Yusuf Sahillioğlu

E-mail

ysahilli@metu.edu.tr

Department

Department of Computer Engineering

ORCID

0000-0000-0000-0000

Scopus Author ID

56595665000

Web of Science Researcher ID

X-0000-0000

3D geometric kernel computation in polygon mesh structures Asiler, Merve; Sahillioğlu, Yusuf (2024-08-01) This paper introduces a novel approach to compute the geometric kernel of a polygon mesh embedded in 3D. The geometric kernel defines the set of points inside or on the shape's boundary, ensuring visibility of the entire s...
A Partition Based Method for Spectrum-Preserving Mesh Simplification Yazgan, Misranur; Sahillioğlu, Yusuf (2024-01-01) The majority of the simplification methods focus on preserving the appearance of the mesh, ignoring the spectral properties of the differential operators derived from the mesh. The spectrum of the Laplace-Beltrami operator...
A surface-based approach for 3D approximate convex decomposition Kuşkonmaz, Onat Zeybek; Sahillioğlu, Yusuf (2024-01-01) Approximate convex decomposition simplifies complex shapes into manageable convex components. In this work, we propose a novel surface-based method that achieves efficient computation times and sufficiently convex results ...
KerGen: A Kernel Computation Algorithm for 3D Polygon Meshes Asiler, Merve; Sahillioğlu, Yusuf (2024-01-01) We compute the kernel of a shape embedded in 3D as a polygon mesh, which is defined as the set of all points that have a clear line of sight to every point of the mesh. The KerGen algorithm, short for Kernel Generation, em...
Augmented Paths and Reodesics for Topologically-Stable Matching Sahillioğlu, Yusuf; Horsman, Devin (2023-04-01) We propose a fully-automatic method that computes from scratch pointto-point dense correspondences between isometric shapes under topological noise. While relying on pairwise distance preservation constraints is common and...
3D point cloud classification with ACGAN-3D and VACWGAN-GP Ergun, Onur; Sahillioğlu, Yusuf (2023-01-01) Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors. In this wor...
A data-centric unsupervised 3D mesh segmentation method Sivri, Talya Tümer; Sahillioğlu, Yusuf (2023-01-01) In this paper, a novel data-centric approach is proposed for solving the 3D mesh segmentation problem. The method uses node2vec, a semi-supervised learning algorithm, to create vector embedding representations for each nod...
Deep generation of 3D articulated models and animations from 2D stick figures Akman, Alican; Sahillioğlu, Yusuf; Sezgin, T. Metin (2022-12-01) Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the ...
3D Shape Deformation Using Stick Figures Seylan, Çağlar; Sahillioğlu, Yusuf (2022-10-01) Obtaining new poses of an articulated character is a critical task in computer graphics. We address this issue with a new shape deformation approach consisting of two phases enabling the user to express the new pose as a s...
Part-based data-driven 3D shape interpolation Aydinlilar, Melike; Sahillioğlu, Yusuf (2021-07-01) An active problem in digital geometry processing is shape interpolation which aims to generate a continuous sequence of in-betweens for a given source and target shape. Unlike traditional approaches that interpolate source...

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