Researches in novel viewpoint synthesis majorly focus on interpolation from multi-view input images. In this paper, we focus on a more challenging and ill-posed problem that is to synthesize novel viewpoints from one single input image. To achieve this goal, we propose a novel deep learning-based technique. We design a full resolution network that extracts local image features with the same resolution of the input, which contributes to derive high resolution and prevent blurry artifacts in the final synthesized images. We also involve a pre-trained depth estimation network into our system, and thus 3D information is able to be utilized to infer the flow field between the input and the target image. Since the depth network is trained by depth order information between arbitrary pairs of points in the scene, global image features are also involved into our system. Finally, a synthesis layer is used to not only warp the observed pixels to the desired positions but also hallucinate the missing pixels with recorded pixels. Experiments show that our technique performs well on images of various scenes, and outperforms the state-of-the-art techniques.
is a popular problem in both computer vision and computer graphics. Usually, synthesizing novel views requires estimating disparity from multiple input images, and then the synthesis is performed by warping input images with the disparity. @cite_5 @cite_17 @cite_18 synthesize automultiscopic images from stereo images. @cite_13 presents a disparity based method to reconstruct light field from micro-image pairs. However, in our problem, the surrounding views need to be predicted by a single image.
@cite_5 @cite_18 @cite_13 @cite_17
2014008866 2737476662 2469939903
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. With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase. We present a variational framework to generate super-resolved novel views from 4D light field data sampled at low resolution, for example by a plenoptic camera. In contrast to previous work, we formulate the problem of view synthesis as a continuous inverse problem, which allows us to correctly take into account foreshortening effects caused by scene geometry transformations. High-accuracy depth maps for the input views are locally estimated using epipolar plane image analysis, which yields floating point depth precision without the need for expensive matching cost minimization. The disparity maps are further improved by increasing angular resolution with synthesized intermediate views. Minimization of the super-resolution model energy is performed with state of the art convex optimization algorithms within seconds. We develop a continuous framework for the analysis of 4D light fields, and describe novel variational methods for disparity reconstruction as well as spatial and angular super-resolution. Disparity maps are estimated locally using epipolar plane image analysis without the need for expensive matching cost minimization. The method works fast and with inherent subpixel accuracy since no discretization of the disparity space is necessary. In a variational framework, we employ the disparity maps to generate super-resolved novel views of a scene, which corresponds to increasing the sampling rate of the 4D light field in spatial as well as angular direction. In contrast to previous work, we formulate the problem of view synthesis as a continuous inverse problem, which allows us to correctly take into account foreshortening effects caused by scene geometry transformations. All optimization problems are solved with state-of-the-art convex relaxation techniques. We test our algorithms on a number of real-world examples as well as our new benchmark data set for light fields, and compare results to a multiview stereo method. The proposed method is both faster as well as more accurate. Data sets and source code are provided online for additional evaluation. In this paper, we propose a new method that can remove view-disturbing noises from stereo images. One of the thorny problems in outdoor surveillance by a camera is that adherent noises such as waterdrops on the protecting glass surface lens disturb the view from the camera. Therefore, we propose a method for removing adherent noises from stereo images taken with a stereo camera system. Our method is based on the stereo measurement and utilizes disparities between stereo image pair. Positions of noises in images can be detected by comparing disparities measured from stereo images with the distance between the stereo camera system and the glass surface. True disparities of image regions hidden by noises can be estimated from the property that disparities are generally similar with those around noises. Finally, we can remove noises from images by replacing the above regions with textures of corresponding image regions obtained by the disparity referring. Experimental results show the effectiveness of the proposed method. We present a novel algorithm for simultaneous color and depth inpainting. The algorithm takes stereo images and estimated disparity maps as input and fills in missing color and depth information introduced by occlusions or object removal. We first complete the disparities for the occlusion regions using a segmentation-based approach. The completed disparities can be used to facilitate the user in labeling objects to be removed. Since part of the removed regions in one image is visible in the other, we mutually complete the two images through 3D warping. Finally, we complete the remaining unknown regions using a depth-assisted texture synthesis technique, which simultaneously fills in both color and depth. We demonstrate the effectiveness of the proposed algorithm on several challenging data sets. We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints. We approach this as a learning task but, critically, instead of learning to synthesize pixels from scratch, we learn to copy them from the input image. Our approach exploits the observation that the visual appearance of different views of the same instance is highly correlated, and such correlation could be explicitly learned by training a convolutional neural network (CNN) to predict appearance flows -- 2-D coordinate vectors specifying which pixels in the input view could be used to reconstruct the target view. Furthermore, the proposed framework easily generalizes to multiple input views by learning how to optimally combine single-view predictions. We show that for both objects and scenes, our approach is able to synthesize novel views of higher perceptual quality than previous CNN-based techniques. We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints. We approach this as a learning task but, critically, instead of learning to synthesize pixels from scratch, we learn to copy them from the input image. Our approach exploits the observation that the