Erkan Gunpinar

dblp:73/1029 · DBLP profile ↗
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19ranked-venue papers
13as first author
10since 2021 · last 2026
0000-0002-0266-5546ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 15 · 11 first-author · 8 since 2021Databases, data management, data science and information retrieval · 3 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author
YearPublicationVenuePosition
2026 Stress-driven and user-guided Pythagorean-Hodograph print-paths for additive manufacturing
Erkan Gunpinar, Serhat Cam, Kenji Shimada
Comput. Aided Des.1
2025 Narrowing-Cascade splines for control nets that shed mesh lines
Serhat Cam, Erkan Gunpinar, Kestutis Karciauskas, Jörg Peters 0001
Comput. Graph.2
2025 Optimization of cross-derivatives for ribbon-based multi-sided surfaces
abstract
This work investigates ribbon-based multi-sided surfaces that satisfy positional and cross-derivative constraints to ensure smooth transitions with adjacent tensor-product and multi-sided surfaces. The influence of cross-derivatives, crucial to surface quality, is studied within Kato’s transfinite surface interpolation instead of control point-based methods. To enhance surface quality, the surface is optimized using cost functions based on curvature metrics. Specifically, a Gaussian curvature-based cost function is also proposed in this work. An automated optimization procedure is introduced to determine rotation angles of cross-derivatives around normals and their magnitudes along curves in Kato’s interpolation scheme. Experimental results using both primitive (e.g., spherical) and realistic examples highlight the effectiveness of the proposed approach in improving surface quality.
Erkan Gunpinar, A. Alper Tasmektepligil, Márton Vaitkus, Péter Salvi
Graph. Model.1
2024 Imbalanced generative sampling of training data for improving quality of machine learning model
Umut Can Coskun, Kemal Mert Dogan, Erkan Gunpinar
Adv. Eng. Informatics3
2024 Splines for Fast-Contracting Polyhedral Control Nets
Erkan Gunpinar, Kestutis Karciauskas, Jörg Peters 0001
Comput. Aided Des.1
2023 Fluid Flow-inspired Curvature-aware Print-paths from Hexahedral Meshes for Additive Manufacturing
Serhat Cam, Erkan Gunpinar
Comput. Aided Des.2
2023 Exploration of 3D motorcycle complexes from hexahedral meshes
abstract
Shape decompositions that are guided by a motorcycle graph endow topological properties that are relevant for many engineering applications, such as T-spline fitting, shape compression and structured mesh generation. While for the surface case this is a widely studied and well-established construction, the concept of motorcycle graph was lifted to volumes only recently (Brückler et al., 2021). Due to this recent introduction, the generation of volumetric motorcycle graphs that fulfill application dependent criteria, such as minimal number of blocks or high approximation capabilities, is still an open problem. In this article we study and compare two alternative approaches to the computation of volume shape decompositions guided by a motorcycle graph. The proposed methodologies are designed to optimize alternative application-dependent quality criteria and, overall, perform better than prior art in most of the cases.
Erkan Gunpinar, Marco Livesu, Marco Attene
Comput. Graph.1
2022 SplineLearner: Generative learning system of design constraints for models represented using B-spline surfaces
A. Alper Tasmektepligil, Erkan Gunpinar
Adv. Eng. Informatics2
2022 4 and 5-Axis additive manufacturing of parts represented using free-form 3D curves
Erkan Gunpinar, Serhat Cam
Graph. Model.1
2021 Curvature and Feature-Aware Print-Paths from Hexahedral Meshes for Additive Manufacturing
Erkan Gunpinar
Comput. Aided Des.1
2019 A generative sampling system for profile designs with shape constraints and user evaluation
Kemal Mert Dogan, Hiromasa Suzuki, Erkan Gunpinar, Myung-Soo Kim
Comput. Aided Des.3
2019 A Generative Design and Drag Coefficient Prediction System for Sedan Car Side Silhouettes based on Computational Fluid Dynamics
Erkan Gunpinar, Umut Can Coskun, Mustafa Ozsipahi, Serkan Gunpinar
Comput. Aided Des.1
2018 Sampling CAD models via an extended teaching-learning-based optimization technique
Shahroz Khan, Erkan Gunpinar
Comput. Aided Des.2
2018 A shape sampling technique via particle tracing for CAD models
Erkan Gunpinar, Serkan Gunpinar
Graph. Model.1
2014 Feature-aware partitions from the motorcycle graph
Erkan Gunpinar, Masaki Moriguchi, Hiromasa Suzuki, Yutaka Ohtake
Comput. Aided Des.1
2014 Motorcycle graph enumeration from quadrilateral meshes for reverse engineering
Erkan Gunpinar, Masaki Moriguchi, Hiromasa Suzuki, Yutaka Ohtake
Comput. Aided Des.1
2013 Generation of bi-monotone patches from quadrilateral mesh for reverse engineering
Erkan Gunpinar, Hiromasa Suzuki, Yutaka Ohtake, Masaki Moriguchi
Comput. Aided Des.1
2008 Special section: CSCWD 2006 interfacing heterogeneous PDM systems using the PLM Services
Erkan Gunpinar, Soonhung Han
Adv. Eng. Informatics1
2006 Interfacing heterogeneous PDM systems by PLM services for design collaboration
abstract
Product data management (PDM) is a popular topic for concurrent engineering to manage and access to product data in a company. There are commercial PDM softwares and companies use different PDM systems which are most suitable for their needs. For collaboration with their suppliers, companies need to interface their PDM systems with PDM systems of their suppliers. It will result faster product information flow between OEM and its suppliers. This study proposes to integrate 2 different PDM systems by PLM services, which is based on Web services. PLM services enables the data exchange through the Internet
Erkan Gunpinar, Hanmin Lee, Soonhung Han
CSCWD1