Pose Determination from Line-to-Plane Correspondences: Existence Condition and Closed-Form Solutions
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
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
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Vanishing Point Detection in Complex Man-made Worlds
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
LSD: A Fast Line Segment Detector with a False Detection Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Image Parsing in Man-Made Environments
International Journal of Computer Vision
Optimal estimation of vanishing points in a Manhattan world
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The problem of estimating vanishing points for visual scenes under the Manhattan world assumption [1, 2] has been addressed for more than a decade. Surprisingly, the special characteristic of the Manhattan world that lines should be orthogonal or parallel to each other is seldom well utilized. In this paper, we present an algorithm that accurately and efficiently estimates vanishing points and classifies lines by thoroughly taking advantage of this simple fact in the Manhattan world with a calibrated camera. We first present a one-unknown-parameter representation of the 3D line direction in the camera frame. Then derive a quadratic which is employed to solve three orthogonal vanishing points formed by a line triplet. Finally, we develop a RANSAC-based approach to fulfill the task. The performance of proposed approach is demonstrated on the York Urban Database[3] and compared to the state-of-the-art method.