Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Advanced Engineering Informatics
3D Line Reconstruction of a Road Environment Using an In-Vehicle Camera
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Image-based street-side city modeling
ACM SIGGRAPH Asia 2009 papers
Modeling and Estimation of the Dynamics of Planar Algebraic Curves via Riccati Equations
Journal of Mathematical Imaging and Vision
An accurate and robust visual-compass algorithm for robot-mounted omnidirectional cameras
Robotics and Autonomous Systems
A multi-stage linear approach to structure from motion
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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We present a novel method for recovering the 3D-line structure of a scene from multiple widely separated views. Traditional optimization-based approaches to line-based structure from motion minimize the error between measured line segments and the projections of corresponding 3D lines. In such a case, 3D lines can be optimized using a minimum of 4 parameters. We show that this number of parameters can be further reduced by introducing additional constraints on the orientations of lines in a 3D scene. In our approach, 2D-lines are automatically detected in images with the assistance of an EM-based vanishing point estimation method which assumes the existence of edges along mutally orthogonal vanishing directions. Each detected line is automatically labeled with the orientation (e.g. vertical, horizontal) of the 3D line which generated the measurement, and it is this additional knowledge that we use to reduce the number of degrees of freedom of 3D lines during optimization. We present 3D reconstruction results for urban scenes based on manually established feature correspondences across images.