“Nonlinear spectral geometry processing via the TV transform” by Fumero, Möller and Rodolà – ACM SIGGRAPH HISTORY ARCHIVES

“Nonlinear spectral geometry processing via the TV transform” by Fumero, Möller and Rodolà

  • 2020 SA Technical Papers_Fumero_Nonlinear spectral geometry processing via the TV transform

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Title:

    Nonlinear spectral geometry processing via the TV transform

Session/Category Title:   Digital Geometry Processing


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Abstract:


    We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such an operator admits a generalized notion of spectral decomposition, yielding a convenient multiscale representation akin to Laplacian-based methods, while at the same time avoiding undesirable over-smoothing effects typical of such techniques. Our approach entails accurate, detail-preserving decomposition and manipulation of 3D shape geometry while taking an especially intuitive form: non-local semantic details are well separated into different bands, which can then be filtered and re-synthesized with a straightforward linear step. Our computational framework is flexible, can be applied to a variety of signals, and is easily adapted to different geometry representations, including triangle meshes and point clouds. We showcase our method through multiple applications in graphics, ranging from surface and signal denoising to enhancement, detail transfer, and cubic stylization.

References:


    1. Yonathan Aflalo, Haim Brezis, and Ron Kimmel. 2014. On the Optimality of Shape and Data Representation in the Spectral Domain. SIAM J. Imaging Sciences 8 (2014).Google Scholar
    2. Francois Alter, Vicente Caselles, and Antonin Chambolle. 2005. A characterization of convex calibrable sets in Rn. Math. Ann. 332 (2005), 239–366.Google ScholarCross Ref
    3. Fuensanta Andreu, Vicente Caselles, Jesú Díaz, and José Mazón. 2002. Some Qualitative Properties for the Total Variation Flow. J. Funct. Anal. 188, 2 (2002), 516 — 547.Google ScholarCross Ref
    4. Mathieu Andreux, Emanuele Rodolà, Mathieu Aubry, and Daniel Cremers. 2015. Anisotropic Laplace-Beltrami Operators for Shape Analysis. In Computer Vision – ECCV 2014 Workshops. Springer International Publishing, 299–312.Google Scholar
    5. Chandrajit L. Bajaj and Guoliang Xu. 2003. Anisotropic Diffusion of Surfaces and Functions on Surfaces. ACM TOG 22, 1 (Jan. 2003), 4–32.Google ScholarDigital Library
    6. Giovanni Bellettini, Vicente Caselles, and Matteo Novaga. 2002. The Total Variation Flow in RN. Journal of Differential Equations 184, 2 (2002), 475 — 525.Google ScholarCross Ref
    7. Matania Ben-Artzi and Philippe LeFloch. 2006. Well-Posedness theory for geometry compatible hyperbolic conservation laws on manifolds. Annales de l’Institut Henri Poincare (C) Non Linear Analysis 24 (12 2006), 989–1008.Google Scholar
    8. Martin Benning and Martin Burger. 2013. Ground states and singular vectors of convex variational regularization methods. MAA 20, 4 (2013), 295–334.Google Scholar
    9. Alexander I. Bobenko and Peter Schröder. 2005. Discrete Willmore Flow. In Proc. SGP. Eurographics Association, 101-es.Google Scholar
    10. Nicolas Bonneel, David Coeurjolly, Pierre Gueth, and Jacques-Olivier Lachaud. 2018. Mumford-Shah Mesh Processing using the Ambrosio-Tortorelli Functional. Comput Graph Forum 37, 10 (Oct. 2018).Google ScholarCross Ref
