REVIEW article

Front. Comput. Sci.

Sec. Computer Vision

Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1467103

Challenges and Advancements in Image-based 3D Reconstruction of Large-Scale Urban Environments: A Review of Deep learning and Classical methods

Provisionally accepted
Alireza  Akhavi ZadeganAlireza Akhavi Zadegan1*Damien  VivetDamien Vivet2Amnir  HadachiAmnir Hadachi1
  • 1University of Tartu, Tartu, Estonia
  • 2Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Toulouse, Occitanie, France

The final, formatted version of the article will be published soon.

Over the past decade, the field of image-based 3D scene reconstruction and generation has experienced a significant transformation, driven by the integration of deep learning technologies. This shift underscores a maturing discipline characterized by rapid advancements and the introduction of numerous innovative methodologies aimed at broadening research boundaries. The specific focus of this study is on image-based 3D reconstruction techniques applicable to large-scale urban environments. This focus is motivated by the growing need for advanced urban planning and infrastructure development for smart city applications and digitalization, which requires precise and scalable modeling solutions. We employ a comprehensive classification framework that distinguishes between traditional and deep learning approaches for reconstructing urban facades, districts, and entire cityscapes. Our review methodically compares these techniques, evaluates their methodologies, highlights their distinct characteristics and performance, and identifies their limitations. Additionally, we discuss commonly utilized 3D datasets for large environments and the prevailing performance metrics in this domain.The paper concludes by outlining the current challenges faced by the field and proposes directions for future research in this swiftly evolving area.

Keywords: 3D Reconstruction, Computer Vision, large scale 3D urban model, 3D mapping, image based 3D modeling

Received: 19 Jul 2024; Accepted: 20 May 2025.

Copyright: © 2025 Akhavi Zadegan, Vivet and Hadachi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Alireza Akhavi Zadegan, University of Tartu, Tartu, Estonia

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