Effective 3D object reconstruction from densely sampled circular light fields

Abstract

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.