We propose a framework for extracting structure from stereo which represents the scene as a collection of approximately planar layers. Each layer consists of an explicit 3D plane equation, a colored image with per-pixel opacity (a sprite), and a per-pixel depth offset relative to the plane. Initial

region and dynamic programming algorithms are two well-known strategies for computing dense correspondences. However the former approach fails in regions of low texture, while the latter imposes an ordering constraint which is not always satisfied in stereo images. In this study, we present two new

We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing

Robotic systems are becoming smaller, lower power, and cheaper, enabling their application in areas not previously considered. This is true of vision systems as well. SRI’s Small Vision Module (SVM) is a compact, inexpensive realtime device for computing dense stereo range images, which are a

We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing

Abstract. This work presents an evolutionary approach to improve the infection algorithm to solve the problem of dense stereo matching. Dense stereo matching is used for 3D reconstruction in stereo vision in order to achieve fine texture detail about a scene. The algorithm presented in this paper

Abstract—Despite the significant number of stereo vision algorithms proposed in literature in the last decade, most proposals are notably computationally demanding and/or memory hungry so that it is unfeasible to employ them in application scenarios requiring real-time or near real-time processing

This paper presents an efficient implementation for correlation based stereo. Research in this area can roughly be divided in two classes: improving accuracy regardless of computing time and scene reconstruction in real-time. Algorithms achieving video frame rates must have strong limitations

different stereo algorithms. We use real imagery, and ex-plore two different methodologies, with different strengths and weak-nesses. Our first methodology is based upon manual computation of dense ground truth. Here we make use of a two stereo pairs: one of these, from the University of Tsukuba, contains

We propose a novel energy minimisation framework for the dense reconstruction of stereo image sequences that incorporates data fidelity as well as spatial and temporal regularity. An iterated dynamic programming scheme is proposed to minimise the energy function. We also present an efficient