Circular Light fields imaging is based on images taken on a regular circle with an equal space. Orientation information in epipolar plane images (EPIs) reveals strong depth clue for 3D reconstruction task. However, EPIs in Circular Light fields show a slightly distorted sinusoidal trajectory in 3D space. Rather than analyzing such spiral line on 2D image processing method, we present an algorithm based on 3D formula. By applying 3D Canny into densely sampled Circular Light fields, we can obtain a 3D point cloud in the image cube. Furthermore, we utilize structure tensor to analyze the disparity information in such 3D data. Finally, we build two Hough spaces to reconstruct depth information and obtain an accurate 3D object. Compared with state-of-the-art image-based 3D reconstruction methods, experiment results show our method can obtain improved reconstruction quality on synthetic data.
Light field cameras have been rapidly developed and are likely to appear in mobile devices in near future. It is essential to develop efficient and robust depth estimation algorithm for mobile applications. However, existing methods are either slow or lack of adaptability to occlusion such that they are not suitable to mobile computing platform. In this paper, we present the generalized EPI representation in light field and formulate it using two linear functions. By combining it with the light field occlusion theory, a highly efficient and anti-occlusion depth estimation algorithm is proposed. Our algorithm outperforms the previous local method, especially in occlusion areas. Experimental results on public light field datasets have demonstrated the effectiveness and efficiency of the proposed algorithm.
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