Training inverse BRDF with incomplete data for 3D reconstruction through photometric stereo

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2014

Öğrenci: SAMET KİLECİ

Danışman: UĞUR HALICI

Özet:

In this thesis, missing data phenomena seen in a photometric stereo model is dealt with machine learning approaches. Photometric stereo model takes input images acquired with different illuminating conditions and predicts surface properties of an object. Specular regions appear on the images due to reflection for certain angle of light and camera and shadow regions appear because of surface structure of the object and light angle. Since specular and shadow regions degrade the performance of the photometric stereo, in this thesis these regions are handled as regions with missing data by using machine learning approaches. Neural network ensembles are implemented to handle the specular and shadow regions. Networks are trained with full range of BRDF data by omitting the values which have irrelevant intensity information. Once they are trained, test data is assigned to their adequate network by considering the location of missing data. This feature selection and ensemble structure of the networks significantly decrease the effect of missing data. Finally, outputs of each networks are used in the 3D reconstruction, surface structure of the object is successfully obtained with proposed photometric stereo model even in the presence of incomplete data.