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
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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. 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. 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)