Pham, MT and Woodford, OJ and Perbet, F and Maki, A and Stenger, B and Cipolla, R (2011) A new distance for scale-invariant 3D shape recognition and registration. Proceedings of the IEEE International Conference on Computer Vision. pp. 145-152.Full text not available from this repository.
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 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. © 2011 IEEE.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:25|
|Last Modified:||16 Jan 2017 10:14|