We present a technique for building a three-dimensional description of a static scene from a dense sequence of images. These images are taken in such rapid succession that they form a solid block of data in which the temporal continuity from image to image is approximately equal to the spatial continuity in an individual image. The technique utilizes knowledge of the camera motion to form and analyze slices of this solid. These slices directly encode not only the three-dimensional positions of objects, but also such spatiotemporal events as the occlusion of one object by another. For straight-line camera motions, these slices have a simple linear structure that makes them easier to analyze. The analysis computes the threedimensional positions of object features, marks occlusion boundaries on the objects, and builds a threedimensional map of "free space. " In our article, we first describe the application of this technique to a simple camera motion, and then show how projective duality is used to extend the analysis to a wider class of camera motions and object types that include curved and moving objects. 1

Abstract. A newpractical method is given for the self-calibration of a camera. In this method, at least three images are taken from the same point in space with different orientations of the camera and calibration is computed from an analysis of point matches between the images. The method requires no knowledge of the orientations of the camera. Calibration is based on the image correspondences only. This method differs fundamentally from previous results by Maybank and Faugeras on selfcalibration using the epipolar structure of image pairs. In the method of this paper, there is no epipolar structure since all images are taken from the same point in space. Since the images are all taken from the same point in space, determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur. The calibration method is evaluated on several sets of synthetic and real image data. 1

Modelling th# push broom sensors commonly used in satellite imagery is quite di#cult and computationally intensive due to th# complicated motion ofth# orbiting satellite with respect to th# rotating earth# In addition, th# math#46 tical model is quite complex, involving orbital dynamics, andh#(0k is di#cult to analyze. Inth#A paper, a simplified model of apush broom sensor(th# linear push broom model) is introduced. Ith as th e advantage of computational simplicity wh#A9 atth# same time giving very accurate results compared with th# full orbitingpush broom model. Meth# ds are given for solving th# major standardph# togrammetric problems for th e linear push broom sensor. Simple non-iterative solutions are given for th# following problems : computation of th# model parameters from groundcontrol points; determination of relative model parameters from image correspondences between two images; scene reconstruction given image correspondences and ground-control points. In addition, th# linearpush broom model leads toth#0 retical insigh ts th# t will be approximately valid for th# full model as well.Th# epipolar geometry of linear push broom cameras in investigated and sh own to be totally di#erent from th at of a perspective camera. Neverth eless, a matrix analogous to th e essential matrix of perspective cameras issh own to exist for linear push broom sensors. Fromth#0 it is sh# wn th# t a scene is determined up to an a#ne transformation from two viewswith linearpush broom cameras. Keywords :push broom sensor, satellite image, essential matrixph# togrammetry, camera model The research describ ed in this paper hasb een supportedb y DARPA Contract #MDA97291 -C-0053 1 Real Push broom sensors are commonly used in satellite cameras, notably th# SPOT satellite forth# generatio...

A new practical method is given for the self-calibration of a camera. In this method, at least three images are taken from the same point in space with different orientations of the camera and calibration is computed from an analysis of point matches between the images. The method requires no knowledge of the orientations of the camera. Calibration is based on the image correspondences only. This method differs fundamentally from previous results by Maybank and Faugeras on self-calibration using the epipolar structure of image pairs. In the method of this paper, there is no epipolar structure since all images are taken from the same point in space, and so Maybank and Faugeras’s method does not apply. Since the images are all taken from the same point in space, determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur. A non-iterative calibration algorithm is given that works with any number of images. An iterative refinement method that may be used with noisy data is also described. The algorithm is implemented and validated on several sets of synthetic and real image data.

We present a method of recovering shape from shading that solves directly for the surface height. By using a discrete formulation of the problem, we are able to achieve good convergence behavior by employing numerical solution techniques more powerful than gradient descent methods derived from variational calculus. Because we directly solve for height, we avoid the problem of finding an integrable surface maximally consistent with surface orientation. Furthermore, since we do not need additional constraints to make the problem well posed, we use a smoothness constraint only to drive the system towards a good solution; the weight of the smoothness term is eventually reduced to near zero. Also, by solving directly for height, we can use stereo processing to provide initial and boundary conditions. Our shape from shading technique, as well as its relation to stereo, is demonstrated on both synthetic and real imagery. 1 Introduction The problem of extracting shape from the shaded image of...

