A New Approach for Vanishing Point Detection in Architectural Environments

By Carsten Rother. British Machine Vision Conference, 2000

316 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/06d4a6e2c59b3c4bf5d7216d32e6e811eb843f43

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In Geology

Citations in the corpus

Geometric reasoning for single image structure recovery (2009, methodology): “Then the three candidates are evaluated using the cost function proposed in [20].”
Thinking Inside the Box: Using Appearance Models and Context Based on Room Geometry (2010, methodology): “[1] for vanishing point estimation, which builds upon the method of [23].”
Pop-up SLAM: Semantic monocular plane SLAM for low-texture environments (2016, methodology): “If the camera rotation matrix is Rw,c ∈ R3×3, then vi can be computed by [5] [19]:”
Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery (2008, methodology): “The calibration method is not reliable if one or more of the camera axes are parallel or close to parallel to the Manhattan directions (Rother, 2002).”
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes (2009, methodology): “their intersection in a RANSAC framework as in [48].”
Camera-based analysis of text and documents: a survey (2005, background): “As described in [84], in a man-made environment where many 3D orthogonal lines exist, the estimation of vanishing points provides a way to recover the perspective.”
Non-iterative approach for fast and accurate vanishing point detection (2009, background): “Finally, Rother proposes heuristics to recover the vanishing points corresponding to Manhattan directions [16].”
Video Compass (2002, background): “In some cases all pairwise intersections of the detected line segments are considered for initialization, yielding dominant peak detection [13,2].”
Recovering the spatial layout of cluttered rooms (2009, methodology): “Several works [3, 12, 20] address estimation of vanishing points from an image (see [5] for an excellent discussion).”
Recovering free space of indoor scenes from a single image (2012, background): “The ability to estimate the scene’s 3D geometry is important for many tasks, such as robot navigation and object placement/manipulation.”