An omni stereo pair of images provides depth information from stereo up to 360 degrees around a central observer. A method for synthesizing omni stereo video textures was recently introduced which was Based on blending of overlapping stereo videos that were filmed several seconds apart. While it produced loop able omni stereo videos that can be displayed up to 360 degrees around a viewer, ghosting was visible within blended overlaps. This paper presents a stereo stitching method to render these overlaps without any ghosting. The stitching method uses a graph-cut minimization. We show results for scenes with differents types of motion, such as water flows, leaves in the wind and moving people.

We introduce in this paper a camera setup for stereo immersive (omnistereo) capture. An omnistereo pair of images gives stereo information up to 360 degrees around a central observer. Previous methods to produce omnistereo images assume a static scene in order to stitch together multiple images captured by a stereo camera rotating on a fixed tripod. Our omnipolar camera setup uses a minimum of 3 cameras with fisheye lenses. The multiple epipoles are used as locations to stitch the images together and produce omnistereo images with no horizontal misalignments due to parallax. We show results of using 3 cameras to capture an unconstrained dynamic scene while the camera is travelling. The produced omnistereo videos are formatted to be displayed on a cylindrical screen or dome.

We present a scanning method that recovers dense sub pixel camera-projector correspondence without requiring any photometric calibration nor preliminary knowledge of their relative geometry. Sub pixel accuracy is achieved by considering several zero-crossings defined by the difference between pairs of unstructured patterns. We use gray-level band-pass white noise patterns that increase robustness to indirect lighting and scene discontinuities. Simulated and experimental results show that our method recovers scene geometry with high sub pixel precision, and that it can handle many challenges of active reconstruction systems. We compare our results to state of the art methods such as micro phase shifting and modulated phase shifting.

Most methods for synthesizing panoramas assume that the scene is static. A few methods have been proposed for synthesizing stereo or motion panoramas, but there has been little attempt to synthesize panoramas that have both stereo and motion. One faces several challenges in synthesizing stereo motion panoramas, for example, to ensure temporal synchronization between left and right views in each frame, to avoid spatial distortion of moving objects, and to continuously loop the video in time. We have recently developed a stereo motion panorama method that tries to address some of these challenges. The method blends space-time regions of a video XYT volume, such that the blending regions are distinct and translate over time. This article presents a perception experiment that evaluates certain aspects of the method, namely how well observers can detect such blending regions. We measure detection time thresholds for different blending widths and for different scenes, and for monoscopic versus stereoscopic videos. Our results suggest that blending may be more effective in image regions that do not contain coherent moving objects that can be tracked over time. For example, we found moving water and partly transparent smoke were more effectively blended than swaying branches. We also found that performance in the task was roughly the same for mono versus stereo videos.

A panoramic stereo (or omnistereo) pair of images provides depth information from stereo up to 360 degrees around a central observer. Because omnistereo lenses or mirrors do not yet exist, synthesizing omnistereo images requires multiple stereo camera positions and baseline orientations. Recent omnistereo methods stitch together many small field of view images called slits which are captured by one or two cameras following a circular motion. However, these methods produce omnistereo images for static scenes only. The situation is much more challenging for dynamic scenes since stitching needs to occur over both space and time and should synchronize the motion between left and right views as much as possible. This paper presents the first ever method for synthesizing panoramic stereo video textures. The method uses full frames rather than slits and uses blending across seams rather than smoothing or matching based on graph cuts. The method produces loopable panoramic stereo videos that can be displayed up to 360 degrees around a viewer.

Reconstruction from structured light can be greatly affected by interreflections between surfaces in the scene. This paper introduces band-pass white noise patterns designed specifically to reduce interreflections, and still be robust to standard challenges in scanning systems such as scene depth discontinuities, defocus and low camera-projector pixel ratio. While this approach uses unstructured light patterns that increase the number of required projected images, it is up to our knowledge the first method that is able to recover scene disparities in the presence of both scene discontinuities and interreflections. Furthermore, the method does not require calibration (geometric nor photometric) or post-processing such as dynamic programming or phase unwrapping. We show results for a challenging scene and compare them to correspondences obtained with the well-known Gray code and Phase-shift methods.

An omnistereoscopic image is a pair of panoramic images that enables stereoscopic depth perception all around an observer. An omnistereo projection on a cylindrical display does not require tracking of the observer's viewing direction. However, such a display introduces stereo distortions. In this article, we investigate two projection models for rendering 3D scenes in omnistereo. The first is designed to give zero disparity errors at the center of the visual field. The second is the well-known slit-camera model. For both models, disparity errors are shown to increase gradually in the periphery, as visual stereo acuity decreases. We use available data on human stereoscopic acuity limits to argue that depth distortions caused by these models are so small that they cannot be perceived.

Many applications in computer vision use Principal Components Analysis (PCA), for example, in camera calibration, stereo, localization and motion estimation. We present a new and fast PCA-based method to analyze optical snow. Optical snow is a complex form of visual motion that occurs when an observer moves through a highly cluttered 3D scene. For this category of motion field, no spatial or depth coherence can be assumed. Previous methods for measuring optical snow have used a wedge filter in a spatiotemporal frequency domain. The PCA method is also based on the spatiotemporal frequency domain analysis, but examines a different geometry property of the spectrum. We compare the results of the PCA method to the previous methods using both real and synthetic sequences. 1