Geometric image parsing in man-made environments
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Simultaneous estimation of vanishing points and their converging lines using the EM algorithm
Pattern Recognition Letters
Vanishing point detection using cascaded 1D Hough Transform from single images
Pattern Recognition Letters
Vision Based UAV Attitude Estimation: Progress and Insights
Journal of Intelligent and Robotic Systems
Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment
International Journal of Robotics Research
Geometric Image Parsing in Man-Made Environments
International Journal of Computer Vision
Real-time estimation of 3D scene geometry from a single image
Pattern Recognition
Fast fusion moves for multi-model estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Vanishing point detection by segment clustering on the projective space
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Vanishing points estimation and line classification in a manhattan world
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Globally optimal consensus set maximization through rotation search
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
An accurate method for line detection and manhattan frame estimation
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Corisco: Robust edgel-based orientation estimation for generic camera models
Image and Vision Computing
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We address the problem of efficiently estimating the rotation of a camera relative to the canonical 3D Cartesian frame of an urban scene, under the so-called "Manhattan World" assumption [1,2]. While the problem has received considerable attention in recent years, it is unclear how current methods stack up in terms of accuracy and efficiency, and how they might best be improved. It is often argued that it is best to base estimation on all pixels in the image [2]. However, in this paper, we argue that in a sense, less can be more: that basing estimation on sparse, accurately localized edges, rather than dense gradient maps, permits the derivation of more accurate statistical models and leads to more efficient estimation. We also introduce and compare several different search techniques that have advantages over prior approaches. A cornerstone of the paper is the establishment of a new public groundtruth database which we use to derive required statistics and to evaluate and compare algorithms.