visual appearance of different views of the same instance is highly correlated, and such correlation could be explicitly learned by training a convolutional neural network (CNN) to predict appearance flows – 2-D coordinate vectors specifying which pixels in the input view could be used to reconstruct the target view. Furthermore, the proposed framework easily generalizes to multiple input views by learning how to optimally combine single-view predictions. We show that for both objects and scenes, our approach is able to synthesize novel views of higher perceptual quality than previous CNN-based techniques. A lot of progress has been made to solve the depth estimation problem in stereo vision. Though, a very satisfactory performance is observed by utilizing the deep learning in supervised manner for depth estimation. This approach needs huge amount of ground truth training data as well as depth maps which is very laborious to prepare and many times it is not available in real scenario. Thus, the unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth. In unsupervised depth computation, the disparity images are generated by training the CNN with an image reconstruction loss based on the epipolar geometry constraints. The effective way of using CNN as well as investigating the better losses for the said problem needs to be addressed. In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map. The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities. The presented DNM6 and DNM12 models are experimented over KITTI driving and Cityscapes urban database and compared with the recent state-of-the-art result of unsupervised depth estimation. In this paper we propose a novel approach to binocular stereo for fast matching of high-resolution images. Our approach builds a prior on the disparities by forming a triangulation on a set of support points which can be robustly matched, reducing the matching ambiguities of the remaining points. This allows for efficient exploitation of the disparity search space, yielding accurate dense reconstruction without the need for global optimization. Moreover, our method automatically determines the disparity range and can be easily parallelized. We demonstrate the effectiveness of our approach on the large-scale Middlebury benchmark, and show that state-of-the-art performance can be achieved with significant speedups. Computing the left and right disparity maps for a one Megapixel image pair takes about one second on a single CPU core. We present a novel method that can enhance the spatial resolution of stereo images using a parallax prior. While traditional stereo imaging has focused on estimating depth from stereo images, our method utilizes stereo images to enhance spatial resolution instead of estimating disparity. The critical challenge for enhancing spatial resolution from stereo images: how to register corresponding pixels with subpixel accuracy. Since disparity in traditional stereo imaging is calculated per pixel, it is directly inappropriate for enhancing spatial resolution. We, therefore, learn a parallax prior from stereo image datasets by jointly training two-stage networks. The first network learns how to enhance the spatial resolution of stereo images in luminance, and the second network learns how to reconstruct a high-resolution color image from high-resolution luminance and chrominance of the input image. Our two-stage joint network enhances the spatial resolution of stereo images significantly more than single-image super-resolution methods. The proposed method is directly applicable to any stereo depth imaging methods, enabling us to enhance the spatial resolution of stereo images. Obtaining accurate depth measurements out of a single image represents a fascinating solution to 3D sensing. CNNs led to considerable improvements in this field, and recent trends replaced the need for ground-truth labels with geometry-guided image reconstruction signals enabling unsupervised training. Currently, for this purpose, state-of-the-art techniques rely on images acquired with a binocular stereo rig to predict inverse depth (i.e., disparity) according to the aforementioned supervision principle. However, these methods suffer from well-known problems near occlusions, left image border, etc inherited from the stereo setup. Therefore, in this paper, we tackle these issues by moving to a trinocular domain for training. Assuming the central image as the reference, we train a CNN to infer disparity representations pairing such image with frames on its left and right side. This strategy allows obtaining depth maps not affected by typical stereo artifacts. Moreover, being trinocular datasets seldom available, we introduce a novel interleaved training procedure enabling to enforce the trinocular assumption outlined from current binocular datasets. Exhaustive experimental results on the KITTI dataset confirm that our proposal outperforms state-of-the-art methods for unsupervised monocular depth estimation trained on binocular stereo pairs as well as any known methods relying on other cues. Image-based novel-view synthesis requires dense correspondences between the original views to produce a high quality synthetic view. In a wide-baseline stereo setup, dense correspondences are difficult to achieve due to the significant change in viewpoint giving rise to a number of problems. To improve their quality, the original, incomplete disparity maps are usually interpolated to fill in the missing regions. When the data is very sparse, such as in the case of the wide-baseline stereo, interpolation alone is not enough. Instead, a 3-D model of the scene can be used to fill in the missing regions more reliably, using a-priori knowledge. However, the 3-D model can be used more efficiently and accurately in disparity space, where the disparity data originates from. In this paper we present and compare the two techniques. We show that, in comparison with the 3-D approach, the disparity space approach offers a computationally less expensive and potentially more accurate solution. (10 pages) Recently, convolutional neural networks (CNN) have been successfully applied to view synthesis problems. However, such CNN-based methods can suffer from lack of texture details, shape distortions, or high computational complexity. In this paper, we propose a novel CNN architecture for view synthesis called Deep View Morphing that does not suffer from these issues. To synthesize a middle view of two input images, a rectification network first rectifies the two input images. An encoder-decoder network then generates dense correspondences between the rectified images and blending masks to predict the visibility of pixels of the rectified images in the middle view. A view morphing network finally synthesizes the middle view using the dense correspondences and blending masks. We experimentally