A computer-implemented method for head image registration, comprising: providing a plurality of combined 2D and depth images of a head captured from different positions, determining landmark points which are predefined points on the head for each of the combined 2D and depth images , and performing a pairwise coarse registration between pairs of combined 2D and depth images based on the landmark points , characterized in that the method further comprises: after the pairwise coarse registration, performing a fine registration of the coarse registered combined 2D and depth images based on full point clouds represented by the combined 2D and depth images, in that the fine registration is performed selectively based on RGBD odometry or point to plane ICP. iterative closest point, depending on a geometrical difference between the combined 2D and depth images of the respective pair after the coarse registration, and in that determining the landmark points comprises determining a visibility probability of the landmark points based on a predetermined depth assigned to each landmark point and the 2D images of the combined 2D images, selecting landmark points based on the visibility probability, and providing 3D landmark points for the selected landmark points based on the combined 2D and depth images. providing a plurality of combined 2D and depth images of a head captured from different positions, determining landmark points which are predefined points on the head for each of the combined 2D and depth images , and performing a pairwise coarse registration between pairs of combined 2D and depth images based on the landmark points , characterized in that the method further comprises: after the pairwise coarse registration, performing a fine registration of the coarse registered combined 2D and depth images based on full point clouds represented by the combined 2D and depth images, in that the fine registration is performed selectively based on RGBD odometry or point to plane ICP. iterative closest point, depending on a geometrical difference between the combined 2D and depth images of the respective pair after the coarse registration, and in that determining the landmark points comprises determining a visibility probability of the landmark points based on a predetermined depth assigned to each landmark point and the 2D images of the combined 2D images, selecting landmark points based on the visibility probability, and providing 3D landmark points for the selected landmark points based on the combined 2D and depth images. after the pairwise coarse registration, performing a fine registration of the coarse registered combined 2D and depth images based on full point clouds represented by the combined 2D and depth images, in that the fine registration is performed selectively based on RGBD odometry or point to plane ICP. iterative closest point, depending on a geometrical difference between the combined 2D and depth images of the respective pair after the coarse registration, and in that determining the landmark points comprises determining a visibility probability of the landmark points based on a predetermined depth assigned to each landmark point and the 2D images of the combined 2D images, selecting landmark points based on the visibility probability, and providing 3D landmark points for the selected landmark points based on the combined 2D and depth images. <EOS>