<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Variational Approaches for Motion and Structure from Monocular Video"^^ . "Motion and structure estimation are elementary problems of computer vision. These are active areas of research, even though the first methods were proposed several decades ago. We develop new approaches for motion and structure estimation for autonomous driving. An autonomous vehicle requires an accurate model of its environment, wrong decisions made by an autonomous car can have severe consequences. We assume the monocular setup, where only a single camera is mounted on the car.\r\nOutdoor traffic sequences are challenging for optical flow estimation. The high speed of the car causes large displacements in the optical flow field, the lighting conditions are unstable and there can be strong\r\ndistortions due to reflections and difficult weather conditions. We propose new discrete methods, which determine optical flow as optimal configuration of probabilistic graphical models. \r\nThe first approach selects sparse locations in the reference frame, and matches them across the second image. The best correspondences, which match constraints from a multiple view configuration, are considered motion vectors in a graphical model. In a second approach, we solve for dense optical flow by approximating the original infeasible graphical model with a sequence of reduced models.\r\nThe monocular configuration poses challenges to the estimation of scene structure, camera positions and scene parameters need to be estimated jointly. The geometry of multiple views creates blind spots in the images, and adds a scale ambiguity, which both to not exist in a setup with multiple cameras. \r\nWe propose two methods for structure estimation. The first approach determines the energy optimal camera track, given optical flow and depth observations. A further approach estimates camera motion and a piecewise planar scene description jointly from a single optical\r\nflow field. The scene description contains depth and plane normal information. \r\nWe evaluate our approaches for motion and structure estimation on different real world and rendered datasets. In addition to evaluation on publicly available evaluation data, we evaluate on a new rendered dataset with ground truth plane normals."^^ . "2017" . . . . . . . "Andreas"^^ . "Neufeld"^^ . "Andreas Neufeld"^^ . . . . . . "Variational Approaches for Motion and Structure from Monocular Video (PDF)"^^ . . . "Thesis_Neufeld.pdf"^^ . . . "Variational Approaches for Motion and Structure from Monocular Video (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Variational Approaches for Motion and Structure from Monocular Video (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Variational Approaches for Motion and Structure from Monocular Video (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Variational Approaches for Motion and Structure from Monocular Video (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Variational Approaches for Motion and Structure from Monocular Video (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #23722 \n\nVariational Approaches for Motion and Structure from Monocular Video\n\n" . "text/html" . . . "004 Informatik"@de . "004 Data processing Computer science"@en . . . "510 Mathematik"@de . "510 Mathematics"@en . .