show the proposed method significantly outperforms the state-of-the-art CNN-based view synthesis method. Stereoscopic 3D (S3D) movies have become widely popular in the movie theaters, but the adoption of S3D at home is low even though most TV sets support S3D. It is widely believed that S3D with glasses is not the right approach for the home. A much more appealing approach is to use automulti-scopic displays that provide a glasses-free 3D experience to multiple viewers. A technical challenge is the lack of native multiview content that is required to deliver a proper view of the scene for every viewpoint. Our approach takes advantage of the abundance of stereoscopic 3D movies. We propose a real-time system that can convert stereoscopic video to a high-quality multiview video that can be directly fed to automultiscopic displays. Our algorithm uses a wavelet-based decomposition of stereoscopic images with per-wavelet disparity estimation. A key to our solution lies in combining Lagrangian and Eulerian approaches for both the disparity estimation and novel view synthesis, which leverages the complementary advantages of both techniques. The solution preserves all the features of Eulerian methods, e.g., subpixel accuracy, high performance, robustness to ambiguous depth cases, and easy integration of inter-view aliasing while maintaining the advantages of Lagrangian approaches, e.g., robustness to large disparities and possibility of performing non-trivial disparity manipulations through both view extrapolation and interpolation. The method achieves real-time performance on current GPUs. Its design also enables an easy hardware implementation that is demonstrated using a field-programmable gate array. We analyze the visual quality and robustness of our technique on a number of synthetic and real-world examples. We also perform a user experiment which demonstrates benefits of the technique when compared to existing solutions. Automatically locating multiple feature points (i.e., the shape) in a facial image and then synthesizing the corresponding facial sketch are highly challenging since facial images typically exhibit a wide range of poses, expressions, and scales, and have differing degrees of illumination and or occlusion. When the facial sketches are to be synthesized in the unique sketching style of a particular artist, the problem becomes even more complex. To resolve these problems, this paper develops an automatic facial sketch synthesis system based on a novel direct combined model (DCM) algorithm. The proposed system executes three cascaded procedures, namely, (1) synthesis of the facial shape from the input texture information (i.e., the facial image); (2) synthesis of the exaggerated facial shape from the synthesized facial shape; and (3) synthesis of a sketch from the original input image and the synthesized exaggerated shape. Previous proposals for reconstructing facial shapes and synthesizing the corresponding facial sketches are heavily reliant on the quality of the texture reconstruction results, which, in turn, are highly sensitive to occlusion and lighting effects in the input image. However, the DCM approach proposed in this paper accurately reconstructs the facial shape and then produces lifelike synthesized facial sketches without the need to recover occluded feature points or to restore the texture information lost as a result of unfavorable lighting conditions. Moreover, the DCM approach is capable of synthesizing facial sketches from input images with a wide variety of facial poses, gaze directions, and facial expressions even when such images are not included within the original training data set. Warping is one of the basic image processing techniques. Directly applying existing monocular image warping techniques to stereoscopic images is problematic as it often introduces vertical disparities and damages the original disparity distribution. In this paper, we show that these problems can be solved by appropriately warping both the disparity map and the two images of a stereoscopic image. We accordingly develop a technique for extending existing image warping algorithms to stereoscopic images. This technique divides stereoscopic image warping into three steps. Our method first applies the user-specified warping to one of the two images. Our method then computes the target disparity map according to the user specified warping. The target disparity map is optimized to preserve the perceived 3D shape of image content after image warping. Our method finally warps the other image using a spatially-varying warping method guided by the target disparity map. Our experiments show that our technique enables existing warping methods to be effectively applied to stereoscopic images, ranging from parametric global warping to non-parametric spatially-varying warping. Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations. In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs. Training is performed in an end-to-end fashion without the need of ground-truth disparity maps. The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map that minimizes the warping error. While this is a simple concept well-known in stereo matching, to make it work in a deep-learning framework, many non-trivial challenges must be overcome, and in this work we provide effective solutions. Our network is self-adaptive to different unseen imageries as well as to different camera settings. Experiments on KITTI and Middlebury stereo benchmark datasets show that our method outperforms many state-of-the-art stereo matching methods with a margin, and at the same time significantly faster. Comparing two images from different views has been a long-standing challenging problem in computer vision, as visual features are not stable under large view point changes. In this paper, given a single input image of an object, we synthesize its features for other views, leveraging an existing modestly-sized 3D model collection of related but not identical objects. To accomplish this, we study the relationship of image patches between different views of the same object, seeking what we call surrogate patches -- patches in one view whose feature content predicts well the features of a patch in another view. Based upon these surrogate relationships, we can create feature sets for all views of the latent object on a per patch basis, providing us an augmented multi-view representation of the object. We provide theoretical and empirical analysis of the feature synthesis process, and evaluate the augmented features in fine-grained image retrieval recognition and instance retrieval tasks. Experimental results show that our synthesized features do enable view-independent comparison between images and perform significantly better than other traditional approaches in this respect. 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