The recovery of depth from defocused images involves calcurecovering the depth [2]. lating the depth of various points in a scene by modeling the The problem addressed in this paper is as follows— effect that the focal parameters of the camera have on images Given a scene with object points at unknown depths from acquired with a small depth of field. In the approach to depth the camera, obtain ...

Models of stereoscopic depth perception for both natural and random-dot images have focused mainly on the matching of achromatic features of binocular images. Recently, a growing body of research has investigated whether chromatic features can also contribute to the construction of stereoscopic depth. Here we present experiments yielding color-contingent depth aftereffects comparable in magnitu...

(76) Inventors: ggégalglb?sfjl’l A pose for an object in a scene is determined by ?rst rendering Cambrid ’e M A (Us) g ’ sets of Virtual images of a model of the object using a Virtual g ’ camera. Each set of Virtual images is for a different known _ pose the model, and constructing Virtual depth edge map from (21) Appl' NO" 12/648’190 each Virtual image, which are stored in a database. A set o...

We consider the effect of depth-image compression artifacts on the quality of virtual views rendered using neighboring views. Such view rendering processes are utilized in new video applications such as 3D television (3DTV) and free viewpoint video (FVV). We first analyze how compression artifacts in compressed depth-images result in distortions in rendered views. We show that the rendering pos...

Monocular depth estimation and image deblurring are two fundamental tasks in computer vision, given their crucial role understanding 3D scenes. Performing any of them by relying on a single is an ill-posed problem. The recent advances the field Deep Convolutional Neural Networks (DNNs) have revolutionized many including deblurring. When it comes to using defocused images, recovery All-in-Focus ...