Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo

Gottfried Graber, Thomas Pock, Stefano Soatto, Jonathan Balzer

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

Abstract

We propose a method for dense three-dimensional surface reconstruction that leverages the strengths of shape-based approaches, by imposing regularization that respects
the geometry of the surface, and the strength of depthmap-based stereo, by avoiding costly computation of surface topology. The result is a near real-time variational reconstruction algorithm free of the staircasing artifacts that affect depth-map and plane-sweeping approaches. This is made possible by exploiting the gauge ambiguity to design
a novel representation of the regularizer that is linear in the parameters and hence amenable to be optimized with state-of-the-art primal-dual numerical schemes.
Original languageEnglish
Title of host publicationThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher.
Pages511-520
Publication statusPublished - 2015
EventCVPR 2015: IEEE Conference on Computer Vision and Pattern Recognition - Boston, United States
Duration: 7 Jun 201512 Jun 2015

Conference

ConferenceCVPR 2015
CountryUnited States
CityBoston
Period7/06/1512/06/15

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Application

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    Graber, G., Pock, T., Soatto, S., & Balzer, J. (2015). Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 511-520). ..