INTERMEDIATE VIEW RECONSTRUCTION FOR MULTISCOPIC 3D DISPLAY

This thesis focuses on Intermediate View Reconstruction (IVR) which generates additional images from the available stereo images. The main application of IVR is to generate the content of multiscopic 3D displays, and it can be applied to generate different viewpoints to Free-viewpoint TV (FTV). Although IVR is considered a good approach to generate additional images, there are some problems with the reconstruction process, such as detecting and handling the occlusion areas, preserving the discontinuity at edges, and reducing image artifices through formation of the texture of the intermediate image. The occlusion area is defined as the visibility of such an area in one image and its disappearance in the other one. Solving IVR problems is considered a significant challenge for researchers. In this thesis, several novel algorithms have been specifically designed to solve IVR challenges by employing them in a highly robust intermediate view reconstruction algorithm. Computer simulation and experimental results confirm the importance of occluded areas in IVR. Therefore, we propose a novel occlusion detection algorithm and another novel algorithm to Inpaint those areas. Then, these proposed algorithms are employed in a novel occlusion-aware intermediate view reconstruction that finds an intermediate image with a given disparity between two input images. This novelty is addressed by adding occlusion awareness to the reconstruction algorithm and proposing three quality improvement techniques to reduce image artifices: filling the re-sampling holes, removing ghost contours, and handling the disocclusion area. We compared the proposed algorithms to the previously well-known algorithms on each field qualitatively and quantitatively. The obtained results show that our algorithms are superior to the previous well-known algorithms. The performance of the proposed reconstruction algorithm is tested under 13 real images and 13 synthetic images. Moreover, analysis of a human-trial experiment conducted with 21 participants confirmed that the reconstructed images from our proposed algorithm have very high quality compared with the reconstructed images from the other existing algorithms

Livrable D5.2 of the PERSEE project : 2D/3D Codec architecture

Livrable D5.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D5.2 du projet. Son titre : 2D/3D Codec architectur

Towards Better Methods of Stereoscopic 3D Media Adjustment and Stylization

Stereoscopic 3D (S3D) media is pervasive in film, photography and art. However, working with S3D media poses a number of interesting challenges arising from capture and editing. In this thesis we address several of these challenges. In particular, we address disparity adjustment and present a layer-based method that can reduce disparity without distorting the scene. Our method was successfully used to repair several images for the 2014 documentary “Soldiers’ Stories” directed by Jonathan Kitzen. We then explore consistent and comfortable methods for stylizing stereo images. Our approach uses a modified version of the layer-based technique used for disparity adjustment and can be used with a variety of stylization filters, including those in Adobe Photoshop. We also present a disparity-aware painterly rendering algorithm. A user study concluded that our layer-based stylization method produced S3D images that were more comfortable than previous methods. Finally, we address S3D line drawing from S3D photographs. Line drawing is a common art style that our layer-based method is not able to reproduce. To improve the depth perception of our line drawings we optionally add stylized shading. An expert survey concluded that our results were comfortable and reproduced a sense of depth

Development of Correspondence Field and Its Application to Effective Depth Estimation in Stereo Camera Systems

Stereo camera systems are still the most widely used apparatus for estimating 3D or depth information of a scene due to their low-cost. Estimation of depth using a stereo camera requires first estimating the disparity map using stereo matching algorithms and calculating depth via triangulation based on the camera arrangement (their locations and orientations with respect to the scene). In almost all cases, the arrangement is determined based on human experience since there lacks an effective theoretical tool to guide the design of the camera arrangement. This thesis presents the development of a novel tool, called correspondence field (CF), and its application to optimize the stereo camera arrangement for depth estimation

Intermediate view generation for perceived depth adjustment of stereo video

There is significant industry activity on delivery of 3D video to the home. It is expected that 3D capable devices will be able to provide consumers with the ability to adjust the depth perceived for stereo content. This paper provides an overview of related techniques and evaluates the effectiveness of several approaches. Practical considerations are also discussed

BidNet : Binocular Image Dehazing without explicit disparity estimation

Heavy haze results in severe image degradation and thus hampers the performance of visual perception, object detection, etc. On the assumption that dehazed binocular images are superior to the hazy ones for stereo vision tasks such as 3D object detection and according to the fact that image haze is a function of depth, this paper proposes a Binocular image dehazing Network (BidNet) aiming at dehazing both the left and right images of binocular images within the deep learning framework. Existing binocular dehazing methods rely on simultaneously dehazing and estimating disparity, whereas BidNet does not need to explicitly perform time-consuming and well-known challenging disparity estimation. Note that a small error in disparity gives rise to a large variation in depth and in estimation of haze-free image. The relationship and correlation between binocular images are explored and encoded by the proposed Stereo Transformation Module (STM). Jointly dehazing binocular image pairs is mutually beneficial, which is better than only dehazing left images. We extend the Foggy Cityscapes dataset to a Stereo Foggy Cityscapes dataset with binocular foggy image pairs. Experimental results demonstrate that BidNet significantly outperforms state-of-the-art dehazing methods in both subjective and objective assessments