M. Li, P. Wonka, L. Nan
European Conference on Computer Vision (ECCV), (2016)
Manhattan-world urban scenes are common in the real world. We propose a
fully automatic approach for reconstructing such scenes from 3D point
samples. Our key idea is to represent the geometry of the buildings in
the scene using a set of well-aligned boxes. We rst extract plane
hypothesis from the points followed by an iterative renement step.
Then, candidate boxes are obtained by partitioning the space of the
point cloud into a non-uniform grid. After that, we choose an optimal
subset of the candidate boxes to approximate the geometry of the
buildings. The contribution of our work is that we transform scene
reconstruction into a labeling problem that is solved based on a novel
Markov Random Field formulation. Unlike previous methods designed for
particular types of input point clouds, our method can obtain faithful
reconstructions from a variety of data sources. Experiments demonstrate
that our method is superior to state-of-the-art methods.