A new distance for scale-invariant 3D shape recognition and registration

Authors:
Minh-Tri Pham;Oliver J. Woodford;Frank Perbet;Atsuto Maki;Bjorn Stenger;Roberto Cipolla
Affiliations:
Cambridge Research Laboratory, Toshiba Research Europe Ltd, UK;Cambridge Research Laboratory, Toshiba Research Europe Ltd, UK;Cambridge Research Laboratory, Toshiba Research Europe Ltd, UK;Cambridge Research Laboratory, Toshiba Research Europe Ltd, UK;Cambridge Research Laboratory, Toshiba Research Europe Ltd, UK;Department of Engineering, University of Cambridge, UK
Venue:
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Year:
2011

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Abstract

This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this space -- the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach.