A Robust Scheme for 3D Point Cloud Copy Detection

TL;DR

This paper introduces a robust method for detecting whether one 3D point cloud is a copy or plagiarized version of another, even under various manipulations, by aligning and measuring their similarity.

Contribution

The paper presents a novel approach that aligns point clouds and computes similarity using three measures, enhancing robustness against common manipulations.

Findings

Effective in detecting copied point clouds under transformations

Robust against noise and smoothing attacks

Faster computation strategies implemented

Abstract

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.