Semi-automated luminance map re-projection via high dynamic range imaging and indoor space 3-D reconstruction

Kim, Michael; Tzempelikos, Athanasios

doi:10.1016/j.autcon.2021.103812

Cited by 12 publications

(2 citation statements)

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“…Structural parameters are the basic parameters for constructing a measurement system and are the main factors that affect the reconstruction accuracy and the size of the common field of view. The structural parameters are both independent of each other and mutually affect and restrict each other ( Kim & Tzempelikos, 2021 ). As shown in Fig.…”

Section: Error Analysis Model For the Binocular 3d Reconstructionmentioning

confidence: 99%

The analysis of the structural parameter influences on measurement errors in a binocular 3D reconstruction system: a portable 3D system

Sha,

Zhang,

Bai

et al. 2023

PeerJ Computer Science

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This study used an analytical model to investigate the factors that affect the reconstruction accuracy composed of the baseline length, lens focal length, the angle between the optical axis and baseline, and the field of the view angle. Firstly, the theoretical expressions of the above factors and measurement errors are derived based on the binocular three-dimensional reconstruction model. Then, the structural parameters’ impact on the error propagation coefficient is analyzed and simulated using MATLAB software. The results show that structural parameters significantly impact the error propagation coefficient, and the reasonable range of structural parameters is pointed out. When the angle between the optical axis of the binocular camera and the baseline is between 30° and 55°, the ratio of the baseline length to the focal length can be reasonably reduced. In addition, using the field angle of the view that does not exceed 20° could reduce the error propagation coefficient. While the angle between the binocular optical axis and the baseline is between 40° and 50°, the reconstruction result has the highest accuracy, changing the angle out of this range will lead to an increase in the reconstruction error. The angle between the binocular optical axis and the baseline changes 30° through 60° leads to the error propagation coefficient being in a lower range. Finally, experimental verification and simulation results show that selecting reasonable structural parameters could significantly reduce measurement errors. This study proposes a model that constructs a binocular three-dimensional reconstruction system with high precision. A portable three-dimensional reconstruction system is built in the article.

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Section: Error Analysis Model For the Binocular 3d Reconstructionmentioning

confidence: 99%

The analysis of the structural parameter influences on measurement errors in a binocular 3D reconstruction system: a portable 3D system

Sha,

Zhang,

Bai

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…It is crucial in applications such as indoor navigation (Abou Diakité & Zlatanova, 2016; Youli Ding et al., 2018) and emergency management (Ahmed et al., 2015). Further, it aids in the dynamic generation and revision of building information modeling (Kang et al., 2020; Kim & Tzempelikos, 2021), architectural design (Cui et al., 2019), safety regulation compliance (Nikoohemat et al., 2020), and historical building preservation (Brunetaud et al., 2012). As the automation requirements push the industry, the automatic reconstruction of the indoor environment is rapidly emerging (Youli Ding et al., 2018; Pintore et al., 2020), leveraging advancements of technologies, from manual modeling, active methods, passive methods to learning‐based methods.…”

Section: Introductionmentioning

confidence: 99%

Multi‐view stereo for weakly textured indoor 3D reconstruction

Wang,

Gan

2024

Computer aided Civil Eng

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A 3D reconstruction enables an effective geometric representation to support various applications. Recently, learning‐based multi‐view stereo (MVS) algorithms have emerged, replacing conventional hand‐crafted features with convolutional neural network‐encoded deep representation to reduce feature matching ambiguity, leading to a more complete scene recovery from imagery data. However, the state‐of‐the‐art architectures are not designed for an indoor environment with abundant weakly textured or textureless objects. This paper proposes AttentionSPP‐PatchmatchNet, a deep learning‐based MVS algorithm designed for indoor 3D reconstruction. The algorithm integrates multi‐scale feature sampling to produce global‐context‐aware feature maps and recalibrates the weight of essential features to tackle challenges posed by indoor environments. A new dataset designed exclusively for indoor environments is presented to verify the performance of the proposed network. Experimental results show that AttentionSPP‐PatchmatchNet outperforms state‐of‐the‐art algorithms with relative 132.87% and 163.55% improvements at the 10 and 2 mm threshold, respectively, making it suitable for accurate and complete indoor 3D reconstruction.

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Image-based 3D reconstruction for Multi-Scale civil and infrastructure Projects: A review from 2012 to 2022 with new perspective from deep learning methods

Lu,

Wang,

Fan

et al. 2024

Advanced Engineering Informatics

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Semi-automated luminance map re-projection via high dynamic range imaging and indoor space 3-D reconstruction

Cited by 12 publications

References 53 publications

The analysis of the structural parameter influences on measurement errors in a binocular 3D reconstruction system: a portable 3D system

The analysis of the structural parameter influences on measurement errors in a binocular 3D reconstruction system: a portable 3D system

Multi‐view stereo for weakly textured indoor 3D reconstruction

Image-based 3D reconstruction for Multi-Scale civil and infrastructure Projects: A review from 2012 to 2022 with new perspective from deep learning methods

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