A survey for image based methods in construction: from images to digital twins

Koulalis, Ilias; Dourvas, Nikolaos I.; Triantafyllidis, Theocharis; Ioannidis, Konstantinos; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.1145/3549555.3549594

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…With advancements in fields such as autonomous driving [1], digital twins [2,3], cultural heritage preservation [4], and humanities research and education [5], the importance of 3D reconstruction has become increasingly paramount. There are numerous methods for 3D reconstruction, including active reconstruction techniques like LiDAR, synthetic aperture radar (SAR), Time-of-Flight (TOF), and structured light cameras, as well as passive reconstruction methods based on vision cameras [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications

Wei,

Pei,

et al. 2024

Applied Sciences

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The task of 3D reconstruction of urban targets holds pivotal importance for various applications, including autonomous driving, digital twin technology, and urban planning and development. The intricate nature of urban landscapes presents substantial challenges in attaining 3D reconstructions with high precision. In this paper, we propose a semantically aware multi-view 3D reconstruction method for urban applications which incorporates semantic information into the technical 3D reconstruction. Our research primarily focuses on two major components: sparse reconstruction and dense reconstruction. For the sparse reconstruction process, we present a semantic consistency-based error filtering approach for feature matching. To address the challenge of errors introduced by the presence of numerous dynamic objects in an urban scene, which affects the Structure-from-Motion (SfM) process, we propose a computation strategy based on dynamic–static separation to effectively eliminate mismatches. For the dense reconstruction process, we present a semantic-based Semi-Global Matching (sSGM) method. This method leverages semantic consistency to assess depth continuity, thereby enhancing the cost function during depth estimation. The improved sSGM method not only significantly enhances the accuracy of reconstructing the edges of the targets but also yields a dense point cloud containing semantic information. Through validation using architectural datasets, the proposed method was found to increase the reconstruction accuracy by 32.79% compared to the original SGM, and by 63.06% compared to the PatchMatch method. Therefore, the proposed reconstruction method holds significant potential in urban applications.

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