@article {12060,
	title = {Visual space-time geometry - A tool for perception and the imagination},
	journal = {Proceedings of the IEEE},
	volume = {90},
	year = {2002},
	month = {2002/07//},
	pages = {1113 - 1135},
	abstract = {Although the fundamental ideas underlying research efforts in the field of computer vision have not radically changed in the past two decades, there has been a transformation in the way work in this field is conducted. This is primarily due to the emergence of a number of tools, of both a practical and a theoretical nature. One such tool, celebrated throughout the nineties, is the geometry of visual space-time. It is known under a variety of headings, such as multiple view geometry, structure from motion, and model building. It is a mathematical theory relating multiple views (images) of a scene taken at different viewpoints to three-dimensional models of the (possibly dynamic) scene. This mathematical theory gave rise to algorithms that take as input images (or video) and provide as output a model of the scene. Such algorithms are one of the biggest successes of the field and they have many applications in other disciplines, such as graphics (image-based rendering, motion capture) and robotics (navigation). One of the difficulties, however is that the current tools cannot yet be fully automated, and they do not provide very accurate results. More research is required for automation and high precision. During the past few years we have investigated a number of basic questions underlying the structure from motion problem. Our investigations resulted in a small number of principles that characterize the problem. These principles, which give rise to automatic procedures and point to new avenues for studying the next level of the structure from motion problem, are the subject of this paper.},
	keywords = {3-D motion estimation, Buildings, Computer vision, Geometry, Graphics, Image sequences, Layout, Mathematical model, mathematical theory, model building, Motion estimation, multiple view geometry, multiple views, Navigation, optical flow, optical illusions, patch correspondence, Rendering (computer graphics), Robotics and automation, Solid modeling, structure from motion, three-dimensional models, visual space-time},
	isbn = {0018-9219},
	doi = {10.1109/JPROC.2002.801440},
	author = {Ferm{\"u}ller, Cornelia and Baker, P. and Aloimonos, J.}
}
@article {14234,
	title = {New eyes for building models from video},
	journal = {Computational Geometry},
	volume = {15},
	year = {2000},
	month = {2000/02//},
	pages = {3 - 23},
	abstract = {Models of real-world objects and actions for use in graphics, virtual and augmented reality and related fields can only be obtained through the use of visual data and particularly video. This paper examines the question of recovering shape models from video information. Given video of an object or a scene captured by a moving camera, a prerequisite for model building is to recover the three-dimensional (3D) motion of the camera which consists of a rotation and a translation at each instant. It is shown here that a spherical eye (an eye or system of eyes providing panoramic vision) is superior to a camera-type eye (an eye with restricted field of view such as a common video camera) as regards the competence of 3D motion estimation. This result is derived from a geometric/statistical analysis of all the possible computational models that can be used for estimating 3D motion from an image sequence. Regardless of the estimation procedure for a camera-type eye, the parameters of the 3D rigid motion (translation and rotation) contain errors satisfying specific geometric constraints. Thus, translation is always confused with rotation, resulting in inaccurate results. This confusion does not happen for the case of panoramic vision. Insights obtained from this study point to new ways of constructing powerful imaging devices that suit particular tasks in visualization and virtual reality better than conventional cameras, thus leading to a new camera technology. Such new eyes are constructed by putting together multiple existing video cameras in specific ways, thus obtaining eyes from eyes. For a new eye of this kind we describe an implementation for deriving models of scenes from video data, while avoiding the correspondence problem in the video sequence.},
	keywords = {model building, shape reconstruction, structure from motion, video analysis},
	isbn = {0925-7721},
	doi = {10.1016/S0925-7721(99)00044-9},
	url = {http://www.sciencedirect.com/science/article/pii/S0925772199000449},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J. and Brodsk{\'y},Tom{\'a}{\v s}}
}
