Scalable Symmetry Detection for Urban Scenes
Article first published online: 9 OCT 2012
© 2012 The Authors Computer Graphics Forum © 2012 The Eurographics Association and Blackwell Publishing Ltd.
Computer Graphics Forum
Volume 32, Issue 1, pages 3–15, February 2013
How to Cite
Kerber, J., Bokeloh, M., Wand, M. and Seidel, H.-P. (2013), Scalable Symmetry Detection for Urban Scenes. Computer Graphics Forum, 32: 3–15. doi: 10.1111/j.1467-8659.2012.03226.x
- Issue published online: 21 FEB 2013
- Article first published online: 9 OCT 2012
- symmetry detection;
- feature detection;
- large scene processing;
- 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
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.