Google Scholar [www] | YouTube [www] | Talk [ppt]

---

Chronological order: TVCG'23, TVCJ'23, TJEECS'23,
TOG'22 (SIGGRAPH Asia),CAD'22,C&G'22,
GMOD'21, CAD'21, CAVW'21,
TVCJ'20, GI'20, TJEECS'20,
3DOR'19,GMOD'19,JRTIP'19,
TOG'18 (SIGGRAPH Asia), TVCJ'18a, TVCJ'18b, GMOD'18,
CGA'17, TJEECS'17,
TOG'16 (SIGGRAPH), 3DOR'16a, 3DOR'16b, MM'16a, MM'16b,
MIA'15, C&G'15, MM'15,
CGF'14a (Pacific Graphics), CGF'14b,
CGF'13,
PAMI'12, CGF'12 (Pacific Graphics), PhD'12,
CGF'11 (Symposium on Geometry Processing),
CVPR'10, CVIU'10, CGVR'10, SIU'10,
PRL'09, CGVR'09,
MSc'06.

---

Correspondence

	Augmented Paths and Reodesics for Topologically-Stable Matching Yusuf Sahillioğlu and Devin Horsman ACM Transactions on Graphics, (Presented at SIGGRAPH Asia), Vol. 42, No. 2, 17, 2022 [pdf]  [ppt]  [dataset]  [video]	Input Isometric (or nearly isometric) shape pairs with/without topological noise Output Dense 1-to-1 correspondence b/w two shapes Idea Augment paths to be matched and use robust geodesics passing through special vertices.
	Recent Advances in Shape Correspondence Y. Sahillioğlu The Visual Computer, Vol. 36, 1705-1721, 2020 [pdf]	Input n/a Output n/a Idea Wrapping up all the interesting and brand new shape correspondence methods of the 2011-2019 interval
	A Genetic Isometric Shape Correspondence Algorithm with Adaptive Sampling Y. Sahillioğlu ACM Transactions on Graphics, (Presented at SIGGRAPH Asia), Vol. 37, No. 5, 175, 2018 [pdf]  [ppt *]  [code and exe]  [video1]  [video2]	Input Isometric (or nearly isometric) shape pairs Output Coarse correspondence b/w samples on two shapes Idea Exploring the space of permutations wisely through a genetic algorithm; improve a given map further with the adaptive sampling scheme
	SHREC’19: Shape Correspondence with Isometric and Non-Isometric Deformations R. Dyke, C. Stride, .., Y. Sahillioğlu, .., J. Yang In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2019 [pdf]  [ppt *]  [code and exe]  [video1]  [video2]	Input Isometric (or nearly isometric) shape pairs Output Coarse correspondence b/w samples on two shapes Idea Putting my TOG'18 algorithm to a test on SHREC contest: If pairs with geodesic inconsistencies and symmetric flips were excluded, my results would have improved significantly (see the penultimate paragraph of Section 4)
	SHREC'16: Matching of Deformable Shapes with Topological Noise Z. Lahner, E. Rodola, M. M. Bronstein, D. Cremers, O. Burghard, L. Cosmo, A. Dieckmann, R. Klein, Y. Sahillioğlu In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016 [pdf]  [code and exe]	Input Isometric (or nearly isometric) shape pairs with topological noise Output Coarse correspondence b/w evenly-spaced high-curvature samples on two shapes Idea Minimizing isometric distortion in probabilistic EM framework using topologically-robust biharmonic distance
	SHREC'16: Partial Matching of Deformable Shapes L. Cosmo, E. Rodola, M. M. Bronstein, A. Torsello, D. Cremers, Y. Sahillioğlu In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016 [pdf]  [code and exe]	Input Partially isometric shape pairs where the partial model is an isometric subset of the complete one Output Partial correspondence b/w two shapes Idea Introducing a distortion measure for partial matching based on raw geodesic distances
