We describe a linear-time algorithm that recovers absolute camera orientations and positions, along with uncertainty estimates, for networks of terrestrial image nodes spanning hundreds of meters in outdoor urban scenes. The algorithm produces pose estimates globally consistent to roughly 0.1 # (2 milliradians) and 5 centimeters on average, or about four pixels of epipolar alignment.

Nowadays the interest in 3D reconstruction and modeling of real humans is one of the most challenging problems and a topic of great interest. The human models are used for movies, video games or ergonomics applications and they are usually created with scanner devices. In this paper a new method to reconstruct the shape of a static human is presented. Our approach is based on photogrammetric techniques and uses a sequence of images acquired around a standing person with a digital still video camera or with a camcorder. First the images are calibrated and orientated using a bundle adjustment and automatically recover the required tie points and initial approximations of the unknowns. After the establishment of a stable adjusted image block, an image matching process is performed between consecutive triplets of images. Finally the 3D coordinates of the matched points are computed with a mean accuracy of ca 2mm by forward ray intersection using the restored camera parameters. The obtained point cloud can then be triangulated to generate a surface model of the body or a virtual human model can be fitted to the recovered 3D data. Results of the 3D human point cloud with pixel color information are presented.

The generation of 3-D models from uncalibrated image sequences is a challenging problem that has been investigated in many research activities in the last decade. In particular, a topic of great interest is the modeling of realistic humans, for animation, manufacture or medicine purposes. Nowadays the common approaches try to reconstruct the human body using specialized hardware (laser scanners) resulting in high costs. In this contribution a di#erent method for the three-dimensional reconstruction of static human body shape from monocular image sequence is presented. The core of the presented work describes the calibration and orientation of the images, mostly based on photogrammetric techniques. Then the process includes also the extraction of correspondences on the body using a least squares matching algorithm and the reconstruction of the 3-D body model in point cloud form.

Abstract. Spatial reasoning is one of the central tasks in Computer Vision. It always has to deal with uncertain data. Projective geometry has become the working horse for modelling multiple view geometry, while modelling uncertainty with statistical tools has become a standard. Geometric reasoning in projective geometry with uncertain geometric elements has been advocated by Kanatani in the early 90’s, and recently made transparent and generalized to basic entities in projective geometry including transformations by Förstner and Heuel, exploiting the multilinearity of nearly all relations, such as incidence and identity, which results from the underlying Grassmann-Cayley algebra (cf. [21, 8, 7]). This paper generalizes geometric reasoning under uncertainty towards circles, spheres and conics, which play a role in many computer vision applications. In particular it will be shown how within the Clifford algebra of conformal space, as introduced by Hestenes et al. [11, 16], circles can be constructed from three uncertain points in 3D-Euclidean space, while propagating the covariance matrices of the points. This then enables us to obtain and visualize the uncertainty of the resulting circle. We also introduce the Clifford algebra over the vector space of 2D-conics, which allows us to apply the same error propagation procedures as for the Clifford algebra of conformal space.

The generation of 3-D models from uncalibrated sequences is a challenging problem that has been investigated in many research activities in the last decade. In particular, a topic of great interest is the modeling of real humans. In this paper a method for the 3-D reconstruction of static human body shapes from images acquired with a video-camera is presented. The process includes the orientation and calibration of the sequence, the extraction of correspondences on the body using least squares matching technique and the reconstruction of the 3-D point cloud of the human body.

A priori object information like parallelism and perpendicularity can be very useful for 3D-reconstruction, especially in architectural photogrammetry. With digital close-range imagery parallelism of object lines can be exploited by applying automatic vanishing point detection. This image analysis technique allows the detection of parallel object lines and leads to their spatial orientation in the camera system. The latter can be used for 3D-reconstruction as well as for the determination of the exterior orientation parameters of the image involved. A priori object information in the form of angles between object lines (like perpendicularity) can improve vanishing point detection considerably.

This thesis addresses the automatic calibration of two static surveillance cameras in a manmade world with orthogonal and parallel structures and a common ground plane. An approach is taken where the calibration of the interior orientation, the undistortion of the lens and the calibration of a camera’s rotation to the world perform before calibrating the camera centers, which allows methods that work in slightly overlapping as in non-overlapping views. We present a new incremental calibration composed of Expectation Maximization and Simulated Annealing that uses the uncertainties of noisy line segments to process a video stream instead of a single image. The advantage of video is that orthogonal and parallel edge