Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Model-Based Brightness Constraints: On Direct Estimation of Structure and Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Contribution to the Determination of Vanishing Points Using Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Camera Calibration from a Single Manhattan Image
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Manhattan World: Compass Direction from a Single Image by Bayesian Inference
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Grouping Based on Projective Geometry Constraints and Uncertainty
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment
International Journal of Robotics Research
Efficient methods for point matching with known camera orientation
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
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The problem of inferring 3D orientation of a camera from video sequences has been mostly addressed by first computing correspondences of image features. This intermediate step is now seen as the main bottleneck of those approaches. In this paper, we propose a new 3D orientation estimation method for urban (indoor and outdoor) environments, which avoids correspondences between frames. The scene property exploited by our method is that many edges are oriented along three orthogonal directions; this is the recently introduced Manhattan world (MW) assumption. The main contributions of this paper are: the definition of equivalence classes of equiprojective orientations, the introduction of a new small rotation model, formalizing the fact that the camera moves smoothly, and the decoupling of elevation and twist angle estimation from that of the compass angle. We build a probabilistic sequential orientation estimation method, based on an MW likelihood model, with the above-listed contributions allowing a drastic reduction of the search space for each orientation estimate. We demonstrate the performance of our method using real video sequences.