Feature line extraction from unorganized noisy point clouds using truncated Fourier series
- PDF / 2,130,119 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 5 Downloads / 231 Views
O R I G I N A L A RT I C L E
Feature line extraction from unorganized noisy point clouds using truncated Fourier series Enkhbayar Altantsetseg · Yuta Muraki · Katsutsugu Matsuyama · Kouichi Konno
Published online: 26 April 2013 © Springer-Verlag Berlin Heidelberg 2013
Abstract The detection of feature lines is important for representing and understanding geometric features of 3D models. In this paper, we introduce a new and robust method for extracting feature lines from unorganized point clouds. We use a one-dimensional truncated Fourier series for detecting feature points. Each point and its neighbors are approximated along the principal directions by using the truncated Fourier series, and the curvature of the point is computed from the approximated curves. The Fourier coefficients are computed by Fast Fourier Transform (FFT). We apply lowpass filtering to remove noise and to compute the curvature of the point robustly. For extracting feature points from the detected potential feature points, the potential feature points are thinned using a curvature weighted Laplacianlike smoothing method. The feature lines are constructed through growing extracted points and then projected onto the original point cloud. The efficiency and robustness of our approach is illustrated by several experimental results. Keywords Feature extraction · Fourier series · FFT · Point clouds
1 Introduction Feature lines are important in representing and understanding the geometric features of a 3D model. The extraction of feature lines from point clouds is used for a variety of graphics applications, such as point cloud segmentation, surface reconstruction, shape recognition, and object illustration from point data. E. Altantsetseg () · Y. Muraki · K. Matsuyama · K. Konno Iwate University, Morioka, Japan e-mail: [email protected]
To extract feature lines from a point cloud, feature points have been detected using different techniques, such as principal component analysis [7, 10, 26], surface approximation methods [5, 17, 23], and statistical methods [15, 32]. Although a number of methods have been proposed to extract features from a point cloud, detecting feature lines on a noisy point cloud still remains a challenge. Some authors [11, 24, 26] used a multi-scale technique to compute features robustly, while others [14, 21] employed a point cloud smoothing method. In this paper, we introduce a new and robust method for extracting feature lines from unorganized noisy point clouds. The main idea of the method is as follows. Each point and its nearest neighbors are approximated along the principal directions by a truncated Fourier series, and the principal curvatures of the point are computed from the approximated curves. The Fourier coefficients are derived by Fast Fourier Transform (FFT). Low-pass filtering is applied to reduce noise and to compute the curvature of the point robustly. In addition, this paper presents a correlation between a pointwise curvature and coefficients of the Fourier transform of the point and its neighbor
Data Loading...
