Scalable Symmetry Detection for Urban Scenes
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Date
2013
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The Eurographics Association and Blackwell Publishing Ltd.
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-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.
Description
@article{:10.1111/j.1467-8659.2012.03226.x,
journal = {Computer Graphics Forum},
title = {{Scalable Symmetry Detection for Urban Scenes}},
author = {Kerber, J. and Bokeloh, M. and Wand, M. and Seidel, H.-P.},
year = {2013},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.},
ISSN = {1467-8659},
DOI = {/10.1111/j.1467-8659.2012.03226.x}
}