A mixture of Manhattan frames: Beyond the Manhattan world

Julian Straub, Guy Rosman, Oren Freifeld, John J. Leonard, John W. Fisher

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

57 Scopus citations

Abstract

Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system. Known as the Manhattan-World model, this assumption is widely used in computer vision and robotics. The complexity of many real-world scenes, however, necessitates a more flexible model. We propose a novel probabilistic model that describes the world as a mixture of Manhattan frames: each frame defines a different orthogonal coordinate system. This results in a more expressive model that still exploits the orthogonality constraints. We propose an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that utilizes the geometry of the unit sphere. We demonstrate the versatility of our Mixture-of-Manhattan-Frames model by describing complex scenes using depth images of indoor scenes as well as aerial-LiDAR measurements of an urban center. Additionally, we show that the model lends itself to focal-length calibration of depth cameras and to plane segmentation.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages3770-3777
Number of pages8
ISBN (Electronic)9781479951178, 9781479951178
DOIs
StatePublished - 24 Sep 2014
Externally publishedYes
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: 23 Jun 201428 Jun 2014

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Country/TerritoryUnited States
CityColumbus
Period23/06/1428/06/14

Keywords

  • Bayesian Model
  • Depth Camera Calibration
  • Manhattan World
  • Plane Segmentation
  • Scene Representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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