Wen, TianciMihail, RaduAl-maliki, shathaLetang, JeanVidal, FranckVidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.2019-09-112019-09-112019978-3-03868-096-3https://doi.org/10.2312/cgvc.20191265https://diglib.eg.org:443/handle/10.2312/cgvc20191265Registration has been studied extensively for the past few decades. In this paper we propose to solve the registration of 3D triangular models onto 2D X-ray projections. Our approach relies extensively on global optimisation methods and fast X-ray simulation on GPU. To evaluate our pipeline, each optimisation is repeated 15 times to gather statistically meaningful results, in particular to assess the reproducibility of the outputs.We demonstrate the validity of our approach on two registration problems: i) 3D kinematic configuration of a 3D hand model, i.e. the recovery of the original hand pose from a postero-anterior (PA) view radiograph. The performance is measured by Mean Absolute Error (MAE). ii) Automatic estimation of the position and rigid transformation of geometric shapes (cube and cylinders) to match an actual metallic sample made of Ti/SiC fibre composite with tungsten (W) cores. In this case the performance is measured in term of F-score (86%), accuracy (95%), precision (75%), recall (100%), and true negative rate (94%). Our registration framework is successful for both test-cases when using a suitable optimisation algorithm.10.2312/cgvc.20191265105-113