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master's thesis
Samonadzirano učenje stereoskopske rekonstrukcije
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:710315
Bratulić, Jelena
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Bratulić, J. (2023). Samonadzirano učenje stereoskopske rekonstrukcije (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:710315

Bratulić, Jelena. "Samonadzirano učenje stereoskopske rekonstrukcije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:710315

Bratulić, Jelena. "Samonadzirano učenje stereoskopske rekonstrukcije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:710315

Bratulić, J. (2023). 'Samonadzirano učenje stereoskopske rekonstrukcije', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:710315

Bratulić J. Samonadzirano učenje stereoskopske rekonstrukcije [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 May 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:710315

J. Bratulić, "Samonadzirano učenje stereoskopske rekonstrukcije", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:710315

Metadata
TitleSamonadzirano učenje stereoskopske rekonstrukcije
Title (english)Self-supervised learning of stereoscopic reconstruction
AuthorJelena Bratulić
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberVladimir Čeperić (član povjerenstva)
Committee memberŽeljka Mihajlović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Stereoskopska rekonstrukcija je važno i atraktivno područje računalnog vida čije primjene pronalazimo u različitim industrijama, robotici, medicini i autonomnim vozilima. Iako nadzirane metode bazirane na dubokom učenju postižu odlične rezultate, često je vrlo teško i skupo prikupiti stvarne oznake dispariteta ili dubine pa se u posljednje vrijeme naglasak stavlja na samonadzirane metode. U okviru ovog rada, proučili smo nekoliko popularnih samonadziranih metoda za stereoskopsku rekonstrukciju te smo predstavili novu metodu koju nazivamo iterativno pseudolabeliranje ansamabliranjem (IPA). U radu smo detaljno opisali našu metodu, postupke učenja i validiranja hiperparametara. Detaljni eksperimenti koristeći model RAFT Stereo pokazuju iznimno kompetitivne rezultate na skupu podataka KITTI. U radu smo naveli i moguće pravce budućeg istraživanja usmjerene u poboljšanje metode ansambliranja te uključivanje znanja semantičke segmentacije.
Abstract (english)
Stereoscopic reconstruction is a significant and appealing field in computer vision, with wide-ranging applications in industries such as robotics, medicine, and autonomous vehicles. While supervised approaches leveraging deep learning have demonstrated remarkable outcomes, acquiring disparity or depth labels often poses considerable challenges and expenses. As a result, self-supervised methods have recently garnered significant attention. In this Master Thesis, we thoroughly examine several popular self-supervised techniques for stereoscopic reconstruction and introduce a novel approach termed iterative pseudolabeling by ensambling (IPE). Our research delves into the intricate workings of IPE, including the methodology, the process for learning, and the validation of hyperparameters. Through comprehensive experimentation utilizing the RAFT Stereo model, our proposed method showcases exceptionally competitive results on the KITTI dataset. We also highlight potential avenues for future research, focusing on enhancing the ensemble method and incorporating semantic segmentation knowledge. These directions offer opportunities to further advance the effectiveness and applicability of the proposed approach.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:710315
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-10-11 08:32:04