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The automatic and accurate registration of terrestrial laser scanning (TLS) data is a topic of great interest in the domains of citymodeling, construction surveying or cultural heritage. While numerous of the most recent approaches focus on keypoint-based pointcloud registration relying on forward-projected 2D keypoints detected in panoramic intensity images, little attention has been paidto the selection of appropriate keypoint detector-descriptor combinations. Instead, keypoints are commonly detected and describedby applying well-known methods such as the Scale Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF). Inthis paper, we present a framework for evaluating the influence of different keypoint detector-descriptor combinations on the results ofpoint cloud registration. For this purpose, we involve five different approaches for extracting local features from the panoramic intensityimages and exploit the range information of putative feature correspondences in order to define bearing vectors which, in turn, may beexploited to transfer the task of point cloud registration from the object space to the observation space. With an extensive evaluation ofour framework on a standard benchmark TLS dataset, we clearly demonstrate that replacing SIFT and SURF detectors and descriptorsby more recent approaches significantly alleviates point cloud registration in terms of accuracy, efficiency and robustness

FINDING A GOOD FEATURE DETECTOR-DESCRIPTOR COMBINATION FOR THE 2D KEYPOINT-BASED REGISTRATION OF TLS POINT CLOUDS