A new high resolution depth map estimation system using stereo vision and depth sensing device

Depth map estimation is a classical problem in computer vision. Conventional depth estimation relies on stereo/multi-view matching or depth sensing devices alone. In this paper, we propose a system which addresses high resolution and high quality depth estimation based on joint fusion of stereo and Kinect data. The problem is formulated as a maximum a posteriori probability (MAP) estimation problem and reliability of two devices are derived. The depth map estimated is further refined by color image guided depth matting and a 2D polynomial regression (LPR)-based filtering. Experimental results show that our system can provide high quality and resolution depth map, which complements the strengths of stereo vision and Kinect depth sensor. © 2013 IEEE.published_or_final_versio

The Video Mesh: A Data Structure for Image-based Video Editing

This paper introduces the video mesh, a data structure for representing video as 2.5D "paper cutouts." The video mesh allows interactive editing of moving objects and modeling of depth, which enables 3D effects and post-exposure camera control. The video mesh sparsely encodes optical flow as well as depth, and handles occlusion using local layering and alpha mattes. Motion is described by a sparse set of points tracked over time. Each point also stores a depth value. The video mesh is a triangulation over this point set and per-pixel information is obtained by interpolation. The user rotoscopes occluding contours and we introduce an algorithm to cut the video mesh along them. Object boundaries are refined with perpixel alpha values. The video mesh is at its core a set of texture mapped triangles, we leverage graphics hardware to enable interactive editing and rendering of a variety of effects. We demonstrate the effectiveness of our representation with a number of special effects including 3D viewpoint changes, object insertion, and depth-of-field manipulation

Color-aware Deep Temporal Backdrop Duplex Matting System

Deep learning-based alpha matting showed tremendous improvements in recent years, yet, feature film production studios still rely on classical chroma keying including costly post-production steps. This perceived discrepancy can be explained by some missing links necessary for production which are currently not adequately addressed in the alpha matting community, in particular foreground color estimation or color spill compensation. We propose a neural network-based temporal multi-backdrop production system that combines beneficial features from chroma keying and alpha matting. Given two consecutive frames with different background colors, our one-encoder-dual-decoder network predicts foreground colors and alpha values using a patch-based overlap-blend approach. The system is able to handle imprecise backdrops, dynamic cameras, and dynamic foregrounds and has no restrictions on foreground colors. We compare our method to state-of-the-art algorithms using benchmark datasets and a video sequence captured by a demonstrator setup. We verify that a dual backdrop input is superior to the usually applied trimap-based approach. In addition, the proposed studio set is actor friendly, and produces high-quality, temporal consistent alpha and color estimations that include a superior color spill compensation

Image-based rendering and synthesis

Multiview imaging (MVI) is currently the focus of some research as it has a wide range of applications and opens up research in other topics and applications, including virtual view synthesis for three-dimensional (3D) television (3DTV) and entertainment. However, a large amount of storage is needed by multiview systems and are difficult to construct. The concept behind allowing 3D scenes and objects to be visualized in a realistic way without full 3D model reconstruction is image-based rendering (IBR). Using images as the primary substrate, IBR has many potential applications including for video games, virtual travel and others. The technique creates new views of scenes which are reconstructed from a collection of densely sampled images or videos. The IBR concept has different classification such as knowing 3D models and the lighting conditions and be rendered using conventional graphic techniques. Another is lightfield or lumigraph rendering which depends on dense sampling with no or very little geometry for rendering without recovering the exact 3D-models.published_or_final_versio

Disparity-compensated view synthesis for s3D content correction

International audienceThe production of stereoscopic 3D HD content is considerably increasing and experience in 2-view acquisition is in progress. High quality material to the audience is required but not always ensured, and correction of the stereo views may be required. This is done via disparity-compensated view synthesis. A robust method has been developed dealing with these acquisition problems that introduce discomfort (e.g hyperdivergence and hyperconvergence...) as well as those ones that may disrupt the correction itself (vertical disparity, color difference between views...). The method has three phases: a preprocessing in order to correct the stereo images and estimate features (e.g. disparity range...) over the sequence. The second (main) phase proceeds then to disparity estimation and view synthesis. Dual disparity estimation based on robust block-matching, discontinuity-preserving filtering, consistency and occlusion handling has been developed. Accurate view synthesis is carried out through disparity compensation. Disparity assessment has been introduced in order to detect and quantify errors. A post-processing deals with these errors as a fallback mode. The paper focuses on disparity estimation and view synthesis of HD images. Quality assessment of synthesized views on a large set of HD video data has proved the effectiveness of our method

Object segmentation from low depth of field images and video sequences

This thesis addresses the problem of autonomous object segmentation. To do so the proposed segementation method uses some prior information, namely that the image to be segmented will have a low depth of field and that the object of interest will be more in focus than the background. To differentiate the object from the background scene, a multiscale wavelet based assessment is proposed. The focus assessment is used to generate a focus intensity map, and a sparse fields level set implementation of active contours is used to segment the object of interest. The initial contour is generated using a grid based technique. The method is extended to segment low depth of field video sequences with each successive initialisation for the active contours generated from the binary dilation of the previous frame's segmentation. Experimental results show good segmentations can be achieved with a variety of different images, video sequences, and objects, with no user interaction or input. The method is applied to two different areas. In the first the segmentations are used to automatically generate trimaps for use with matting algorithms. In the second, the method is used as part of a shape from silhouettes 3D object reconstruction system, replacing the need for a constrained background when generating silhouettes. In addition, not using a thresholding to perform the silhouette segmentation allows for objects with dark components or areas to be segmented accurately. Some examples of 3D models generated using silhouettes are shown