We present an autonomous mobile robot navigation system using stereo fish-eye lenses for navigation in an indoor structured environment, and for generating a model of the imaged scene. The system estimates the three-dimensional (3D) position of significant features in the scene, and by estimating its relative position, navigates through narrow passages and makes turns at corridor ends. Fish-eye lenses are used to provide a large field of view, which helps in imaging objects close to the robot and in making smooth transitions in the direction of motion. Calibration is performed for the lens-camera setup and the distortion is corrected to obtain accurate quantitative measurements. A vision based algorithm that uses the vanishing points of extracted segments from a scene in a few 3D orientations provides an accurate estimate of the robot orientation. This is used, in addition to 3D recovery via stereo correspondence, to maintain the robot motion in a purely translational path as well as to r...

We present a method to estimate a dense and sharp depth map using multiple cameras. A key issue for obtaining sharp depth map is how to overcome the harmful influence of occlusion. Thus, we first propose an occlusion-overcoming strategy which selectively use the depth information from multiple cameras. With a simple selection technique, we resolve the occlusion problem considerably at a slight sacrifice of noise tolerance. Another key issue in area-based stereo matching is the size of matching window. We propose to use a hierarchical estimation scheme that attempts to acquire a sharp depth map such that edges of the depth map coincide with object boundaries on the one hand, reduce noisy estimates due to insufficient size of matching window on the other hand. Owing to the unique property of occlusion-overcoming strategy, we can utilize full benefit of hierarchical schemes. We show the method can produce a sharp and correct depth map for a variety of images. 1 1 Introduction Recently,...

. We present a method to estimate a dense and sharp depth map using multiple cameras. A key issue in obtaining sharp depth map is how to overcome the harmful influence of occlusion. Thus, we first propose an occlusion-overcoming strategy which selectively use the depth information from multiple cameras. With a simple sort and discard technique, we resolve the occlusion problem considerably at a slight sacrifice of noise tolerance. Another key issue in area-based stereo matching is the size of matching window. We propose a hierarchical scheme that attempts to acquire a sharp depth map such that edges of the depth map coincide with object boundaries on the one hand, reduce noisy estimates due to insufficient size of matching window on the other hand. We show the hierarchical method can produce a sharp and correct depth map. 1 Introduction Recently, computer vision technology is widely used for achieving high degree of freedom and efficiency in video content creation. Thus, it is even sa...

In this paper, we present a new intensity based technique for recovering depth information from two or more images. Our method uses planar patches to approximate 3-D surfaces. In order to recover the depth information, the view-line constraint and the imaging geometry are introduced. The view-line constraint is used to restrict the position of a planar patch. From the constraint we can get the candidates of the corresponding planar patch. The imaging geometry will then help us to find the best estimation from the candidates. A hypothesis and verification based optimization procedure is used. We project each candidate of the planar patch perspectively onto the observed images, and calculate the intensity difference between the projected image and the observed images. The candidate which has the best fitting with the observed data will be selected as the solution. This method is different from the traditional stereo algorithms because it does not require to solve the corresponding proble...

In stereo vision the depth of a 3-D point is estimated based are classified, based on the matching primitives, into areabased and feature-based techniques. Area-based methods on the position of its projections on the left and right images. correspond brightness patterns in two images [14, 24]. The image plane of cameras that produces the images consists of discrete pixels. This discretization of images generates uncertainty in estimation of the depth at each 3-D point. In this paper, we investigate the effect of vergence and spatially vary-ing resolution on the depth estimation error. First, vergence is studied when pairs of stereo images with uniform resolution are used. Then the problem is studied for a stereo system similar to that of humans, in which cameras have high resolution in These algorithms have several drawbacks which are pointed out in [12, 18]. Feature-based methods match fea-tures such as edges [3, 9, 11, 13, 15, 21], and linear edge segments [1, 23]. Finally, the depth of each point is obtained using triangulation. Cameras have an image plane which consists of a number of discrete picture elements (pixels). In general, these pix-the center and nonlinearly decreasing resolution toward the els are uniformly arranged in a two-dimensional array ac-periphery. In this paper we are only concerned with error in cording to certain industrial standards. The projection of depth perception, assuming that stereo matching is already each 3-D point in the scene is approximated to the nearest done. © 1996 Academic Press, Inc. pixel—the resulting error is referred to as discretization error. In stereo, discretization error generates uncertainty 1.