heads to [12; 16; 17]. According to the specifications of optical element in LFC, such as sensor size, pixel pitch, lens pitch, \(f\)-number, and position of focal plane, each pixel of LFC is magnified to a certain position on space and it records a bundle of rays having a specific range of angle of incidence. This means that light field information is sampled differently to the captured image, depending on many input parameters of the light field camera. Therefore, to transform the captured image to the exact light field, it is necessary to define which light field the image sensor samples according to the details of the LFC. One research on the LFC has been conducted on how much information the captured 2D image has in the 4D light field domain [17]. By using this study, an image that is taken by an LFC can be easily processed to light field information. On the other hand, to reconstruct a 3D object from the recorded light field, depth information should be extracted from the light field data. Methods for extracting the depth by using integral imaging, which is an optical system that is similar to LFC, have been proposed [18; 19]. These methods use stereo matching with pixel-unit accuracy. Therefore, it is impossible to extract continuous depth information and cannot reconstruct accurate 3D volume. There is also a study to extract depth map with sub-pixel accuracy in images that are taken with a LFC [20; 21]. However, although this method has a high accuracy, there is a limitation that the extracted depth is a relative value between pixels, not absolute value, so that additional calibration is required to reconstruct as 3D model. In this paper, depth extraction method based on optical flow for light field camera (DOLF) is proposed. Optical flow calculation is used to analyze light field information for several applications, such as visual odometry and the reconstruction of transparent flow surface [22; 23]. In these studies, the optical flow is calculated between images made by each lens of lens array. There is also a study using optical flow for depth information in integral imaging (InIm), but it has not yet been used to extract depth information by comparing sub-images taken by an LFC [24]. Paying attention to the difference between InIm and LFC systems, we geometrically analyze the two optical systems to determine the relationship about the optical flow value of the sub-images and the actual depth of the object. The depth information of center sub-image is calculated as absolute value of length under given conditions of the LFC.