Abstract
In this paper, we propose a new robust feature extraction algorithm for 3D models based on principal curvature direction. Generally, the feature regions tend to be more noisy, so it demands a robust technique to handle features effectively. Because the integral invariants are robust against noise, the principal curvature information is estimated based on principal component analysis. After fuzzy filtering of the principal curvature direction, it becomes a good description of the geometric discontinuity. Compared with the curvature values, the impact of noise on the principal curvature direction is small. Therefore, feature extraction based on principal curvature direction is more robust and accurate. The experimental results show that the proposed algorithm can efficiently extract feature and distinguish noise.
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Wei J, Lou Y. Feature preserving mesh simplification using feature sensitive metric. J Comput Sci Technol, 2010, 25: 595–605
Lai Y K, Kobbelt L, Hu S M. Feature aligned quad dominant remeshing using iterative local updates. Comput Aid Des, 2010, 42: 109–117
Shen C H, Huang S S, Fu H B, et al. Adaptive partitioning of urban facades. ACM Trans Graphic, 2011, 30: 184
Miao Y W, Bosch J, Pajarola R, et al. Feature sensitive re-sampling of point set surfaces with Gaussian spheres. Sci China Inf Sci, 2012, 55: 2075–2089
Milroy M J, Bradley C, Vickers G W. Segmentation of a wraparound model using an active contour. Comput Aid Des, 1997, 29: 299–320
Ho H T, Gibbins D. A curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds. IET Comput Vis, 2009, 3: 201–212
Nair P, Cavallaro A. Region segmentation and feature point extraction on 3D faces using a point distribution model. In: IEEE International Conference on Image Processing, London, 2007. 85–88
Fang Y M, Chen J, Xia Y H. A Study of feature points extraction based on point cloud data sets and model simplification in goaf. In: International Conference on Geo-spatial Solutions for Emergency Management, Beijing, 2009. 84–87
Wu J J, Wang Q F, Huang Z D, et al. Feature point detection based on local entropy and repeatability rate. J Comput Aid Des Comput Graphic, 2005, 17: 1046–1053
Novatnack J, Nishino K. Scale-dependent 3D geometric features. In: IEEE 11th International Conference on Computer Vision, Rio de Janeiro, 2007. 1–8
Demarsin K, Vanderstraeten D, Volodine T, et al. Detection of closed sharp feature lines in point clouds for reverse engineering applications. In: Proceedings of the 4th International Conference on Geometric Modeling and Processing. Berlin/Heidelberg: Springer-Verlag, 2006. 571–577
Lai Y K, Zhou Q Y, Hu S M, et al. Robust feature classification and editing. IEEE Trans Visual Comput Graphic, 2007, 13: 34–45
Smith S M, Brady J M. Susan-a new approach to low level image processing. Int J Comput Vis, 1997, 23: 45–78
Walter N, Aubreton O, Fougerolle Y D, et al. Susan 3D operator, principal saliency segrees and directions extraction and a brief study on the robustness to noise. In: Proceedings of the 16th IEEE International Conference on Image Processing, Cairo, 2009. 3493–3496
Hamdi D. Feature Point Extraction, Auto Correspondance and Non-linear 3D Reconstruction. Project Report. Universiteit Van Amsterdam, Faculty of Science, 2006
Cristina C, Licesio J R A, Enrique C. Automatic 3D face feature points extraction with spin images. In: Proceedings of International Conference on Image Analysis and Recognition, Portugal, 2006. 317–328
Li J J, Fan H. Robust feature extraction based on principal curvature direction. In: Proceedings of the 1st International Conference on Computational Visual Media. Berlin/Heidelberg: Springer-Verlag, 2012. 186–193
Manay S, Hong B, Yezzi A, et al. Integral invariant signatures. In: Proceedings of European Conference on Computer Vision, Prague, 2004. 87–99
Yang Y L, Lai Y K, Hu S M, et al. Robust principal curvatures on multiple scales. In: Proceedings of Eurographics Symposium on Geometry Processing. Switzerland: Eurographics Association Aire-la-Ville, 2006. 223–226
Pottmann H, Wallner J, Yang Y L, et al. Principal curvatures from the integral invariant viewpoint. Comput Aid Geom Design, 2007, 24: 428–442
Pottmann H, Wallner J, Huang Q X, et al. Integral invariants for robust geometry processing. Comput Aid Geom Design, 2009, 26: 37–60
Wang Y P, Hu S M. A new watermarking method for 3D model based on integral invariant. IEEE Trans Vis Comput Graphic, 2009, 15: 285–294
Shen Y Z, Kenneth E B. Fuzzy vector median-based surface smoothing. IEEE Trans Vis Comput Graphic, 2004, 10: 252–265
Lee C H, Amitabh V, David W J. Mesh saliency. ACM Trans Graphic, 2005, 24: 659–666
Garland M, Heckbert P. Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, New York, 1997. 209–216
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Li, J., Fan, H. Curvature-direction measures for 3D feature detection. Sci. China Inf. Sci. 56, 1–9 (2013). https://doi.org/10.1007/s11432-013-4991-6
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DOI: https://doi.org/10.1007/s11432-013-4991-6