  In this paper, a shape-constrained iterative algorithm is proposed to
register a rigid template point-cloud to a given reference point-cloud. The
algorithm embeds a shape-based similarity constraint into the principle of
gravitation. The shape-constrained gravitation, as induced by the reference,
controls the movement of the template such that at each iteration, the template
better aligns with the reference in terms of shape. This constraint enables the
alignment in difficult conditions indtroduced by change (presence of outliers
and/or missing parts), translation, rotation and scaling. We discuss efficient
implementation techniques with least manual intervention. The registration is
shown to be useful for change detection in the 3D point-cloud. The algorithm is
compared with three state-of-the-art registration approaches. The experiments
are done on both synthetic and real-world data. The proposed algorithm is shown
to perform better in the presence of big rotation, structured and unstructured
outliers and missing data.

Computer Vision and Pattern Recognition