3D camera tracking is an important issue for many kinds of applications such as augmented reality and robotics navigation. When image sequences are used, tracking needs to be initialized by matching 2D data extracted from images with 3D data representing a priori knowledge of the world. However, this process is difficult and the accuracy of the pose estimation strongly depends on the accuracy of the previous matching step. In this paper, we present two original approaches that achieve a 2D/3D points-based matching in order to initialize the 3D camera tracking. The first one is semi-automatic and requires the intervention of the user. The second one is completely automatic. Both approaches are based on SURF descriptors, which have the advantages of being fast and robust against outliers. A description of these two approaches is given and results obtained from experiments performed on real data are exposed and discussed.