A light field records numerous light rays from a real-world scene. However, capturing a dense light field by existing devices is a time-consuming process. Besides, reconstructing a large amount of light rays equivalent to multiple light fields using sparse sampling arises a severe challenge for existing methods. In this paper, we present a learning-based method to reconstruct multiple novel light fields between two mutually independent light fields. We indicate that light rays distributed in different light fields have the same consistent constraints under a certain condition. The most significant constraint is a depth related correlation between angular and spatial dimensions. Our method avoids working out the error-sensitive constraint by employing a deep neural network. We predict residual values of pixels on epipolar plane image (EPI) to reconstruct novel light fields. Our method is able to reconstruct 2 to 4 novel light fields between two mutually independent input light fields. We also compare our results with those yielded by a number of alternatives elsewhere in the literature, which shows our reconstructed light fields have better structure similarity and occlusion.
Wanner and Goldluecke @cite_1 used the estimated depth map to warp input view image to novel view. However, the quality of synthesized view is easily affected by the accuracy of depth map. @cite_19 used a new prior to render a 4D light field from a 3D focal stack. @cite_16 took advantage of the sparsity of light field in continuous Fourier domain to reconstruct a full light field. The method sampled multiple 1D viewpoint trajectories with special patterns to reconstruct a full 4D light field. @cite_20 introduced a phase-based method to reconstruct a full light field from micro-baseline image pair. @cite_22 reconstructed a full light field by searching for best-matching multidimensional patches within the dataset. However, in these methods, due to the limitation of specific sampling pattern and algorithm complexity, they are unable to properly generate a dense light field. The angular resolution of synthesized light field is at most 20*20 with commercial light field cameras. @cite_12 proposed a method to reconstruct light field from a coded 2D projection. But they need a special designed equipment to capture compressive light field.
@cite_22 @cite_1 @cite_19 @cite_16 @cite_12 @cite_20
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We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point. Please see our supplementary video at this https URL We present a novel paradigm to deal with depth reconstruction from 4D light fields in a variational framework. Taking into account the special structure of light field data, we reformulate the problem of stereo matching to a constrained labeling problem on epipolar plane images, which can be thought of as vertical and horizontal 2D cuts through the field. This alternative formulation allows to estimate accurate depth values even for specular surfaces, while simultaneously taking into account global visibility constraints in order to obtain consistent depth maps for all views. The resulting optimization problems are solved with state-of-the-art convex relaxation techniques. We test our algorithm on a number of synthetic and real-world examples captured with a light field gantry and a plenoptic camera, and compare to ground truth where available. All data sets as well as source code are provided online for additional evaluation. We present a novel phase-based approach for reconstructing 4D light field from a micro-baseline stereo pair. Our approach takes advantage of the unique property of complex steerable pyramid filters in micro-baseline stereo. We first introduce a Disparity Assisted Phase based Synthesis (DAPS) strategy that can integrate disparity information into the phase term of a reference image to warp it to its close neighbor views. Based on the DAPS, an “analysis by synthesis” approach is proposed to warp from one of the input binocular images to the other, and iteratively optimize the disparity map to minimize the phase differences between the warped one and the ground truth input. Finally, the densely and regularly spaced, high quality light field images can be reconstructed using the proposed DAPS according to the refined disparity map. Our approach also solves the problems of disparity inconsistency and ringing artifact in available phase-based view synthesis methods. Experimental results demonstrate that our approach substantially improves both the quality of disparity map and light field, compared with the state-of-the-art stereo matching and image based rendering approaches. Acquiring and representing the 4D space of rays in the world (the light field) is important for many computer vision and graphics applications. Yet, light field acquisition is costly due to their high dimensionality. Existing approaches either capture the 4D space explicitly, or involve an error-sensitive depth estimation process. This paper argues that the fundamental difference between different acquisition and rendering techniques is a difference between prior assumptions on the light field. We use the previously reported dimensionality gap in the 4D light field spectrum to propose a new light field prior. The new prior is a Gaussian assigning a non-zero variance mostly to a 3D subset of entries. Since there is only a low-dimensional subset of entries with non-zero variance, we can reduce the complexity of the acquisition process and render the 4D light field from 3D measurement sets. Moreover, the Gaussian nature of the prior leads to linear and depth invariant reconstruction algorithms. We use the new prior to render the 4D light field from a 3D focal stack sequence and to interpolate sparse directional samples and aliased spatial measurements. In all cases the algorithm reduces to a simple spatially invariant deconvolution which does not involve depth estimation.