Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved in separate tracks. In this paper, we present a unified learning-based technique that simultaneously uses both types of cues for depth inference. Specifically, we use a pair of focal stacks as input to emulate human perception. We first construct a comprehensive focal stack training dataset synthesized by depth-guided light field rendering. We then construct three individual networks: a FocusNet to extract depth from a single focal stack, a EDoFNet to obtain the extended depth of field (EDoF) image from the focal stack, and a StereoNet to conduct stereo matching. We then integrate them into a unified solution to obtain high quality depth maps. Comprehensive experiments show that our approach outperforms the state-of-the-art in both accuracy and speed and effectively emulates human vision systems.
Blur carries information about the object's distance. Depth from Focus Defocus (DfF DfD) recovers scene depth from a collection of images captured under varying focus settings. In general, DfF @cite_18 @cite_25 @cite_1 determines the depth by analyzing the most in-focus slice in the focal stack, while DfD @cite_36 @cite_15 infers depth based on the amount of the spatially varying blur at each pixel. To avoid ambiguity in textureless region, Moreno-Noguer @cite_52 used active illumination to project a sparse set of dots onto the scene. The defocus of the dots offers depth cue, which could be further used for realistic refocusing. @cite_53 combined focal stack with varying aperture to recover scene geometry. Moeller @cite_6 applied an efficient nonconvex minimization technique to solve DfD in a variational framework. Suwajanakorn @cite_17 proposed the DfF with mobile phone under uncalibrated setting. They first aligned the focal stack, then jointly optimized the camera parameters and depth map, and further refined the depth map using anisotropic regularization.
@cite_18 @cite_36 @cite_53 @cite_1 @cite_52 @cite_6 @cite_15 @cite_25 @cite_17
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While prior depth from focus and defocus techniques operated on laboratory scenes, we introduce the first depth from focus (DfF) method capable of handling images from mobile phones and other hand-held cameras. Achieving this goal requires solving a novel uncalibrated DfF problem and aligning the frames to account for scene parallax. Our approach is demonstrated on a range of challenging cases and produces high quality results. We present a system for refocusing images and videos of dynamic scenes using a novel, single-view depth estimation method. Our method for obtaining depth is based on the defocus of a sparse set of dots projected onto the scene. In contrast to other active illumination techniques, the projected pattern of dots can be removed from each captured image and its brightness easily controlled in order to avoid under- or over-exposure. The depths corresponding to the projected dots and a color segmentation of the image are used to compute an approximate depth map of the scene with clean region boundaries. The depth map is used to refocus the acquired image after the dots are removed, simulating realistic depth of field effects. Experiments on a wide variety of scenes, including close-ups and live action, demonstrate the effectiveness of our method. We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov random fields with a smoothness prior, and their estimates are obtained by minimizing a suitable energy function using simulated annealing. The main advantage of the proposed method, despite being computationally less efficient than the standard stereo or DFD method, is simultaneous recovery of depth as well as space-variant restoration of the original focused image of the scene. In this paper we propose a novel depth measurement method by fusing depth from defocus (DFD) and stereo. One of the problems of passive stereo method is the difficulty of finding correct correspondence between images when an object has a repetitive pattern or edges parallel to the epipolar line. On the other hand, the accuracy of DFD method is inherently limited by the effective diameter of the lens. Therefore, we propose the fusion of stereo method and DFD by giving different focus distances for left and right cameras of a stereo camera with coded apertures. Two types of depth cues, defocus and disparity, are naturally integrated by the magnification and phase shift of a single point spread function (PSF) per camera. In this paper we give the proof of the proportional relationship between the diameter of defocus and disparity which makes the calibration easy. We also show the outstanding performance of our method which has both advantages of two depth cues through simulation and actual experiments. In this paper, we presents a novel method (RGBF-IID) for intrinsic image decomposition of a wild scene without any restrictions on the complexity, illumination or scale of the image. We use focal stacks of the scene as input. A focal stack captures a scene at varying focal distances. Since focus depends on distance to the object, this representation has information beyond an RGB image towards an RGBD image with depth. We call our representation an RGBF image to highlight this. We use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack. These maps are used to define sparse local and global pixel neighbourhoods, adhering to the structure of the underlying 3D scene. We use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system. We present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions. We also test our system on a larger dataset of synthetically generated focal stacks from NYUv2 and MPI Sintel datasets and show competitive performance against current state-of-the-art IID methods that use RGBD images. Our method provides a strong evidence for the potential of RGBF modality in place of RGBD in computer vision. The classical approach to depth from defocus (DFD) uses lenses with circular apertures for image capturing. We show in this paper that the use of a circular aperture severely restricts the accuracy of DFD. We derive a criterion for evaluating a pair of apertures with respect to the precision of depth recovery. This criterion is optimized using a genetic algorithm and gradient descent search to arrive at a pair of high resolution apertures. These two coded apertures are found to complement each other in the scene frequencies they preserve. This property enables them to not only recover depth with greater fidelity but also obtain a high quality all-focused image from the two captured images. Extensive simulations as well as experiments on a variety of real scenes demonstrate the benefits of using the coded apertures over conventional circular apertures. This paper deals with the problem of reconstructing a depth map from a sequence of differently focused images, also known as depth from focus (DFF) or shape from focus. We propose to state the DFF problem as a variational problem, including a smooth but nonconvex data fidelity term and a convex nonsmooth regularization, which makes the method robust to noise and leads to more realistic depth maps. In addition, we propose to solve the nonconvex minimization problem with a linearized alternating directions method of multipliers, allowing to minimize the energy very efficiently. A numerical comparison to classical methods on simulated as well as on real data is presented. The concept of depth from focus involves calculating distances to points in an observed scene by modeling the effect that the camera's focal parameters have on images acquired with a small depth of field. This technique is passive and requires only a single camera. The most difficult segment of calculating depth from focus is deconvolving the defocus operator from the scene and modeling it. Most current methods for determining the defocus operator employ inverse filtering. The authors reveal some fundamental problems with inverse filtering: inaccuracies in finding the frequency domain representation, windowing effects, and border effects. A general, matrix-based method using regularization is presented, which eliminates these problems. The new method is confirmed experimentally, with the results showing an RMS error of 1.3 . Light Fields capturing all light rays at every point in space and in all directions contain very rich information about the scene. This rich description of the scene enables advanced image creation capabilities, such as re-focusing or extended depth of field from a single capture. But, it yields a very high volume of data which needs compression. This paper studies the impact of Light Fields compression on two key functionalities: refocusing and extended focus. The sub-aperture images forming the Light Field are compressed as a video sequence with HEVC. A focus stack and the scene depth map are computed from the compressed light field and are used to render an image with an extended depth of field (called the extended focus image). It has been first observed that the Light Field could be compressed with a factor up to 700 without significantly affecting the visual quality of both refocused and extended focus images. To further analyze the compression effect, a dedicated quality evaluation method based on contrast and gradient measurements is considered to differentiate the natural geometrical blur from the blur resulting from compression. As a second part of the experiments, it is shown that the texture distortion of the in-focus regions in the focus stacks is the main cause of the quality degradation in the extended focus and that the depth errors do not impact the extended focus quality unless the light field is significantly distorted with a compression ratio of around 2000:1.