A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Oriented projective geometry
Performance Evaluation and Analysis of Vanishing Point Detection Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision
Vanishing Point Detection by Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calibrated, Registered Images of an Extended Urban Area
International Journal of Computer Vision
Vanishing Point Detection without Any A Priori Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Cascaded Hough Transform as an Aid in Aerial Image Interpretation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Using Geometric Constraints through Parallelepipeds for Calibration and 3D Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
What can we learn about the scene structure from three orthogonal vanishing points in images
Pattern Recognition Letters
Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Practical modeling technique for large-scale 3D building models from ground images
Pattern Recognition Letters
Interpreting perspective images
Artificial Intelligence
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The analysis of vanishing points on digital images provides strong cues for inferring the 3D structure of the depicted scene and can be exploited in a variety of computer vision applications. In this paper, we propose a method for estimating vanishing points in images of architectural environments that can be used for camera calibration and pose estimation, important tasks in large-scale 3D reconstruction. Our method performs automatic segment clustering in projective space --- a direct transformation from the image space --- instead of the traditional bounded accumulator space. Since it works in projective space, it handles finite and infinite vanishing points, without any special condition or threshold tuning. Experiments on real images show the effectiveness of the proposed method. We identify three orthogonal vanishing points and compute the estimation error based on their relation with the Image of the Absolute Conic (IAC) and based on the computation of the camera focal length.