which is introduced in [@agarwal2013robust]. More recently, in [@yang2020graduated], several forms of robust cost functions are unified and solved using graduated non-convexity without an initial guess, which demonstrates good performance in 3D-3D registration, pose graph optimization, and is extended to non-minimal solver for shape reconstruction from an image in [@yang2020perfect]. Alternatively, in [@tzoumas2019outlier], the outlier rejection is solved by adaptively removing the measurements with large errors, which is simple but show superior performance than RANSAC. These methods achieve deterministic convergence, while some of them offer certifiable optimality (or sub-optimality guarantees).

Global optimization method
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Global optimization methods are proposed to achieve the global optimality and deterministic convergence. In this branch of literatures, Branch-and-Bound (BnB) is mostly used, which gradually prunes the solution space by coarse-to-fine division. In [@breuel2003implementation], BnB is used to solve the 2D-2D registration problems. In [@olsson2008branch], a general framework for point, line and plane features is proposed to solve 3D-3D registration via BnB. Integrated with mixed integer programming, the BnB optimization can converge faster [@li2009consensus][@chin2016guaranteed]. In [@speciale2017consensus], the linear matrix inequality constraints are introduced to mixed integer programming, resulting in a general-purpose faster BnB for all 2D-2D, 2D-3D and 3D-3D geometric vision problems. In the works mentioned 