We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...
We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...
Dense three-dimensional reconstruction of a scene from images is a challenging task. Usually, it is achieved by finding correspondences in successive images and computing the distance by means of epipolar geometry. In this paper, we propose a variational f ...
Stereo reconstruction is a fundamental problem of computer vision. It has been studied for more than three decades and significant progress has been made in recent years as evidenced by the quality of the models now being produced. This is highly related w ...
In this paper we describe an efficient software architecture for object-tracking, based on a stereoscopic vision system, that has been applied to a mobile robot controlled by a PC. After analyzing the epipolar rectification required to correct the original ...
