  This paper proposes a global approach for the multi-view registration of
unordered range scans. As the basis of multi-view registration, pair-wise
registration is very pivotal. Therefore, we first select a good descriptor and
accelerate its correspondence propagation for the pair-wise registration. Then,
we design an effective rule to judge the reliability of pair-wise registration
results. Subsequently, we propose a model augmentation method, which can
utilize reliable results of pair-wise registration to augment the model shape.
Finally, multi-view registration can be accomplished by operating the pair-wise
registration and judgment, and model augmentation alternately. Experimental
results on public available data sets show, that this approach can
automatically achieve the multi-view registration of unordered range scans with
good accuracy and effectiveness.
