This paper proposes a global approach for the multi-view registration of unordered range scans.Our method starts with the pair-wise registration, where multi-scale descriptor is selected for feature point and the propagation of feature correspondence is accordingly accelerated.Subsequently, we design an effective rule to judge the reliability of these pair-wise registration results.
According to the judgment of reliability, Occurrence of geohelminths in the soil of public squares in Rio Branco, Acre State, Brazilian Western Amazon we propose a model fusion method, which can utilize reliable results of pair-wise registration to augment the model shape.Finally, multi-view registration can be achieved by operating the pair-wise registration, reliability judgment, and model fusion alternately.The proposed approach can be applied to scene reconstruction and robot mapping.
Experimental results conducted on public datasets show that the proposed approach can automatically achieve multi-view registration of unordered range scans.Compared with other related approaches, the proposed approach has TRABAJAR SIN SOLUCIONES. ENTREVISTA CON KELLER EASTERLING superior performances in accuracy and effectiveness.