	Multiple Shape Correspondence by Dynamic Programming Yusuf Sahillioğlu and Yücel Yemez Computer Graphics Forum (Proc. PG), Vol. 33, No. 7, 121-130, 2014 [pdf]  [ppt]  [code and exe]  [video1]  [video2]	Input A collection of isometric (or nearly isometric) shapes Output Set of consistent maps b/w all shape pairs w/ the least overall isometric distortion Idea Finding optimal path of nodes on a graph where each node is a pairwise map
	Partial 3D Correspondence from Shape Extremities Yusuf Sahillioğlu and Yücel Yemez Computer Graphics Forum, Vol. 33, No. 6, 63-76, 2014 [pdf]  [code and exe]	Input Isometric (or nearly isometric or partially isometric) shape pairs; partial shapes w/ uncommon parts are welcome Output Partial or complete correspondences b/w two shapes Idea Making triplets of qualified matches produce votes based for matching in the presence of uncommon parts
	Coarse-to-Fine Isometric Shape Correspondence by Tracking Symmetric Flips Yusuf Sahillioğlu and Yücel Yemez Computer Graphics Forum, Vol. 32, No. 1, 177-189, 2013 [pdf]  [code and exe]	Input Isometric (or nearly isometric) shape pairs Output Correspondence b/w two shapes at desired resolution Idea Handling the symmetric flip problem by tracking multiple maps upto a level of detail
	Scale Normalization for Isometric Shape Matching Yusuf Sahillioğlu and Yücel Yemez Computer Graphics Forum (Proc. PG), Vol. 31, No. 7, 2233-2240, 2012 [pdf]  [ppt]  [code and exe]  [video]	Input Isometric (or nearly isometric or partially isometric) shape pairs; partial model is an isometric subset of the complete one Output Partial or complete correspondences b/w two shapes Idea Introducing a distortion measure for partial matching based on unnormalized geodesic distances; use the fact that ratios between geodesic distances are preserved under scaling and isometric deformations
	Minimum-Distortion Isometric Shape Correspondence Using EM Algorithm Yusuf Sahillioğlu and Yücel Yemez IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 34, No. 11, 2203-2215, 2012 [pdf]  [code and exe]	Input Isometric (or nearly isometric) shape pairs Output Coarse correspondence b/w evenly-spaced high-curvature samples on two shapes Idea Minimizing isometric distortion in probabilistic EM framework
	Coarse-to-Fine Combinatorial Matching For Dense Isometric Shape Correspondence Yusuf Sahillioğlu and Yücel Yemez Computer Graphics Forum (Proc. SGP), Vol. 30, No. 5, 1461-1470, 2011 [pdf]  [ppt]  [code and exe]	Input Isometric (or nearly isometric) shape pairs Output Correspondence b/w two shapes at desired resolution Idea Subdividing matched patches recursively into smaller patches to be matched
	3D Shape Correspondence by Isometry-Driven Greedy Optimization Yusuf Sahillioğlu and Yücel Yemez IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 453-458, 2010 [pdf]  [code and exe]  [video]	Input Isometric (or nearly isometric) shape pairs Output Coarse correspondence b/w evenly-spaced samples on two shapes Idea Exposing the initial spectral correspondence to a greedy refinement
	Algorithms for 3D Isometric Shape Correspondence Yusuf Sahillioğlu PhD Thesis, Computer Science Dept., Koç University, Turkey, August 2012 (Graduate studies excellence award) [pdf]  [ppt *]	Input Isometric (or nearly isometric or partially isometric) shape pairs Output Complete or partial correspondences according to the given scenario Idea PhD

