Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Modeling and calibration of automated zoom lenses
Modeling and calibration of automated zoom lenses
Robust methods for estimating pose and a sensitivity analysis
CVGIP: Image Understanding
Ray tracing on programmable graphics hardware
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Algorithmics for Hard Problems
Algorithmics for Hard Problems
A Four-step Camera Calibration Procedure with Implicit Image Correction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Simulation of cloud dynamics on graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Accurate Camera Calibration for Off-line, Video-Based Augmented Reality
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
A general method for comparing the expected performance of tracking and motion capture systems
Proceedings of the ACM symposium on Virtual reality software and technology
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
A Model of SIMD Machines and a Comparison of Various Interconnection Networks
IEEE Transactions on Computers
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In this paper we present a method for finding the optimal camera alignment for a tracking system with multiple cameras, by specifying the volume that should be tracked and an initial camera setup. The approach we use is twofold: on the one hand, we use a rather simple gradient based steepest descent method and on the other hand, we also implement a simulated annealing algorithm that features guaranteed optimality assertions. Both approaches are fully automatic and take advantage of modern graphics hardware since we implemented a GPU-based accelerated visibility test. The proposed algorithms can automatically optimize the whole camera setup by adjusting the given set of parameters. The optimization may have different goals depending on the desired application, e.g. one may wish to optimize towards the widest possible coverage of the specified volume, while others would prefer to maximize the number of cameras seeing a certain area to overcome heavy occlusion problems during the tracking process. Our approach also considers parameter constraints that the user may specify according to the local environment where the cameras have to be set up. This makes it possible to simply formulate higher level constraints e.g. all cameras have a vertical up vector. It individually adapts the optimization to the given situation and also asserts the feasibility of the algorithm's output.