The plenoptic camera can capture both angular and spatial information of the rays, enabling 3D reconstruction by single exposure. The geometry of the recovered scene structure is affected by the calibration of the plenoptic camera significantly. In this paper, we propose a novel unconstrained two-parallel-plane (TPP) model with 7 parameters to describe a 4D light field. By reconstructing scene points from ray-ray association, a 3D projective transformation is deduced to establish the relationship between the scene structure and the TPP parameters. Based on the transformation, we simplify the focused plenoptic camera as a TPP model and calibrate its intrinsic parameters. Our calibration method includes a close-form solution and a nonlinear optimization by minimizing re-projection error. Experiments on both simulated data and real scene data verify the performance of the calibration on the focused plenoptic camera.
To acquire light field, there are various imaging systems developed from the traditional camera. @cite_25 presented a camera array to obtain light field with high spatial and angular resolution. Prior work dealt with the calibration of the camera arrays @cite_4 . Unfortunately, applications on camera arrays are limited by its high cost and complex control. In contrast, a MLA enables a single camera to record 4D light field more conveniently and efficiently, though the baseline and spatial resolution is smaller than the camera array. Recent work devoted to calibrate the intrinsic parameters of the plenoptic cameras in two designs @cite_0 @cite_3 , which are quite different according to the image structure of the micro lenses. Moreover, in traditional multi-view geometry, multiple cameras in different poses are defined as a set of unconstrained rays, which is known as as Generalized Camera Model (GCM) @cite_16 . The ambiguity of the reconstructed scene was discussed in traditional topics. For a plenoptic camera, the different views of the same scene point are obtained, and the calibration of a plenoptic camera can use the theory on traditional multi-view for reference.
@cite_4 @cite_3 @cite_0 @cite_16 @cite_25
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A light field consists of images of a scene taken from different viewpoints. Light fields are used in computer graphics for image-based rendering and synthetic aperture photography, and in vision for recovering shape. In this paper, we describe a simple procedure to calibrate camera arrays used to capture light fields using a plane + parallax framework. Specifically, for the case when the cameras lie on a plane, we show (i) how to estimate camera positions up to an affine ambiguity, and (ii) how to reproject light field images onto a family of planes using only knowledge of planar parallax for one point in the scene. While planar parallax does not completely describe the geometry of the light field, it is adequate for the first two applications which, it turns out, do not depend on having a metric calibration of the light field. Experiments on acquired light fields indicate that our method yields better results than full metric calibration. Plenoptic cameras, constructed with internal microlens arrays, focus those microlenses at infinity in order to sample the 4D radiance directly at the microlenses. The consequent assumption is that each microlens image is completely defocused with respect to to the image created by the main camera lens and the outside object. As a result, only a single pixel in the final image can be rendered from it, resulting in disappointingly low resolution. In this paper, we present a new approach to lightfield capture and image rendering that interprets the microlens array as an imaging system focused on the focal plane of the main camera lens. This approach captures a lightfield with significantly higher spatial resolution than the traditional approach, allowing us to render high resolution images that meet the expectations of modern photographers. Although the new approach samples the lightfield with reduced angular density, analysis and experimental results demonstrate that there is sufficient parallax to completely support lightfield manipulation algorithms such as refocusing and novel views We illustrate how to consider a network of cameras as a single generalized camera in a framework proposed by Nayar (2001). We derive the discrete structure from motion equations for generalized cameras, and illustrate the corollaries to epipolar geometry. This formal mechanism allows one to use a network of cameras as if they were a single imaging device, even when they do not share a common center of projection. Furthermore, an analysis of structure from motion algorithms for this imaging model gives constraints on the optimal design of panoramic imaging systems constructed from multiple cameras. The advent of inexpensive digital image sensors and the ability to create photographs that combine information from a number of sensed images are changing the way we think about photography. In this paper, we describe a unique array of 100 custom video cameras that we have built, and we summarize our experiences using this array in a range of imaging applications. Our goal was to explore the capabilities of a system that would be inexpensive to produce in the future. With this in mind, we used simple cameras, lenses, and mountings, and we assumed that processing large numbers of images would eventually be easy and cheap. The applications we have explored include approximating a conventional single center of projection video camera with high performance along one or more axes, such as resolution, dynamic range, frame rate, and or large aperture, and using multiple cameras to approximate a video camera with a large synthetic aperture. This permits us to capture a video light field, to which we can apply spatiotemporal view interpolation algorithms in order to digitally simulate time dilation and camera motion. It also permits us to create video sequences using custom non-uniform synthetic apertures.