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Abstract:
In this report we present a novel method for detecting partial symmetries
in very large point clouds of 3D city scans. Unlike previous work, which
was limited to data sets of a few hundred megabytes maximum, our method
scales to very large scenes. We map the detection problem to a nearestneighbor
search in a low-dimensional feature space, followed by 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 state-of-the-art methods. In practice, it scales linearly with
the scene size and achieves a high absolute throughput, processing half a
terabyte of raw scanner data over night on a dual socket commodity PC.
