Abstract
Nowadays, three dimensional point cloud processing plays a very important role in a wide range of areas: autonomous driving, robotics, cartography, etc. Three dimensional point cloud registration pipelines have high computational complexity, mainly because of the cost of point feature signature calculation. By selecting keypoints and using only them for registration, data points that are interesting in some way, one can significantly reduce the number of points for which feature signatures are needed, hence the running time of registration pipelines. Consequently, keypoint detectors have a prominent role in an efficient processing pipeline. In this paper, we propose to analyze the usefulness of various keypoint detection algorithms and investigate whether and when it is worth to use a keypoint detector for registration. We define the goodness of a keypoint detection algorithm based on the success and quality of registration. Most keypoint detection methods require manual tuning of their parameters for best results. Here we revisit the most popular methods for keypoint detection in 3D point clouds and perform automatic parameter tuning with goodness of registration and run time as primary objectives. We compare keypoint-based registration to registration with randomly selected points and using all data points as a baseline. In contrast to former work, we use point clouds of different sizes, with and without noise, and register objects with different sizes.
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Varga, D., Laki, S., Szalai-Gindl, J., Dobos, L., Vaderna, P., Formanek, B. (2020). On Fast Point Cloud Matching with Key Points and Parameter Tuning. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_35
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