Učenje korespondencijskog ugrađivanja za stereoskopsku rekonstrukciju

undergraduate thesis

• Title (english): Learning a correspondence embedding for stereoscopic reconstruction
• Author: Antonio Ilinović
• Mentor: Siniša Šegvić (mentor)
• Committee member: Siniša Šegvić (predsjednik povjerenstva)
• Committee member: Josip Šarić (član povjerenstva)
• Committee member: Iva Sović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-07-01, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Stereoskopska rekonstrukcija postupak je kojim pomoću dviju kamera želimo rekonstruirati geometriju scene. Važan korak prije tog postupka jest odrediti korespondentne piksele. Korespondencija se može ostvariti pomoću ručno napravljenih metrika ili metrika naučenih dubokim neuronskim modelima.
U radu je opisan postupak korespondencije koji ugrađuje slike u visokodimenzionalan metrički prostor, a zatim takva ugrađivanja koristi za pronalazak korespondentnih piksela tj. mape dispariteta.
Preuzet je javno dostupan skup slika "KITTI 2015 stereo" te su iz tog skupa izvedeni podaci za učenje mreže.
Provedeni su eksperimenti za vrednovanje točnosti rekonstrukcije na ispitnom skupu te opisani su koraci za daljni razvoj.

Abstract (english)

Stereoscopic reconstruction is a procedure by which we want to reconstruct the geometry of a scene with the help of two cameras. An important step before this procedure is to determine the corresponding pixels. Correspondence can be achieved by using hand-crafted metrics or metrics learned by deep neural models. The paper describes the procedure of correspondence by which images are embedded in a high-dimensional metric space, and then such embedding are used to find the correponding pixels ie. disparity maps.
A publicly available set of images "KITTI 2015 stereo" was downloaded. Dataset for learning neural network was derived.
Experiments were conducted to evaluate the accuracy of the reconstruction at the test set. Steps for further development are described.

• Keywords: stereoskopska rekonstrukcija; korespondencijsko ugrađivanje; konvolucijska neuronska mreža
• Keywords (english): stereocopic reconstruction; correspondence embedding; convolutional neural network
• Language: croatian
• URN:NBN: urn:nbn:hr:168:492943
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-14 20:37:49