Implicit surface reconstruction with total variation regularization

被引:15
|
作者
Liu, Yuan [1 ]
Song, Yanzhi [1 ]
Yang, Zhouwang [1 ]
Deng, Jiansong [1 ]
机构
[1] Univ Sci & Technol China, Sch Math Sci, Hefei, Peoples R China
关键词
Surface reconstruction; Implicit representation; Total variation; Spurious sheets; Level set; TOTAL VARIATION MINIMIZATION; ALGORITHM; SPLINES; TV;
D O I
10.1016/j.cagd.2017.02.005
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Implicit representations have been widely used for surface reconstruction on account of their capability to describe shapes that exhibit complicated geometry and topology. However, extra zero-level sets or spurious sheets usually emerge in implicit algorithms and damage the reconstruction results. In this paper, we propose a reconstruction approach that involves the total variation (TV) of the implicit representation to minimize the occurrence of spurious sheets. Proof is given to show that the recovered shape has the simplest topology with respect to the input data. By using algebraic spline functions as the implicit representation, an efficient discretization is presented together with effective algorithms to solve it. Hierarchical structures with uniform subdivisions can be applied in the framework for fitting fine details. Numerical experiments demonstrate that our algorithm achieves high quality reconstruction results while reducing the existence of extra sheets. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:135 / 153
页数:19
相关论文
共 50 条
  • [21] Fractional order total variation regularization for image super-resolution
    Ren, Zemin
    He, Chuanjiang
    Zhang, Qifeng
    SIGNAL PROCESSING, 2013, 93 (09) : 2408 - 2421
  • [22] A Framework for Moving Least Squares Method with Total Variation Minimizing Regularization
    Lee, Yeon Ju
    Lee, Sukho
    Yoon, Jungho
    JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2014, 48 (03) : 566 - 582
  • [23] DYNAMIC MRI RECONSTRUCTION VIA WEIGHTED NUCLEAR NORM AND TOTAL VARIATION REGULARIZATION
    Shi, Bao-li
    Fu, Li-wen
    Yuan, Meng
    Zhu, Hao-hui
    Pang, Zhi-feng
    INVERSE PROBLEMS AND IMAGING, 2025, 19 (03) : 539 - 559
  • [24] Efficient Compressed Sensing Reconstruction Using Group Sparse Total Variation Regularization
    Jiang, Mingfeng
    Liu, Yuan
    Xu, Wenlong
    Hu, Jie
    Wang, Yaming
    Gong, Yinglan
    Xia, Ling
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2015, 5 (05) : 907 - 917
  • [25] Logarithmic total variation regularization via preconditioned conjugate gradient method for sparse reconstruction of bioluminescence tomography
    Zhang, Gege
    Zhang, Jun
    Chen, Yi
    Du, Mengfei
    Li, Kang
    Su, Linzhi
    Yi, Huangjian
    Zhao, Fengjun
    Cao, Xin
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2024, 243
  • [26] Fast Implicit Surface Reconstruction for the Radial Basis Functions Interpolant
    Zhong, Deyun
    Zhang, Ju
    Wang, Liguan
    APPLIED SCIENCES-BASEL, 2019, 9 (24):
  • [27] An automatic regularization parameter selection algorithm in the total variation model for image deblurring
    Chen, K.
    Piccolomini, E. Loli
    Zama, F.
    NUMERICAL ALGORITHMS, 2014, 67 (01) : 73 - 92
  • [28] Hybrid model of tensor sparse representation and total variation regularization for image denoising
    Deng, Kai
    Wen, Youwei
    Li, Kexin
    Zhang, Juan
    SIGNAL PROCESSING, 2024, 217
  • [29] A regularization parameter selection model for total variation based image noise removal
    Pan, Huan
    Wen, You-Wei
    Zhu, Hui-Min
    APPLIED MATHEMATICAL MODELLING, 2019, 68 : 353 - 367
  • [30] χ2 TEST FOR TOTAL VARIATION REGULARIZATION PARAMETER SELECTION
    Mead, J.
    INVERSE PROBLEMS AND IMAGING, 2020, 14 (03) : 401 - 421