Adaptive scale selection for hierarchical stereo

Yi Hung Jen, Enrique Dunn, Pierre Fite-Georgel, Jan Michael Frahm

Research output: Contribution to conferencePaperpeer-review

13 Scopus citations

Abstract

Hierarchical stereo provides an efficient coarse-to-fine mechanism for disparity map estimation. However, common drawbacks of such an approach include the loss of high frequency structures not observable at coarse scale levels, as well as the unrecoverable propagation of erroneous disparity estimates through the scale space. This paper presents an adaptive scale selection mechanism to determine a suitable resolution level from which to begin the hierarchical depth estimation process for each pixel. The proposed scale selection mechanism allows us to robustly implement variable cost aggregation in order to reduce the variability of the photo-consistency measure across scale space. We also incorporate a weighted shiftable window mechanism to enable error correction during coarse-to-fine depth refinement. Experiments illustrate the effectiveness of our approach in terms of disparity accuracy, while attaining a computational efficiency compromise between full resolution and hierarchical disparity map estimation.

Original languageEnglish
DOIs
StatePublished - 2011
Event2011 22nd British Machine Vision Conference, BMVC 2011 - Dundee, United Kingdom
Duration: 29 Aug 20112 Sep 2011

Conference

Conference2011 22nd British Machine Vision Conference, BMVC 2011
Country/TerritoryUnited Kingdom
CityDundee
Period29/08/112/09/11

Fingerprint

Dive into the research topics of 'Adaptive scale selection for hierarchical stereo'. Together they form a unique fingerprint.

Cite this