Ahmad Nasikun

PhD Student at the Computer Graphics and Visualization group at TU Delft
Room E6.420, Building 28
Office Phone: +31 (0)15 278 4698
E-mail: <A.Nasikun_AT_tudelft.nl>

About Me

I am a first year PhD student, funded by Indonesia Endowment Fund for Education (LPDP) Scholarship. My research interest lies in the area of Geometry Processing, particularly in the use of spectral methods for solving geometry-related problems.

I did my undergraduate at Department of Electrical Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia. I then continued my master at 3D Modeling and Processing laboratory of Department of Electrical Engineering and Computer Science, Seoul National University (SNU), South Korea, under the supervision of Prof. Myung-Soo Kim. My master thesis was about the 3D printing of deformable objects. This master program was supported and funded by the Korean Government Scholarship Program (KGSP).

Linkedin: ahmad nasikun


Fast Approximation of Laplace–Beltrami Eigenproblems
Ahmad Nasikun, Christopher Brandt, Klaus Hildebrandt
Computer Graphics Forum 37(5) (Presented at Symposium on Geometry Processing 2018)
Abstract:  The spectrum and eigenfunctions of the Laplace-Beltrami operator are at the heart of effective schemes for a variety of problems in geometry processing. A burden attached to these spectral methods is that they need to numerically solve a large-scale eigenvalue problem, which results in costly precomputation. In this paper, we address this problem by proposing a fast approximation algorithm for the lowest part of the spectrum of the Laplace–Beltrami operator. Our experiments indicate that the resulting spectra well-approximate reference spectra, which are computed with state-of-the-art eigensolvers. Moreover, we demonstrate that for different applications that comparable results are produced with the approximate and the reference spectra and eigenfunctions. The benefits of the proposed algorithm are that the cost for computing the approximate spectra is just a fraction of the cost required for numerically solving the eigenvalue problems, the storage requirements are reduced and evaluation times are lower. Our approach can help to substantially reduce the computational burden attached to spectral methods for geometry processing.
[preprint] [supp. document] [video 1] [video 2]