Philips has developed a multi-view autostereoscopic 3D television which is capable of displaying video in real 3D without the use of external glasses or any other devices. Creating 3D content for these screens is difficult because they require not only the video itself but also the depth map as input. Depth maps are computed from a multi-view recording of the video sequence. To extract the dept...

In this paper we propose a new method for estimating depth using a fusion of defocus and stereo, that relaxes the assumption of a pinhole model of the camera. It avoids the correspondence problem of stereo. Main advantage of this algorithm is simultaneous recovery of depth and image restoration. The depth (blur or disparity) in the scene and the intensity process in the focused image are indivi...

We describe a non-parametric, “example-based” method for estimating the depth of an object, viewed in a single photo. Our method consults a database of example 3D geometries, searching for those which look similar to the object in the photo. The known depths of the selected database objects act as shape priors which constrain the process of estimating the object’s depth. We show how this proces...

In this paper we propose two techniques to qualitatively estimate distance in monocular vision. Two kinds of approaches are described, the former based on texture analysis and the latter on histogram inspection. Although both the methods allow only to determine whether a point within an image is nearer or farther than another with respect to the observer, they can be usefully exploited in all t...

In this paper, we present a new approach for dense stereo matching which is mainly oriented towards the recovery of depth map of an observed scene. The extraction of depth information from the disparity map is well understood, while the correspondence problem is still subject to errors. In our approach, we propose optimizing correlation based technique by detecting and rejecting mismatched poin...

This paper presents the estimation of depth in an indoor, structured environment based on a stereo setup consisting of two fish-eye lenses, with parallel optical axes, mounted on a robot platform. The use of fish-eye lenses provides for a large field of view to estimate better the depth of features very close to the lens. To extract significant information from the fish-eye lens images, we firs...

Depth estimation in computer vision and robotics is most commonly done via stereo vision (stereopsis), in which images from two cameras are used to triangulate and estimate distances. However, there are also numerous monocular visual cues— such as texture variations and gradients, defocus, color/haze, etc.—that have heretofore been little exploited in such systems. Some of these cues apply even...

We describe an estimation technique which, given a measurement of the depth of a target from a wide-fieldof-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidenceinterval decision theory. The approach is evaluated in terms of t...