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Scalable Symmetry Detection for Urban Scenes
Author(s) -
Kerber J.,
Bokeloh M.,
Wand M.,
Seidel H.P.
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2012.03226.x
Subject(s) - computer science , point cloud , terabyte , scanner , artificial intelligence , scalability , computer vision , feature (linguistics) , pattern recognition (psychology) , cluster analysis , computer graphics (images) , database , linguistics , philosophy , operating system
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.