    11. Mario Botsch, Leif Kobbelt, Mark Pauly, Pierre Alliez, and Bruno Lévy. 2010. Polygon mesh processing. CRC press.Google Scholar
    12. Christopher Brandt, Leonardo Scandolo, Elmar Eisemann, and Klaus Hildebrandt. 2017. Spectral processing of tangential vector fields. Comput Graph Forum 36, 6 (2017), 338–353.Google ScholarDigital Library
    13. Thomas Bühler and Matthias Hein. 2009. Spectral Clustering Based on the Graph P-Laplacian. In Proc. ICML. 81–88.Google Scholar
    14. Martin Burger, Guy Gilboa, Michael Moeller, Lina Eckardt, and Daniel Cremers. 2016. Spectral decompositions using one-homogeneous functionals. SIAM Journal on Imaging Sciences 9, 3 (2016), 1374–1408.Google ScholarCross Ref
    15. Antonin Chambolle, Vicent Caselles, Daniel Cremers, Matteo Novaga, and Thomas Pock. 2010. An introduction to total variation for image analysis. Theoretical foundations and numerical methods for sparse recovery 9, 263–340 (2010), 227.Google Scholar
    16. Antonin Chambolle and Thomas Pock. 2011. A first-order primal-dual algorithm for convex problems with applications to imaging. JMIV 40, 1 (2011), 120–145.Google ScholarDigital Library
    17. Ming Chuang, Szymon Rusinkiewicz, and Michael Kazhdan. 2016. Gradient-Domain Processing of Meshes. J. Comput. Grap. Tech. 5, 4 (Dec. 2016), 44–55.Google Scholar
    18. Ulrich Clarenz, Udo Diewald, and Martin Rumpf. 2000. Anisotropic Geometric Diffusion in Surface Processing. In Proc. VIS. IEEE, 397–405.Google ScholarCross Ref
    19. Ido Cohen and Guy Gilboa. 2019. Introducing the p-Laplacian Spectra. (2019).Google Scholar
    20. Dorin Comaniciu and Peter Meer. 2002. Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE PAMI 24, 5 (May 2002), 603–619.Google ScholarDigital Library
    21. Keenan Crane, Ulrich Pinkall, and Peter Schröder. 2013. Robust Fairing via Conformal Curvature Flow. ACM TOG 32 (2013).Google Scholar
    22. Mathieu Desbrun, Mark Meyer, Peter Schröder, and Alan H. Barr. 1999. Implicit Fairing of Irregular Meshes Using Diffusion and Curvature Flow. In Proc. SIGGRAPH. ACM Press, 317–324.Google Scholar
    23. Mathieu Desbrun, Mark Meyer, Peter Schröder, and Alan H. Barr. 2000. Anisotropic Feature-Preserving Denoising of Height Fields and Bivariate Data. In Proc. Graphics Interface. 145–152.Google Scholar
    24. Julie Digne and Carlo de Franchis. 2017. The Bilateral Filter for Point Clouds. Image Processing On Line 7 (10 2017), 278–287.Google Scholar
    25. Joan Duran, Michael Moeller, Catalina Sbert, and Daniel Cremers. 2016. Collaborative Total Variation: A General Framework for Vectorial TV Models. SIAM Journal on Imaging Sciences 9, 1 (2016), 116–151.Google ScholarCross Ref
    26. Shachar Fleishman, Iddo Drori, and Daniel Cohen-Or. 2003. Bilateral Mesh Denoising. In Proc. SIGGRAPH. 950–953.Google ScholarDigital Library
    27. Wendell H. Fleming and Raymond Rishel. 1960. An integral formula for total gradient variation. Archiv der Mathematik 11, 1 (01 Dec 1960), 218–222.Google Scholar
    28. Guy Gilboa. 2013. A Spectral Approach to Total Variation. In Scale Space and Variational Methods in Computer Vision. Springer Berlin Heidelberg, 36–47.Google Scholar
    29. Guy Gilboa. 2014. A Total Variation Spectral Framework for Scale and Texture Analysis. SIAM Journal on Imaging Sciences 7, 4 (2014), 1937–1961.Google ScholarCross Ref
