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Keywords:

  • symmetry detection;
  • feature detection;
  • large scene processing;
  • clustering
  • I.4.8 [IMAGE PROCESSING AND COMPUTER VISION]: Scene Analysis–Shape;
  • I.3.5 [COMPUTER GRAPHICS]: Computational Geometry and Object Modeling–Hierarchy and geometric transformations;
  • I.5.3 [PATTERN RECOGNITION]: Clustering–Similarity measures

Abstract

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-neighbour 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.