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We address the challenging issue of camera localization in a partially known environment, i.e. for which a geometric 3D model that covers only a part of the observed scene is available. When this scene is static, both known and unknown parts of the environment provide constraints on the camera motion. This paper proposes a nonlinear refinement process of an initial SfM reconstruction that takes advantage of these two types of constraints. Compare to those that exploit only the model constraints i.e. the known part of the scene, including the unknown part of the environment in the optimization process yields a faster, more accurate and robust refinement. It also presents a much larger convergence basin. This paper will demonstrate these statements on varied synthetic and real sequences for both 3D object tracking and outdoor localization applications.