Josselin Gautier

Depth-based image completion for view synthesis

This paper describes a depth-based inpainting algorithm which efficiently handles disocclusion occurring on virtual viewpoint rendering. A single reference view and a set of depth maps are used in the proposed approach. The method not only deals with small disocclusion filling related to small camera baseline, but also manages to fill in larger disocclusions in distant synthesized views. This relies on a coherent tensor-based color and geometry structure propagation. The depth is used to drive the filling order, while enforcing the structure diffusion from similar candidate-patches. By acting on patch prioritization, selection and combination, the completion of distant synthesized views allows a consistent and realistic rendering of virtual viewpoints.

Examplar-based inpainting based on local geometry

In this paper, we propose a novel inpainting algorithm combining the advantages of PDE-based schemes and examplar-based approaches. The proposed algorithm relies on the use of structure tensors to define the filling order priority and template matching. The structure tensors are computed in a hierarchic manner whereas the template matching is based on a K-nearest neighbor algorithm. The value K is adaptively set in function of the local texture information. Compared to two state of the art approaches, the proposed method provides more coherent results.

Efficient depth map compression based on lossless edge coding and diffusion

The multi-view plus depth video (MVD) format has recently been introduced for 3DTV and free-viewpoint video (FVV) scene rendering. Given one view (or several views) with its depth information, depth image-based rendering techniques have the ability to generate intermediate views. The MVD format however generates large volumes of data which need to be compressed for storage and transmission. This paper describes a new depth map encoding algorithm which aims at exploiting the intrinsic depth maps properties. Depth images indeed represent the scene surface and are characterized by areas of smoothly varying grey levels separated by sharp edges at the position of object boundaries. Preserving these characteristics is important to enable high quality view rendering at the receiver side. The proposed algorithm proceeds in three steps: the edges at object boundaries are first detected using a Sobel operator. The positions of the edges are encoded using the JBIG algorithm. The luminance values of the pixels along the edges are then encoded using an optimized path encoder. The decoder runs a fast diffusion-based inpainting algorithm which fills in the unknown pixels within the objects by starting from their boundaries. The performance of the algorithm is assessed against JPEG-2000 and HEVC, both in terms of PSNR of the depth maps versus rate as well as in terms of PSNR of the synthesized virtual views.

A Time-Dependent Saliency Model Combining Center and Depth Biases for 2D and 3D Viewing Conditions

The role of the binocular disparity in the deployment of visual attention is examined in this paper. To address this point, we compared eye tracking data recorded while observers viewed natural images in 2D and 3D conditions. The influence of disparity on saliency, center and depth biases is first studied. Results show that visual exploration is affected by the introduction of the binocular disparity. In particular, participants tend to look first at closer areas in 3D condition and then direct their gaze to more widespread locations. Beside this behavioral analysis, we assess the extent to which state-of-the-art models of bottom-up visual attention predict where observers looked at in both viewing conditions. To improve their ability to predict salient regions, low-level features as well as higher-level foreground/background cues are examined. Results indicate that, consecutively to initial centering response, the foreground feature plays an active role in the early but also middle instants of attention deployments. Importantly, this influence is more pronounced in stereoscopic conditions. It supports the notion of a quasi-instantaneous bottom-up saliency modulated by higher figure/ground processing. Beyond depth information itself, the foreground cue might constitute an early process of “selection for action”. Finally, we propose a time-dependent computational model to predict saliency on still pictures. The proposed approach combines low-level visual features, center and depth biases. Its performance outperforms state-of-the-art models of bottom-up attention.

3D view synthesis with inter-view consistency

In this paper, we propose a new pipeline to synthesize virtual views by extrapolation. It allows us to generate virtual views far away from each other, each presenting the exact same level of quality. This inter-view consistency is key to seamlessly navigate between viewpoints. Its computational cost is also lower than that of existing approaches. We compare the proposed approach with state-of-the-art methods and show the effectiveness of this new view synthesis pipeline.