No abstract

We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera. The key idea is to derive a region-based cost function using temporally consistent local color histograms. While such region-based cost functions are commonly optimized using first-order gradient descent techniques, we systematically derive a Gauss-Newton optimization scheme which gives rise to drastically faster convergence and highly accurate and robust tracking performance. We furthermore propose a novel complex dataset dedicated for the task of monocular object pose tracking and make it publicly available to the community. To our knowledge, it is the first to address the common and important scenario in which both the camera as well as the objects are moving simultaneously in cluttered scenes. In numerous experiments -including our own proposed dataset -we demonstrate that the proposed Gauss-Newton approach outperforms existing approaches, in particular in the presence of cluttered backgrounds, heterogeneous objects and partial occlusions. ! Henning Tjaden is a PhD student at the Johannes Gutenberg University of Mainz and a research assistant at the RheinMain University of Applied Sciences in Germany where he is part of the Computer Vision and Mixed Reality group. His current research interests include real-time object pose estimation of 3D objects from 2D images by using only a single monocular camera. He is especially interested in improving the robustness, accuracy and runtime performance in order enable approaches to be applicable to a large variety of practical scenarios in dynamic human environments. Ulrich Schwanecke received a PhD in Mathematics (2000) from the Technische Universität Darmstadt, Germany. He then spent a year as a postdoctoral researcher at

Abstract. We give an exact geometry kernel for conic arcs, algorithms for exact computation with low-degree algebraic numbers, and an algorithm for computing the arrangement of conic arcs that immediately leads to a realization of regularized boolean operations on conic polygons. A conic polygon, or polygon for short, is anything that can be obtained from linear or conic halfspaces (= the set of points where a linear or quadratic function is non-negative) by regularized boolean operations. The algorithm and its implementation are complete (they can handle all cases), exact (they give the mathematically correct result), and efficient (they can handle inputs with several hundred primitives).

No abstract

No abstract