Deep Learning

	A Data-Centric Unsupervised 3D Mesh Segmentation Method Talya Sivri and Yusuf Sahillioğlu The Visual Computer, 2023 [pdf]	Input 3D model in mesh representation Output Segmentation label for each mesh vertex Idea Adapt the word embedding node2vec framework to 3D data to solve the segmentation problem
	3D Point Cloud Classification with ACGAN-3D and VACWGAN-GP Onur Ergün and Yusuf Sahillioğlu Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 31, 381-395, 2023 [pdf]	Input Unoriented 3D point cloud Output Class label of the point cloud Idea Train the classifier with more data produced by generative models
	Deep generation of 3D articulated models and animations from 2D stick figures Alican Akman, Yusuf Sahillioğlu, Metin Sezgin Computers & Graphics, Vol. 109, 65-74, 2022 [pdf]	Input 2D stick figure(s) of human or horse objects Output 3D model of the stick figure or 3D animation between two stick figures Idea Exploiting variational autoencoders in order to convert 2D to static or dynamic 3D
	Generation of 3D Human Models and Animations Using Simple Sketches Alican Akman, Yusuf Sahillioğlu, Metin Sezgin Proc. Graphics Interface, 2020 [pdf]  [video1]  [video2]	Input 2D stick figure sketch (1 for modeling, 2 for animation) Output 3D model of the sketch or 3D animation between two sketches Idea Learning the mapping between the input sketch and the output 3D model through a deep net in order to synthesize new 3D content
	Deep 3D semantic scene extrapolation Ali Abbasi and Sinan Kalkan and Yusuf Sahillioğlu The Visual Computer, Vol. 35, 271-279, 2019 (online 2018) [pdf]  [code and exe]	Input Left half of a 3D scene Output Extrapolated right half of the scene Idea Learning extrapolation from a deep net with a convenient loss function

Deformation

	3D Shape Deformation Using Stick Figures Çağlar Seylan and Yusuf Sahillioğlu Computer-Aided Design, Vol. 151, 103352, 2022 [pdf]  [code and exe]  [video]	Input Source mesh and target pose described by a stick figure Output New mesh in the pose of the target stick figure Idea Deforming augmented source mesh as-rigid-as-possible in the guidance of the input stick figure.
	Part-Based Data-Driven 3D Shape Interpolation Melike Aydınlılar and Yusuf Sahillioğlu Computer-Aided Design, Vol. 136, 103027, 2021 [pdf]  [code and exe]  [video]	Input Source and target meshes and a 3D mesh database Output Interpolated in-between meshes Idea Interpolating each shape part through their own shortest paths computed over the database.
	Human Body Reconstruction from Limited Number of Points Oğuzhan Taştan and Yusuf Sahillioğlu Computer Animation and Virtual Worlds, Vol. 32, No. 5, e1995, 2021 [pdf]  [code and exe]  [video]	Input Few number of 3D points (can be obtained with touch-probe, wearables, etc. or as shown in video) and a 3D mesh database Output Reconstructed 3D surface model Idea Fetch and merge the best fits of 3D parts from the database
	Detail-Preserving Mesh Unfolding for Nonrigid Shape Retrieval Yusuf Sahillioğlu and Ladislav Kavan ACM Transactions on Graphics, (Presented at SIGGRAPH), Vol. 35, No. 3, 27, 2016 [pdf]  [ppt *]  [code and exe]  [supplementary material]  [video]	Input A tetrahedral mesh to be unfolded with preserved details Output Tetrahedral and triangular meshes in the detail-preserved unfolded pose Idea Using mass-spring system to separate each mesh vertex as far as possible while preserving original edge lengths; additional finite element constraints enhace the regularization
	Skuller: A Volumetric Shape Registration Algorithm for Modeling Skull Deformities Yusuf Sahillioğlu and Ladislav Kavan Medical Image Analysis, Vol. 23, No. 1, 15-27, 2015 [pdf]  [ppt]  [code and exe]  [video]	Input Subject-specific CT scan data and a generic skull model Output Deformed skull model conformed to the CT data Idea Using volumetric models on both sides of the registration pipeline
	A Shape Deformation Algorithm for Constrained Multidimensional Scaling Yusuf Sahillioğlu Computers & Graphics, Vol. 53, 156-165, 2015 [pdf]  [code and exe]  [video]	Input A tetrahedral mesh to be brought into its MDS pose with preserved details Output Tetrahedral and triangular meshes in the detail-preserved MDS pose Idea Guiding the deformation by MDS representation using a fast sparse linear system

