# A Robust Scheme for 3D Point Cloud Copy Detection

arXiv: https://arxiv.org/abs/2110.00972 · HTML: https://mlsanity.com/paper/2110.00972/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [MeshNet: Mesh Neural Network for 3D Shape Representation](https://mlsanity.com/paper/1811.11424/) (2018)
- [Triplet-Center Loss for Multi-View 3D Object Retrieval](https://mlsanity.com/paper/1803.06189/) (2018)
- [A Survey on Deep Geometry Learning: From a Representation Perspective](https://mlsanity.com/paper/2002.07995/) (2020)
- [Perturbation Robust Representations of Topological Persistence Diagrams](https://mlsanity.com/paper/1807.10400/) (2018)
- [A Robust Blind 3-D Mesh Watermarking based on Wavelet Transform for Copyright Protection](https://mlsanity.com/paper/1911.03793/) (2019)
- [A Robust Blind 3-D Mesh Watermarking technique based on SCS quantization and mesh Saliency for Copyright Protection](https://mlsanity.com/paper/1910.11211/) (2019)

## Built on by (most cited first, 1 of 1)

- [NGS-Marker: Robust Native Watermarking for 3D Gaussian Splatting](https://mlsanity.com/paper/2608.17447/) (2026)

## Abstract (arXiv)

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.