    30. Guy Gilboa, Michael Möller, and Martin Burger. 2015. Nonlinear Spectral Analysis via One-Homogeneous Functionals: Overview and Future Prospects. Journal of Mathematical Imaging and Vision 56 (2015), 300–319.Google ScholarDigital Library
    31. Lei He and Scott Schaefer. 2013. Mesh denoising via L 0 minimization. ACM TOG 32, 4 (July 2013), 1.Google ScholarDigital Library
    32. Jin Huang, Tengfei Jiang, Zeyun Shi, Yiying Tong, Hujun Bao, and Mathieu Desbrun. 2014. ℓ/1-based construction of polycube maps from complex shapes. ACM TOG 33, 3 (2014), 1–11.Google ScholarDigital Library
    33. Thouis R. Jones, Frédo Durand, and Mathieu Desbrun. 2003. Non-Iterative, Feature-Preserving Mesh Smoothing. ACM TOG 22, 3 (July 2003), 943–949.Google ScholarDigital Library
    34. Michael Kazhdan, Jake Solomon, and Mirela Ben-Chen. 2012. Can mean-curvature flow be modified to be non-singular? Comput Graph Forum 31, 5 (2012), 1745–1754.Google ScholarDigital Library
    35. Bruno Lévy. 2006. Laplace-beltrami eigenfunctions towards an algorithm that” understands” geometry. In Proc. SMI. IEEE, 13–13.Google ScholarDigital Library
    36. Peter Lindqvist. 2006. Notes on the p-Laplace equation. Report. University of Jyväskylä Department of Mathematics and Statistics 102 (01 2006).Google Scholar
    37. Hsueh-Ti Derek Liu and Alec Jacobson. 2019. Cubic Stylization. ACM TOG 38, 6 (Nov. 2019).Google Scholar
    38. Hsueh-Ti Derek Liu, Alec Jacobson, and Keenan Crane. 2017. A Dirac Operator for Extrinsic Shape Analysis. Comput. Graph. Forum 36, 5 (Aug. 2017), 139–149.Google Scholar
    39. Simone Melzi, Jing Ren, Emanuele Rodolà, Abhishek Sharma, Peter Wonka, and Maks Ovsjanikov. 2019. ZoomOut: Spectral Upsampling for Efficient Shape Correspondence. ACM TOG 38, 6 (Nov. 2019).Google ScholarDigital Library
    40. Niloy J Mitra and An Nguyen. 2003. Estimating surface normals in noisy point cloud data. In Proc. SoCG. 322–328.Google ScholarDigital Library
    41. Michael Moeller, Julia Diebold, Guy Gilboa, and Daniel Cremers. 2015. Learning nonlinear spectral filters for color image reconstruction. In Proc. ICCV. 289–297.Google ScholarDigital Library
    42. Thomas Neumann, Kiran Varanasi, Christian Theobalt, Marcus Magnor, and Markus Wacker. 2014. Compressed Manifold Modes for Mesh Processing. Comput Graph Forum 33, 5 (2014), 35–44.Google ScholarDigital Library
    43. Stanley Osher and Leonid I. Rudin. 1990. Feature-Oriented Image Enhancement Using Shock Filters. SIAM J. Numer. Anal. 27, 4 (Aug. 1990), 919–940.Google ScholarDigital Library
    44. Maks Ovsjanikov, Mirela Ben-Chen, Justin Solomon, Adrian Butscher, and Leonidas Guibas. 2012. Functional maps: a flexible representation of maps between shapes. ACM TOG 31, 4 (2012), 30.Google ScholarDigital Library
    45. Fabian Prada and Michael Kazhdan. 2015. Unconditionally Stable Shock Filters for Image and Geometry Processing. Comput Graph Forum 34, 5 (2015), 201–210.Google ScholarDigital Library
    46. Guodong Rong, Yan Cao, and Xiaohu Guo. 2008. Spectral mesh deformation. The Visual Computer 24, 7–9 (2008), 787–796.Google ScholarDigital Library
    47. Leonid I. Rudin, Stanley Osher, and Emad Fatemi. 1992. Nonlinear Total Variation Based Noise Removal Algorithms. Phys. D 60, 1–4 (Nov. 1992), 259–268.Google Scholar
    48. Nicholas Sharp, Yousuf Soliman, and Keenan Crane. 2019. Navigating intrinsic triangulations. ACM TOG 38, 4 (2019), 55.Google ScholarDigital Library
