Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach.
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| Title: | Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach. |
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| Authors: | Karkınlı, Ahmet Emin1 (AUTHOR), Janowski, Artur2 (AUTHOR) artur.janowski@uwm.edu.pl, Kaderli, Leyla3 (AUTHOR), Gül Hüsrevoğlu, Betül4 (AUTHOR), Hüsrevoğlu, Mustafa1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1971. 26p. |
| Subjects: | Differential evolution, Optimization algorithms, Digital preservation, Aerial photogrammetry, Three-dimensional imaging |
| Geographic Terms: | Türkiye |
| Abstract: | Highlights: What are the main findings? Multi-population based differential evolution refines the TLS–UAV registration of the Hasaköy (Sasima) Church to a 30-run median bidirectional trimmed RMSE of 0.3718 m, below the TR-ICP (0.4152 m), PSO, and Aquila Optimizer baselines under the same 6-DoF budget. Six-degree-of-freedom decomposition shows the residual adjustment concentrates near a 3.4° vertical-axis rotation and a 0.71 m translation norm. What are the implications of the main findings? A parameter-light, training-free optimization framework supports reproducible highfidelity 3D documentation of partially overlapping multi-sensor heritage sites. Reporting bidirectional trimmed RMSE alongside facade-overlap accuracy and UAV block control avoids conflating decimeter-scale cross-source distances with centimeterscale sensor precision. The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 194915104 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Karkınlı%2C+Ahmet+Emin%22">Karkınlı, Ahmet Emin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Janowski%2C+Artur%22">Janowski, Artur</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> artur.janowski@uwm.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Kaderli%2C+Leyla%22">Kaderli, Leyla</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gül+Hüsrevoğlu%2C+Betül%22">Gül Hüsrevoğlu, Betül</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hüsrevoğlu%2C+Mustafa%22">Hüsrevoğlu, Mustafa</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1971. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+preservation%22">Digital preservation</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+photogrammetry%22">Aerial photogrammetry</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+imaging%22">Three-dimensional imaging</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Türkiye%22">Türkiye</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Multi-population based differential evolution refines the TLS–UAV registration of the Hasaköy (Sasima) Church to a 30-run median bidirectional trimmed RMSE of 0.3718 m, below the TR-ICP (0.4152 m), PSO, and Aquila Optimizer baselines under the same 6-DoF budget. Six-degree-of-freedom decomposition shows the residual adjustment concentrates near a 3.4° vertical-axis rotation and a 0.71 m translation norm. What are the implications of the main findings? A parameter-light, training-free optimization framework supports reproducible highfidelity 3D documentation of partially overlapping multi-sensor heritage sites. Reporting bidirectional trimmed RMSE alongside facade-overlap accuracy and UAV block control avoids conflating decimeter-scale cross-source distances with centimeterscale sensor precision. The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18121971 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1971 Subjects: – SubjectFull: Differential evolution Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Digital preservation Type: general – SubjectFull: Aerial photogrammetry Type: general – SubjectFull: Three-dimensional imaging Type: general – SubjectFull: Türkiye Type: general Titles: – TitleFull: Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Karkınlı, Ahmet Emin – PersonEntity: Name: NameFull: Janowski, Artur – PersonEntity: Name: NameFull: Kaderli, Leyla – PersonEntity: Name: NameFull: Gül Hüsrevoğlu, Betül – PersonEntity: Name: NameFull: Hüsrevoğlu, Mustafa IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
