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Building 3D models of objects from color images with next best view planning

Kristian Žarn (2019) Building 3D models of objects from color images with next best view planning. MSc thesis.

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    Reconstruction of geometry from RGB images is one of the classic computer vision problems. The quality of the produced 3D model heavily depends on the input images. Manual image acquisition can be a lengthy process with which we want to attain the desired accuracy and completeness of the model. A user that has no feedback about the suitability of the images during the acquisition process can have difficulties complying with assumptions of the algorithms, which can result in an unsuccessful reconstruction. Additional image acquisition can be expensive or even impossible. In this work, we focus on development of a system and software that support the entire reconstruction process. The user gets online information about the adequacy of every captured image and an estimate of quality for the current 3D model. We also present a novel method for next best view planning, that systematically improves the quality of reconstruction. The method is based on a new quality measure. We show that the linear correlation coefficient between our measure and accuracy is better than that of the existing measure. We also show that the reconstruction obtained by the next best view planning is comparable and in some cases better than reconstruction with evenly spaced camera configuration in the shape of a hemisphere.

    Item Type: Thesis (MSc thesis)
    Keywords: computer vision, 3D reconstruction, structure from motion, next best view
    Number of Pages: 98
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Danijel Skočaj296Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1538284995)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 4442
    Date Deposited: 10 Jul 2019 13:38
    Last Modified: 26 Jul 2019 13:02
    URI: http://eprints.fri.uni-lj.si/id/eprint/4442

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