Manhattan-world urban reconstruction from point clouds

Minglei Li, Peter Wonka, Liangliang Nan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
PublisherSpringer Verlag
Pages54-69
Number of pages16
Volume9908 LNCS
ISBN (Print)9783319464923
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9908 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Keywords

  • Box fitting
  • Manhattan-world scenes
  • Reconstruction
  • Urban reconstruction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Li, M., Wonka, P., & Nan, L. (2016). Manhattan-world urban reconstruction from point clouds. In Computer Vision - 14th European Conference, ECCV 2016, Proceedings (Vol. 9908 LNCS, pp. 54-69). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9908 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46493-0_4