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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Guided Next Best View for 3D Reconstruction of Large Complex Structures

Almadhoun, Randa, Abduldayem, Abdullah, Taha, Tarek, Seneviratne, Lakmal and Zweiri, Yahya (2019) Guided Next Best View for 3D Reconstruction of Large Complex Structures. Remote Sensing, 11 (20). p. 2440. ISSN 2072-4292

Item Type: Article


In this paper, a Next Best View (NBV) approach with a profiling stage and a novel utility function for 3D reconstruction using an Unmanned Aerial Vehicle (UAV) is proposed. The proposed approach performs an initial scan in order to build a rough model of the structure that is later used to improve coverage completeness and reduce flight time. Then, a more thorough NBV process is initiated, utilizing the rough model in order to create a dense 3D reconstruction of the structure of interest. The proposed approach exploits the reflectional symmetry feature if it exists in the initial scan of the structure. The proposed NBV approach is implemented with a novel utility function, which consists of four main components: information theory, model density, traveled distance, and predictive measures based on symmetries in the structure. This system outperforms classic information gain approaches with a higher density, entropy reduction and coverage completeness. Simulated and real experiments were conducted and the results show the effectiveness and applicability of the proposed approach.

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Related URLs:
Depositing User: Randa Almadhoun


Item ID: 16461
Identification Number:
ISSN: 2072-4292
Official URL:

Users with ORCIDS

ORCID for Randa Almadhoun: ORCID iD
ORCID for Tarek Taha: ORCID iD
ORCID for Lakmal Seneviratne: ORCID iD
ORCID for Yahya Zweiri: ORCID iD

Catalogue record

Date Deposited: 20 Nov 2023 10:42
Last Modified: 20 Nov 2023 10:42


Author: Randa Almadhoun ORCID iD
Author: Tarek Taha ORCID iD
Author: Lakmal Seneviratne ORCID iD
Author: Yahya Zweiri ORCID iD
Author: Abdullah Abduldayem

University Divisions

Faculty of Technology


Computing > Artificial Intelligence

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