Departmental Papers (CIS)

Date of this Version


Document Type

Conference Paper


Suggested Citation:
Makadia, A., A. Patterson and K. Daniilidis. (2006). Fully Automatic Registration of 3D Point Clouds. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE.

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We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two point sets can have very little overlap. Crude alignment is achieved by estimation of the 3D-rotation from two Extended Gaussian Images even when the data sets inducing them have partial overlap. The technique is based on the correlation of the two EGIs in the Fourier domain and makes use of the spherical and rotational harmonic transforms. For pairs with low overlap which fail a critical verification step, the rotational alignment can be obtained by the alignment of constellation images generated from the EGIs. Rotationally aligned sets are matched by correlation using the Fourier transform of volumetric functions. A fine alignment is acquired in the final step by running Iterative Closest Points with just few iterations.



Date Posted: 12 October 2010

This document has been peer reviewed.