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Point clouds are collected nowadays from a plethora of sensors, some havinghigher accuracies and higher costs, some having lower accuracies but also lowercosts. Not only there is a large choice for different sensors, but also thesecan be transported by different platforms, which can provide different scangeometries. In this work we test the extraction of four different keypointdetectors and three feature descriptors. We benchmark performance in terms ofcalculation time and we assess their performance in terms of accuracy in theirability in coarse automatic co-registration of two clouds that are collectedwith different sensors, platforms and scan geometries. One, which we define ashaving the higher accuracy, and thus will be used as reference, was surveyedvia a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a LivoxHorizon over a walking path with un-even ground.The novelty in this workconsists in comparing several strategies for fast alignment of point cloudsfrom very different surveying geometries, as the drone has a bird's eye viewand the bicycle a ground-based view. An added challenge is related to the lowercost of the bicycle sensor ensemble that, together with the rough terrain,reasonably results in lower accuracy of the survey. The main idea is to userange images to capture a simplified version of the geometry of the surveyedarea and then find the best features to match keypoints. Results show that NARFfeatures detected more keypoints and resulted in a faster co-registrationprocedure in this scenariowhereas the accuracy of the co-registration issimilar to all the combinations of keypoint detectors and features.

Automatic coarse co-registration of point clouds from diverse scan geometries: a test of detectors and descriptors