Pham, MT and Woodford, OJ and Perbet, F and Maki, A and Gherardi, R and Stenger, B and Cipolla, R (2013) Scale-invariant vote-based 3D recognition and registration from point clouds. Studies in Computational Intelligence, 411. pp. 137-162. ISSN 1860-949XFull text not available from this repository.
This chapter 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 transformations for the first time. We introduce a new distance between poses in this spacethe 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. © 2013 Springer-Verlag Berlin Heidelberg.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||09 Dec 2016 18:36|
|Last Modified:||24 Feb 2017 23:38|