Printing

	A Fabrication-Oriented Remeshing Method for Auxetic Pattern Extraction Levend Mert, Ulaş Yaman, Yusuf Sahillioğlu Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 28, 1535-1548, 2020 [pdf]  [supplementary material]  [video]	Input A quadmesh Output A fabrication-ready auxetic mesh, e.g., edges thickened Idea Remeshing the quad mesh into a hex mesh with auxetic reentrant honeycomb geometry

Reconstruction

	Coarse-to-Fine Surface Reconstruction from Silhouettes and Range Data Using Mesh Deformation Yusuf Sahillioğlu and Yücel Yemez Computer Vision and Image Understanding (CVIU), Vol. 114, 334-348, 2010 [pdf]  [code and exe]	Input Real-world object along w/ its multi-view silhouettes and range data Output Visual hull and cavity-sensitive refined mesh of the object Idea Moving the intersected triangle from the visual hull surface towards the range surface
	Triangulation-free 3D Reconstruction from LiDAR Data Yusuf Sahillioğlu International Conference on Computer Graphics & Virtual Reality (CGVR), 27-32, 2010 [pdf]  [code and exe]	Input Unorganized, noisy, and dense 3D points acquired by a LiDAR system Output Low-resolution 2-manifold triangular mesh approximating the LiDAR surface Idea Guiding the deformation w.r.t. the best-fit tangent planes spread over LiDAR points
	Shape from Silhouette Using Topology-Adaptive Mesh Deformation Yücel Yemez and Yusuf Sahillioğlu Pattern Recognition Letters, Vol. 30, 1198-1207, 2009 [pdf]  [code and exe]	Input Real-world object along w/ its multi-view silhouettes and range data Output Visual hull of the object Idea Projecting each 3D vertex of the deforming surface to the silhouettes in order to guide the topology-adaptive deformation
	A Surface Deformation Framework for 3D Shape Recovery Yusuf Sahillioğlu MS Thesis, Computer Science Dept., Koç University, Turkey, 2006 [pdf]   [ppt]	Input Multi-view silhouettes and/or range data of a real-world object to be reconstructed Output Reconstruction, i.e., visual hull and/or its refined version, as a 2-manifold triangular mesh Idea Deforming the bounding sphere in the guidance of silhouettes and then refining it further w/ scan lines of the range data

Registration

	Scale-Adaptive ICP Y. Sahillioğlu and L. Kavan Graphical Models, Vol. 116, 101113, 2021 [pdf]  [code and exe] [video]	Input Two point clouds at arbitrary scale with rigid motion difference Output Point cloud aligned with the fixed target point cloud Idea Integrating the scale directly into the least-squares ICP problem

Rendering

	Voxel transformation: scalable scene geometry discretization for global illumination B. Yalçıner and Y. Sahillioğlu Journal of Real-Time Image Processing, Vol. 17, No. 5, 1585-1596, 2020 (online 2019) [pdf]  [video]	Input A dynamic scene with many animated objects Output Realistic and fast rendering of the scene Idea Transforming pre-generated voxel data from model space to world space, which is in contrast to the common (and slower) way of voxelizing each dynamic object over each frame

Retrieval

	3D indirect shape retrieval based on hand interaction Erdem Can Irmak and Yusuf Sahillioğlu The Visual Computer, Vol. 36, 5-17, 2020 (online 2018) [pdf]	Input Hand pose captured by the Leap Motion device and a 3D model database to explore Output Set of 3D shapes that are similar to the query hand pose Idea Learning the optimal parameters that encode the way humans grab certain objects and using them for interaction-based shape retrieval
	An Evaluation of Canonical Forms for Non-Rigid 3D Shape Retrieval D. Pickup, J. Liu, X. Sun, P. Rosin, R. Martin, Z. Cheng, Z. Lian, S. Nie, L. Jin, G. Shami, Y. Sahillioğlu, L. Kavan Graphical Models, Vol. 97, 17-29, 2018 [pdf]  [code and exe]	Input Pose-independent surface or volume mesh of a 3D shape to be retrieved and a 3D model database to explore Output Set of 3D shapes that are similar to the query shape up to articulations Idea Evaluating my existing algorithms (C&G'15 and TOG'16) in a comprehensive test suite
	Sketch-based Articulated 3D Shape Retrieval Yusuf Sahillioğlu and Metin Sezgin IEEE Computer Graphics and Applications, Vol. 37, No. 6, 88-101, 2017 [pdf]  [code and exe]	Input Pose-independent 2D sketch of a 3D shape to be retrieved and a 3D model database to explore Output Set of 3D shapes that are similar to the query sketch up to articulations Idea Using our good continuation rule on sketches to facilitate articulation-invariant comparisons

