3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction
Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-07-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/19/15/3268 |
id |
doaj-9bc2f261e715414dbe276af9ffc828d8 |
---|---|
record_format |
Article |
spelling |
doaj-9bc2f261e715414dbe276af9ffc828d82020-11-25T01:25:42ZengMDPI AGSensors1424-82202019-07-011915326810.3390/s19153268s191532683D Convex Hull-Based Registration Method for Point Cloud Watermark ExtractionBogdan Lipuš0Borut Žalik1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, SI-2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, SI-2000 Maribor, SloveniaMost 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these issues efficiently. A point cloud registration technique is developed, based on a 3D convex hull. The scale and the initial rigid affine transformation between the watermarked and the original point cloud can be estimated in this way to obtain a coarse point cloud registration. An iterative closest point algorithm is performed after that to align the attacked watermarked point cloud to the original one completely. The watermark can then be extracted from the watermarked point cloud easily. The extensive experiments confirmed that the proposed approach resists the affine transformation, cropping, random point removal, and various combinations of these attacks. The most dangerous is an attack with noise that can be handled only to some extent. However, this issue is common to the other state-of-the-art approaches.https://www.mdpi.com/1424-8220/19/15/3268point cloud registrationmulti-scale registrationpoint cloud alignmentpoint cloud watermarkingremote sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bogdan Lipuš Borut Žalik |
spellingShingle |
Bogdan Lipuš Borut Žalik 3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction Sensors point cloud registration multi-scale registration point cloud alignment point cloud watermarking remote sensing |
author_facet |
Bogdan Lipuš Borut Žalik |
author_sort |
Bogdan Lipuš |
title |
3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction |
title_short |
3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction |
title_full |
3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction |
title_fullStr |
3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction |
title_full_unstemmed |
3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction |
title_sort |
3d convex hull-based registration method for point cloud watermark extraction |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-07-01 |
description |
Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these issues efficiently. A point cloud registration technique is developed, based on a 3D convex hull. The scale and the initial rigid affine transformation between the watermarked and the original point cloud can be estimated in this way to obtain a coarse point cloud registration. An iterative closest point algorithm is performed after that to align the attacked watermarked point cloud to the original one completely. The watermark can then be extracted from the watermarked point cloud easily. The extensive experiments confirmed that the proposed approach resists the affine transformation, cropping, random point removal, and various combinations of these attacks. The most dangerous is an attack with noise that can be handled only to some extent. However, this issue is common to the other state-of-the-art approaches. |
topic |
point cloud registration multi-scale registration point cloud alignment point cloud watermarking remote sensing |
url |
https://www.mdpi.com/1424-8220/19/15/3268 |
work_keys_str_mv |
AT bogdanlipus 3dconvexhullbasedregistrationmethodforpointcloudwatermarkextraction AT borutzalik 3dconvexhullbasedregistrationmethodforpointcloudwatermarkextraction |
_version_ |
1725112409355452416 |