In this chapter, we focus on keypoint-based point cloud registration which has proven to be among the most efficient strategies for aligning pairs of overlapping scans. We present a novel and fully automated framework which consists of six components addressing (i) the generation of 2D image representations in the form of range and intensity images, (ii) point quality assessment, (iii) feature extraction and matching, (iv) the forward projection of 2D keypoints to 3D space, (v) correspondence weighting, and (vi) point cloud registration. For the respective components, we take into account different approaches and our main contributions address the issue of how to increase the robustness, efficiency, and accuracy of point cloud registration by either introducing further constraints (e.g., addressing a correspondence weighting based on point quality measures) or replacing commonly applied approaches by more promising alternatives. The latter may not only address the involved strategy for point cloud registration, but also the involved approaches for feature extraction and matching. In a detailed evaluation, we demonstrate that, instead of directly aligning sets of corresponding 3D points, a transfer of the task of point cloud registration to the task of solving the Perspective-n-Point (PnP) problem or to the task of finding the relative orientation between sets of bearing vectors offers great potential for future research. Furthermore, our results clearly reveal that the further consideration of both a correspondence weighting based on point quality measures and a selection of an appropriate feature detector–descriptor combination may result in significant advantages with respect to robustness, efficiency, and accuracy.