This paper investigates the use of the Space Carving algorithm with outdoor image sequences, using a lambertian lighting model. A new consistency function is proposed that uses a statistical comparison instead of the voxel centroid sampling that was initially proposed. This is important when there is more detail in the images than can be stored in a voxel representation. The new function is evaluated using synthetic data and real image sequences. 1 Introduction Many dierent techniques have been applied to the problem of reconstructing three-dimensional shape from image sequences. These techniques all work well for constrained problems, such as small camera baseline [6, 1, 2, 8], or smooth curved objects [4]. Recently voxel based algorithms [5, 9] have been demonstrated which can reconstruct very complex shapes, but at the cost of large memory requirements. One of these algorithms is called Space Carving [5] (see section 2.1 for a description). In this paper the eectiveness o...

When a scene is viewed from two different viewpoints there are often regions which are only visible in one of the two views. These occluded regions give important information about depth discontinuities in the image. When a background line is obscured by a foreground object, it forms a T-junction in the image, and these junction points can be used to detect occlusion. Two algorithms are presented in this paper. The first algorithm uses the trifocal tensor to automatically locate T-junctions visible in three views, whilst the second application uses junction points to obtain a constraint for the detection of planar surfaces. 1 Introduction The stereo correspondence problem is one of the oldest problems in computer vision, yet a general and robust solution remains elusive. One of the major difficulties for stereo matching is the presence of occlusion, although significant advances have been made by computing an occlusion map whilst computing the correspondence [2, 10, 5, 9]. Th...

The object of this paper is to find a quick and accurate method for computing the projection matrices of an image sequence, so that the error is distributed evenly along the sequence. It assumes that a set of correspondences between points in the images is known, and that these points represent rigid points in the world. This paper extends the algebraic minimisation approach developed by Hartley so that it can be used for long image sequences. This is achieved by initially computing a trifocal tensor using the three most extreme views. The intermediate views are then computed linearly using the trifocal tensor. An iterative algorithm as presented which perturbs the twelve entries of one camera matrix so that the algebraic error along the whole sequence is minimised.