摘要： In recent years, optical field imaging technology has received extensive attention in the academic circle for its novel imaging characteristics of shooting first and focusing later, variable depth of field, variable viewpoint, and so on. However, the existing optical field acquisition equipment can only acquire a limited number of discrete angle signals, so image aliasing caused by under sampling of optical field angle signals reduces the quality of optical field images. Based on the camera array system as a platform, this paper studies the optical field imaging and depth estimation method based on the Big Data in Internet of Things obtained from camera array around the angle sampling characteristics of the optical field data set, and has achieved some innovative research results in the following aspects. On the basis of analyzing the characteristics of different depth clues in the optical field data set, a depth estimation method combining parallax method and focusing method is proposed. First, this paper analyzes the disparity clues and focus clues contained in the multi-view data set and the light field refocusing image set of the camera array, respectively, and points out the differences and relationships between the two depth clues extraction methods in the light field sampling frequency domain space, that is, the disparity method focuses on the energy concentration characteristics near the frequency domain spatial angle axis, while the focus method focuses on the high frequency proportion of energy distribution on the angle axis. Then, the weighted linear fusion method based on image gradient is used to fuse the two calculation results, which improves the accuracy and robustness of depth estimation. Finally, the results of depth estimation experiments on different sets of scenes show that compared with the method based on a single depth cue, the method in this paper has higher accuracy in depth calculation in discontinuous areas of scene depth and similar texture areas.