Manhattan-world Urban Reconstruction from Point Clouds

Minglei Li KAUST, KSA Peter Wonka KAUST, KSA Liangliang Nan KAUST, KSA

Geometric Modeling and Processing mathscidoc:1608.16116

ECCV, 2016.9
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 first extract plane hypothesis from the points followed by an iterative refinement 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.
urban reconstruction, Manhattan-world scenes, reconstruction, box fi tting
[ Download ] [ 2016-08-29 14:34:57 uploaded by liangliangnan ] [ 1210 downloads ] [ 0 comments ]
  title={Manhattan-world Urban Reconstruction from Point Clouds},
  author={Minglei Li, Peter Wonka, and Liangliang Nan},
Minglei Li, Peter Wonka, and Liangliang Nan. Manhattan-world Urban Reconstruction from Point Clouds. 2016. In ECCV.
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