Shape and Motion from Image Streams: a Factorization Method Parts 2,8,10 Full Report on the Orthographic Case
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shape and the Stereo Correspondence Problem
International Journal of Computer Vision
3D Scene Reconstruction from Reflection Images in a Spherical Mirror
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAM
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Consistent Depth Maps Recovery from a Video Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Shape and Motion from Image Sequences Using Affine Approximation
ICIC '09 Proceedings of the 2009 Second International Conference on Information and Computing Science - Volume 02
Video stabilization based on a 3D perspective camera model
The Visual Computer: International Journal of Computer Graphics
Piecewise planar scene reconstruction from sparse correspondences
Image and Vision Computing
Efficient non-consecutive feature tracking for structure-from-motion
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Discovering Higher Level Structure in Visual SLAM
IEEE Transactions on Robotics
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In this work, we perform three-dimensional scene recovery from image data capturing railway transportation corridors. Typical three-dimensional scene recovery methods initialise recovered feature positions by searching for correspondences between image frames. We intend to take advantage of a relationship between image data and recovered scene data to reduce the search space traversed when performing such correspondence matching.We build multi-dimensional Gaussian models of recurrent visual features associated with distributions representing recovery results from our own dense planar recovery method. Results show that such a scheme decreases the number of checks made per feature to 6% of a comparable exhaustive method, whilst unaffecting accuracy. Further, the proposed method performs competitively when compared with other methods presented in literature.