Simplification

	A Partition Based Method for Spectrum-Preserving Mesh Simplification M. Yazgan and Y. Sahillioğlu IEEE Transactions on Visualization and Computer Graphics, 2023 [pdf]  [code and exe]	Input High-resolution 3D triangular mesh to be simplified Output Simplified low-resolution 3D triangular mesh Idea Preserve Laplacian spectrum explicitly by local simplifications per partition

Skeleton

	3D Skeleton Transfer for Meshes and Clouds Ç. Seylan and Y. Sahillioğlu Graphical Models, Vol. 105, 2019 [pdf]  [supplementary material]  [data]	Input Source mesh and its skeleton, and the target mesh or point cloud without skeleton Output 1D skeleton inside the target mesh or point cloud Idea Transferring the source skeleton to the target model, which can be a watertight or punctured surface mesh, or a cloud

Others

My invited METU Computer Engineering Image Lab seminar talk on Mesh Processing: [pdf] [ppt] [video]
My invited Eurasia Graphics 2018 Workshop talk on 3D Printing: [pdf] [ppt] [video]
My invited TOBB Computer Engineering Department seminar talk on Shape Matching: [pdf] [ppt]
My instructive presentation on Range Queries and Square Root Complexity: [pdf] [ppt]
My instructive presentation on Machine Learning and Graph Coloring: [pdf]
My C++ implementation of the ICP algorithm: [code and exe]
A marching algorithm for isosurface extraction from face-centered cubic lattices, Y. Sahillioğlu, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 25, 2501-2512, 2017. [pdf] [code and exe]
iAutoMotion - an Autonomous Content-based Video Retrieval Engine, L. Rossetto, I. Giangreco, C. Tanase, H. Schuldt, O. Seddati, S. Dupont. T. M. Sezgin, Y. Sahillioğlu, 22nd International Conference on Multimedia Modeling, 2016. [pdf]
IMOTION - Searching for Video Sequences using Multi-Shot Sketch Queries, L. Rossetto, I. Giangreco, S. Heller, C. Tanase, H. Schuldt, O. Seddati, S. Dupont. T. M. Sezgin, O. C. Altıok, Y. Sahillioğlu, 22nd International Conference on Multimedia Modeling, 2016. [pdf]
IMOTION: A Content-based Video Retrieval Engine, L. Rossetto, I. Giangreco, H. Schuldt, S. Dupont, O. Seddati, T. M. Sezgin, Y. Sahillioğlu, 21st International Conference on Multimedia Modeling, 2015. [pdf]
3B İzometrik Şekil Eşleme, Yusuf Sahillioğlu and Yücel Yemez, IEEE Sinyal İşleme ve Uygulamaları Kurultayı (SİU), 2010 [pdf] (Best student paper)
3D Correspondence by Breadth-First Search Frontiers, Yusuf Sahillioğlu, Int. Conference on Computer Graphics & Virtual Reality (CGVR), 203-207, 2009 [pdf]
A Surface Deformation Framework for 3D Shape Recovery, Yusuf Sahillioğlu and Yücel Yemez, Lecture notes in Computer Science (MCRS), Vol. 4105, 570-577, 2006 [pdf]
Çok Kameralı Video Görüntülerinden Yüzey Deformasyonu ile 3B Şekil Geri Çatma ve İzleme, Yusuf Sahillioğlu and Yücel Yemez, SİU, 2006 [pdf]
Hair Motion Simulation, Yusuf Sahillioğlu and Bülent Özgüç, Int. Symp. on Computer and Inf. Sciences (ISCIS), 126-135, 2004 [pdf]

---

Back to home page.