A method for the reconstruction of 3D shape and texture from integral photography (IP) images is presented. Sharing the same principles with stereoscopic-based object reconstruction, it offers increased robustness to noise and occlusions due to the unique characteristics of IP images. A coarse-to-fine approach is used, employing what we believe to be a novel grid refinement step in order to increase the quality of the reconstructed objects. The proposed method's properties include configurable depth accuracy and direct and seamless triangulation. We evaluate our method using synthetic data from a computer-simulated IP setup as well as real data from a simple yet effective digital IP setup. Experiments show reconstructed objects of high-quality indicating that IP can be a competitive modality for 3D object reconstruction.
Integral imaging is one of the most promising techniques for delivering three-dimensional content. Most processing tasks usually require prior knowledge of the size and positions of the elemental images that comprise an integral image. In this paper we propose an automated method for calibrating the acquisition setup, by applying a preprocessing stage to an acquired integral image. The skew angle is extracted and the size and positions of the elemental images are accurately determined. For these purposes a method is developed to automatically identify an elemental image lattice that best matches the acquired integral image.
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