![]() ![]() Multi-scale registration point cloud alignment point cloud registration point cloud watermarking remote sensing. However, this issue is common to the other state-of-the-art approaches. The most dangerous is an attack with noise that can be handled only to some extent. The extensive experiments confirmed that the proposed approach resists the affine transformation, cropping, random point removal, and various combinations of these attacks. The watermark can then be extracted from the watermarked point cloud easily. A convex hull point co-ordinate file is then created using writeconvexhullxy() ''' if os. NOTE: When this option is activated, the qhull. brief If set to true, the qhull library is called to compute the total area and volume of the convex hull. ![]() param out points the resultant points lying on the convex hull. brief Compute a convex hull for all points given. An iterative closest point algorithm is performed after that to align the attacked watermarked point cloud to the original one completely. import os import sys import numpy as np from scipy import spatial def xyconvexhull (inputxyfile): ''' Calculates the convex hull of a given xy data set returning the indicies of the convex hull points in the input data set. The Vertices structure contains an array of point indices. 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. A point cloud registration technique is developed, based on a 3D convex hull. In this work, an alternative approach is proposed that solves these issues efficiently. Unfortunately, they fail in the case of cropping and random point removal attacks. Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. ![]()
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