Ergun, Serkan

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12975
Name Variants
Job Title
Öğrt.Gör.
Email Address
Main Affiliation
01.01.10.01. İç Mimarlık ve Çevre Tasarımı Bölümü
Status
Former Staff
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ORCID ID
0000-0003-1373-249X
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Turkish CoHE Profile ID
Google Scholar ID
3Y_m2LIAAAAJ
WoS Researcher ID

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Scholarly Output

4

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0/0

Supervised MSc Theses

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Supervised PhD Theses

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WoS Citation Count

5

Scopus Citation Count

13

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0

WoS Citations per Publication

1.25

Scopus Citations per Publication

3.25

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0

Supervised Theses

0

JournalCount
21st International Conference in Central Europe on Computer Graphics Visualization and Computer Vision WSCG 20131
22nd IEEE Signal Processing and Communications Applications Conference (SIU)1
28th Spring Conference on Computer Graphics SCCG 20121
Contemporary Topics in Computer Graphics and Games1
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2012 - 20194
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Now showing 1 - 4 of 4
  • Thumbnail Image
    Conference Object
    Mobile GPU-Based Importance Sampling
    (IEEE, 2014) Ozkan Anil Toral; Serkan Ergun; Murat Kurt; Aydin Ozturk; Ergun, Serkan; Kurt, Murat; Töral, Özkan Ami; Öztürk, Aydin
    In this paper we developed an interactive global illumination application for mobile devices and we show that interactive filtered importance sampling is possible even with a low-power mobile graphics processing unit (GPU). Taking the limited power and the screen size of mobile devices into account we designed a user interface that allows selecting different predefined objects environments and textures. With our implementation desired materials can be visualized interactively by changing the parameters of Bidirectional Reflectance Distribution Function (BRDF).
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 5
    Real-time kd-tree based importance sampling of environment maps
    (2012) Serkan Ergun; Murat Kurt; Aydin Öztürk; Ergun, Serkan; Kurt, Murat; Ozturk, Aydn
    We present a new real-time importance sampling algorithm for environment maps. Our method is based on representing environment maps using kd-tree structures and generating samples with a single data lookup. An efficient algorithm has been developed for realtime image-based lighting applications. In this paper we compared our algorithm with Inversion method [Fishman 1996]. We show that our proposed algorithm provides compactness and speedup as compared to Inversion method. Based on a number of rendered images we have demonstrated that in a fixed time frame the proposed algorithm produces images with a lower noise than that of the Inversion method. We also demonstrate that our algorithm can successfully represent a wide range of material types. © 2013 ACM. © 2013 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Book Part
    Real-time distant light filtering using gaussian mixture model
    (Peter Lang Publishing Group, 2019) Özkan Anıl Töral; Serkan Ergun; Aydin Öztürk; Töral, Özkan Anıl; Ergun, Serkan; Öztürk, Aydın
    We propose a novel real-time rendering technique using GMM for environment lighting. We represent isotropic and anisotropic BRDF using sum of SG fitted by EM algorithm which provide an accurate approximation with acceptable number of lobes. To suppress the approximation errors we use GPU generated MIP-maps for filtering environment maps that does not require a pre-computation. MIP-mapped lookup is performed with the size of SG lobes to make filtering efficient. Based on empirical results it is shown that both isotropic and anisotropic reflectances can be handled in real-time using our technique. © 2020 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 5
    Citation - Scopus: 8
    BRDF reconstruction using compressive sensing
    (Union Agency Science Press, 2013) Nurcan Seylan; Serkan Ergun; Aydin Öztürk; Seylan, Nurcan; Ergun, Serkan; Ozturk, Aydin
    Compressive sensing is a technique for efficiently acquiring and reconstructing the data. This technique takes advantage of sparseness or compressibility of the data allowing the entire measured data to be recovered from relatively few measurements. Considering the fact that the BRDF data often can be highly sparse we propose to employ the compressive sensing technique for an efficient reconstruction. We demonstrate how to use compressive sensing technique to facilitate a fast procedure for reconstruction of large BRDF data. We have showed that the proposed technique can also be used for the data sets having some missing measurements. Using BRDF measurements of various isotropic materials we obtained high quality images at very low sampling rates both for diffuse and glossy materials. Similar results also have been obtained for the specular materials at slightly higher sampling rates. © 2013 Elsevier B.V. All rights reserved.