    49. Justin Solomon, Keenan Crane, Adrian Butscher, and Chris Wojtan. 2014. A General Framework for Bilateral and Mean Shift Filtering. CoRR abs/1405.4734 (2014).Google Scholar
    50. Olga Sorkine. 2005. Laplacian Mesh Processing. In Eurographics 2005 – State of the Art Reports. 53–70.Google Scholar
    51. Olga Sorkine, Daniel Cohen-Or, Yaron Lipman, Marc Alexa, Christian Rössl, and HansPeter Seidel. 2004. Laplacian Surface Editing. In Proc. SGP. 175–184.Google ScholarDigital Library
    52. Gabriele. Steidl, Joachim. Weickert, Thomas. Brox, Pavel. Mrázek, and Martin. Welk. 2004. On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and SIDEs. SIAM J. Numer. Anal. 42, 2 (2004), 686–713.Google ScholarDigital Library
    53. Xianfang Sun, Paul L. Rosin, Ralph Martin, and Frank Langbein. 2007. Fast and Effective Feature-Preserving Mesh Denoising. IEEE TVCG 13, 5 (2007), 925–938.Google Scholar
    54. Tolga Tasdizen, Ross Whitaker, Paul Burchard, and Stanley Osher. 2002. Geometric surface smoothing via anisotropic diffusion of normals. In Proc. VIS. 125–132.Google ScholarCross Ref
    55. Gabriel Taubin. 1995. A Signal Processing Approach to Fair Surface Design. In Proc. SIGGRAPH. ACM, 351–358.Google ScholarDigital Library
    56. Carlo Tomasi and Roberto Manduchi. 1998. Bilateral filtering for gray and color images. In Proc. ICCV. Narosa Publishing House, 839–846.Google Scholar
    57. Weihua Tong and Xue-Cheng Tai. 2016. A Variational Approach for Detecting Feature Lines on Meshes. Journal of Computational Mathematics 34 (01 2016), 87–112.Google Scholar
    58. Peng-Shuai Wang, Xiao-Ming Fu, Yang Liu, Xin Tong, Shi-Lin Liu, and Baining Guo. 2015. Rolling Guidance Normal Filter for Geometric Processing. ACM TOG 34, 6 (2015).Google Scholar
    59. Yu Wang and Justin Solomon. 2019. Chapter 2 – Intrinsic and extrinsic operators for shape analysis. In Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2. Handbook of Numerical Analysis, Vol. 20. Elsevier, 41 — 115.Google Scholar
    60. Li Xu, Cewu Lu, Yi Xu, and Jiaya Jia. 2011. Image smoothing via L 0 gradient minimization. In Proc. SIGGRAPH Asia. ACM Press, 1.Google Scholar
    61. Wotao Yin and Stanley Osher. 2013. Error Forgetting of Bregman Iteration. J. Sci. Comput. 54 (02 2013).Google Scholar
    62. Hao Zhang, Oliver van Kaick, and Ramsay Dyer. 2010. Spectral Mesh Processing. Comput Graph Forum 29, 6 (2010), 1865–1894.Google ScholarCross Ref
    63. Huayan Zhang, Chunlin Wu, Juyong Zhang, and Jiansong Deng. 2015. Variational Mesh Denoising Using Total Variation and Piecewise Constant Function Space. IEEE Transactions on Visualization and Computer Graphics 21, 7 (2015), 873–886.Google ScholarDigital Library
    64. Qi Zhang, Xiaoyong Shen, Li Xu, and Jiaya Jia. 2014. Rolling Guidance Filter. In Computer Vision – ECCV 2014. Springer International Publishing.Google ScholarCross Ref
    65. Wangyu Zhang, Bailin Deng, Juyong Zhang, Sofien Bouaziz, and Ligang Liu. 2015. Guided Mesh Normal Filtering. Comput Graph Forum 34, 7 (2015), 23–34.Google ScholarDigital Library
    66. Youji Zheng, Hongbo Fu, Oscar Au, and Chiew-Lan Tai. 2011. Bilateral Normal Filtering for Mesh Denoising. IEEE TVCG 17, 10 (2011), 1521–1530.Google Scholar
    67. Saishang Zhong, Zhong Xie, Weina Wang, Zheng Liu, and Ligang Liu. 2018. Mesh denoising via total variation and weighted Laplacian regularizations. Computer Animation and Virtual Worlds 29, 3–4 (2018), e1827.Google ScholarCross Ref


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