Active estimation of distance in a robotic system that replicates human eye movement
Robotics and Autonomous Systems
Stereoscopic Video Synthesis from a Monocular Video
IEEE Transactions on Visualization and Computer Graphics
Automatic object extraction and reconstruction in active video
Pattern Recognition
A framework for modeling 3D scenes using pose-free equations
ACM Transactions on Graphics (TOG)
Robust and efficient feature tracking for indoor navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image-based exploration obstacle avoidance for mobile robot
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Computer Vision and Image Understanding
AniCAP: an animated 3d CAPTCHA scheme based on motion parallax
CANS'11 Proceedings of the 10th international conference on Cryptology and Network Security
Parking assistance using dense motion-stereo
Machine Vision and Applications
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The task of recovering three-dimensional (3-D) geometry from two-dimensional views of a scene is called 3-D reconstruction. It is an extremely active research area in computer vision. There is a large body of 3-D reconstruction algorithms available in the literature. These algorithms are often designed to provide different tradeoffs between speed, accuracy, and practicality. In addition, even the output of various algorithms can be quite different. For example, some algorithms only produce a sparse 3-D reconstruction while others are able to output a dense reconstruction. The selection of the appropriate 3-D reconstruction algorithm relies heavily on the intended application as well as the available resources. The goal of this paper is to review some of the commonly used motion-parallax-based 3-D reconstruction techniques and make clear the assumptions under which they are designed. To do so efficiently, we classify the reviewed reconstruction algorithms into two large categories depending on whether a prior calibration of the camera is required. Under each category, related algorithms are further grouped according to the common properties they share.