  Most existing 3D geometry copy detection research focused on 3D watermarking,
which first embeds ``watermarks'' and then detects the added watermarks.
However, this kind of methods is non-straightforward and may be less robust to
attacks such as cropping and noise. In this paper, we focus on a fundamental
and practical research problem: judging whether a point cloud is plagiarized or
copied to another point cloud in the presence of several manipulations (e.g.,
similarity transformation, smoothing). We propose a novel method to address
this critical problem. Our key idea is first to align the two point clouds and
then calculate their similarity distance. We design three different measures to
compute the similarity. We also introduce two strategies to speed up our
method. Comprehensive experiments and comparisons demonstrate the effectiveness
and robustness of our method in estimating the similarity of two given 3D point
clouds.
