Abstract. In this paper we describe a flexible approach for determining the relative orientation of the camera with respect to the scene. The main premise of the approach is the fact that in man-made environments, the majority of lines is aligned with the principal orthogonal directions of the world coordinate frame. We exploit this observation towards efficient detection and estimation of vanishing points, which provide strong constraints on camera parameters and relative orientation of the camera with respect to the scene. By combining efficient image processing techniques in the line detection and initialization stage we demonstrate that simultaneous grouping and estimation of vanishing directions can be achieved in the absence of internal parameters of the camera. Constraints between vanishing points are then used for partial calibration and relative rotation estimation. The algorithm has been tested in a variety of indoors and outdoors scenes and its efficiency and automation makes it amenable for implementation on robotic platforms. Key words: Vanishing point estimation, relative orientation, calibration using vanishing points, vision guided mobile and aerial robots. 1

Abstract not found

This paper proposes a technique for estimating piecewise planar models of objects from their images and geometric constraints. First, assuming a bounded noise in the localization of 2D points, the position of the 3D point is estimated as a polyhedron containing all the possible solutions of the triangulation. Then, given the topological structure of the 3D points cloud, geometric relationships among facets, such as coplanarity, parallelism, orthogonality, and angle equality, are automatically detected. A subset of them that is sufficient to stabilize the 3D model estimation is selected with a flow-network based algorithm. Finally a feasible instance of the 3D model, i.e. one that satisfies the selected geometric relationships and whose 3D points lie within the associated polyhedral bounds, is computed by solving a Constraint Satisfaction Problem. 1.

In this paper we describe a flexible approach for constructing mosaics of architectural environments from a sparse set of uncalibrated views. The main contribution this paper is the use of environment constraints in order increase the efficiency and level of automation of the mosaic construction process. The observation that in architectural environments, the majority of lines is aligned with the principal orthogonal directions of the world coordinate frame, will be exploited in different stages of the mosaic construction pipeline. The automated detection of vanishing directions will enable us to partially calibrate the camera an estimate the relative orientation of the camera with respect to the scene from a single view. These initial estimates will facilitate efficient feature matching, computation of displacements between the views as well as alignment of multiple views. While the approach described here will be presented in the context of rotational mosaics, the alignment and matching techniques are applicable for general displacements, where the constraints of man-made environments are present and the displacement between the views is large. Key words: panoramic mosaic construction, vanishing point estimation, relative orientation, partial calibration using vanishing points.

This paper proposes a technique for estimating piecewise planar models of objects from their images and geometric constraints. First, assuming a bounded noise in the localization of 2D points, the position of the 3D point is estimated as a polyhedron containing all the possible solutions of the triangulation. Then, given the topological structure of the 3D points cloud, geometric relationships among facets, such as coplanarity, parallelism, orthogonality, and angle equality, are automatically detected. A subset of them that is sufficient to stabilize the 3D model estimation is selected with a flow-network based algorithm. Finally a feasible instance of the 3D model, i.e. one that satisfies the geometric constraints and whose 3D vertices lie within the associated polyhedral bounds, is computed by solving a Constraint Satisfaction Problem. The process accommodates uncertainty in a non-probabilistic fashion and thus provides rigorous results. Synthetic and real experiments illustrate the approach. Key words: PACS: