Abstract. We present methods for recovering surface height fields such as geometric details of 3D textures by incorporating shadow constraints. We introduce shadow graphs which give a new graph-based representation for shadow constraints. It can be shown that the shadow graph alone is sufficient to solve the shape-from-shadow problem from a dense set of images. Shadow graphs provide a simpler and more systematic approach to represent and integrate shadow constraints from multiple images. To recover height fields from a sparse set of images, we propose a method for integrated shadow and shading constraints. Previous shape-fromshadow algorithms do not consider shading constraints while shape-from-shading usually assumes there is no shadow. Our method is based on collecting a set of images from a fixed viewpoint as a known light source changes its position. It first builds a shadow graph from shadow constraints from which an upper bound for each pixel can be derived if the height values of a small number of pixels are initialized correctly. Finally, a constrained optimization procedure is designed to make the results from shape-from-shading consistent with the height bounds derived from the shadow constraints. Our technique is demonstrated on both synthetic and real imagery.

This paper outlines a new scheme that makes simultaneous reconstruction of the scene and the camera motion from an image sequence taken by an uncalibrated affine camera. Correspondences of point, line and conic features are used in a unified manner to constrain the reconstruction. For the projective camera, the trifocal tensor has proven to be very important in reconstruction from three views. We derive the analogous tensor for the affine camera model. It is also shown how to estimate the components of this tensor in order to recover the viewing geometry. Then, for longer image sequences it is shown how to use factorisation to recover the scene and the camera motion. However, the drawback of all factorisation methods is the difficulty of handling missing data, i.e. some features are only visible in some images. In this case, an alternative method is given that is based on closure constraints. The proposed methods are illustrated on real data.

This paper proposes a method for recovery of projective shape and motion from multiple images by factorization of a matrix containing the images of all scene points. Compared to previous methods, this method can handle perspective views and occlusions jointly. The projective depths of image points are estimated by the method of Sturm & Triggs [5] using epipolar geometry. Occlusions are solved by the extension of the method by Jacobs [4] for filling of missing data. This extension can exploit the geometry of perspective camera so that both points with known and unknown projective depths are used. Many ways of combining the two methods exist, and therefore several of them have been examined and the one with the best results is presented. The new method gives accurate results in practical situations, as demonstrated here with a series of experiments on laboratory and outdoor image sets. It becomes clear that the method is particularly suited for wide base-line multiple view stereo.

Abstract. Lambertian photometric stereo with uncalibrated light directions and intensities determines the surface normals only up to an invertible linear transformation. We show that if object reflectance is a sum of Lambertian and specular terms, the ambiguity reduces into a 2dof group of transformations (compositions of isotropic scaling, rotation around the viewing vector, and change in coordinate frame handedness). Such ambiguity reduction is implied by the consistent viewpoint constraint which requires that all lights reflected around corresponding specular normals must give the same vector (the viewing direction). To employ the constraint, identification of specularities in images corresponding to four different point lights in general configuration suffices. When the consistent viewpoint constraint is combined with integrability constraint, binary convex/concave ambiguity composed with isotropic scaling results. The approach is verified experimentally. We observe that an analogical result applies to the case of uncalibrated geometric stereo with four affine cameras in a general configuration observing specularities from a single distant point light source. 1