We describe a convex programming framework for pose estimation in 2D/3D point-set registration with unknown point correspondences. We give two mixed-integer nonlinear program (MINLP) formulations of the 2D/3D registration problem when there are multiple 2D images, and propose convex relaxations for both the MINLPs to semidefinite programs that can be solved efficiently by interior point methods. Our approach to the 2D/3D registration problem is non-iterative in nature as we jointly solve for pose and correspondence. Furthermore, these convex programs can readily incorporate feature descriptors of points to enhance registration results. We prove that the convex programs exactly recover the solution to the MINLPs under certain noiseless condition. We apply these formulations to the registration of 3D models of coronary vessels to their 2D projections obtained from multiple intra-operative fluoroscopic images. For this application, we experimentally corroborate the exact recovery property in the absence of noise and further demonstrate robustness of the convex programs in the presence of noise.

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interior point noiseless exact recovery semidefinite programs convex programming framework 2d3d registration problem pose estimation in 2d3d pointset registration with unknown point pose correspondence noise approach registration of 3d models of coronary vessels mixedinteger nonlinear program minlp formulations minlps intraoperative fluoroscopic 2d projections feature descriptors of points

AnkurKapoor,YuehawKhoo,.Non-Iterative Rigid 2D/3D Point-Set Registration Using Semidefinite Programming. 25 (7),2956-2970.

AnkurKapoor,YuehawKhoo,"Non-Iterative Rigid 2D/3D Point-Set Registration Using Semidefinite Programming" 25

AnkurKapoor,YuehawKhoo,"Non-Iterative Rigid 2D/3D Point-Set Registration Using Semidefinite Programming"