Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery
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 . 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.
KeywordsGround Truth Camera Parameter Urban Scene Vanishing Point Gauss Sphere
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