Abstract
In this work, we describe man-made structures via an appropriate structure assumption, called Atlanta world, which
contains a vertical direction (typically the gravity direction)
and a set of horizontal directions orthogonal to the vertical
direction. Contrary to the commonly used Manhattan world
assumption, the horizontal directions in Atlanta world are
not necessarily orthogonal to each other. While Atlanta
world permits to encompass a wider range of scenes, this
makes the solution space larger and the problem more challenging. Given a set of inputs, such as lines in a calibrated
image or surface normals, we propose the first globally optimal method of inlier set maximization for Atlanta direction estimation. We define a novel search space for Atlanta
world, as well as its parameterization, and solve this challenging problem by a branch-and-bound framework. Experimental results with synthetic and real-world datasets have
successfully confirmed the validity of our approach