Kerber, J.Bokeloh, M.Wand, M.Seidel, H.-P.Holly Rushmeier and Oliver Deussen2015-02-282015-02-2820131467-8659https://doi.org/10.1111/j.1467-8659.2012.03226.xIn this paper, we present a novel method for detecting partial symmetries in very large point clouds of 3D city scans. Unlike previous work, which has only been demonstrated on data sets of a few hundred megabytes maximum, our method scales to very large scenes: We map the detection problem to a nearest-eighbour problem in a low-dimensional feature space, and follow this with a cascade of tests for geometric clustering of potential matches. Our algorithm robustly handles noisy real-world scanner data, obtaining a recognition performance comparable to that of state-of-the-art methods. In practice, it scales linearly with scene size and achieves a high absolute throughput, processing half a terabyte of scanner data overnight on a dual socket commodity PC.In this paper we present a novel method for detecting partial symmetries in very large point clouds of 3D city scans. Unlike previous work, which has only been demonstrated on data sets of a few hundred megabytes maximum, our method scales to very large scenes: We map the detection problem to a nearest-eighbor problem in a lowdimensional feature space, and follow this with a cascade of tests for geometric clustering of potential matches. Our algorithm robustly handles noisy real-world scanner data, obtaining a recognition performance comparable to that of state-of-the-art methods. In practice, it scales linearly with scene size and achieves a high absolute throughput, processing half a terabyte of scanner data overnight on a dual socket commodity PC.I.4.8 [IMAGE PROCESSING AND COMPUTER VISION]Scene AnalysisShapeI.3.5 [COMPUTER GRAPHICS]Computational Geometry and Object ModelingHierarchy and geometric transformationsI.5.3 [PATTERN RECOGNITION]ClusteringSimilarity measuressymmetry detectionfeature detectionlarge scene processingclustering