Open Access
Issue |
Wuhan Univ. J. Nat. Sci.
Volume 29, Number 4, August 2024
|
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Page(s) | 301 - 314 | |
DOI | https://doi.org/10.1051/wujns/2024294301 | |
Published online | 04 September 2024 |
- Gong F Y, Liu Y, You Z P, et al. Characterization and evaluation of morphological features for aggregate in asphalt mixture: A review[J]. Construction and Building Materials, 2021, 273: 121989. [CrossRef] [Google Scholar]
- Bessa I S, Branco V T F C, Soares J B, et al. Aggregate shape properties and their influence on the behavior of hot-mix asphalt[J]. Journal of Materials in Civil Engineering, 2015, 27(7): 04014212 . [CrossRef] [Google Scholar]
- Sun Z Y, Wang C F, Hao X L, et al. Quantitative evaluation for shape characteristics of aggregate particles based on 3D point cloud data[J]. Construction and Building Materials, 2020, 263: 120156. [CrossRef] [Google Scholar]
- Sun Z Y, Liu H Y, Ju H Y, et al. Assessment of importance-based machine learning feature selection methods for aggregate size distribution measurement in a 3D binocular vision system[J]. Construction and Building Materials, 2021, 306: 124894. [CrossRef] [Google Scholar]
- Ge H T, Sha A M, Han Z Q, et al. Three-dimensional characterization of morphology and abrasion decay laws for coarse aggregates[J]. Construction and Building Materials, 2018, 188: 58-67. [CrossRef] [Google Scholar]
- Anochie-Boateng J K, Komba J J, Mvelase G M. Three-dimensional laser scanning technique to quantify aggregate and ballast shape properties[J]. Construction and Building Materials, 2013, 43: 389-398. [CrossRef] [Google Scholar]
- Li Q J, Zhan Y, Yang G W, et al. 3D characterization of aggregates for pavement skid resistance[J]. Journal of Transportation Engineering, Part B: Pavements, 2019, 145(2): 04019002. [CrossRef] [Google Scholar]
- Wang H F, Wu Z J, He Z C, et al. Detection of HF-ERW process by 3D bead shape measurement with line-structured laser vision[J]. IEEE Sensors Journal, 2021, 21(6): 7681-7690. [NASA ADS] [CrossRef] [Google Scholar]
- Wang H F, Wang Y F, Zhang J J, et al. Laser stripe center detection under the condition of uneven scattering metal surface for geometric measurement[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(5): 2182-2192. [NASA ADS] [CrossRef] [Google Scholar]
- Tuan N M, Kim Y, Lee J Y, et al. Automatic stereo vision-based inspection system for particle shape analysis of coarse aggregates[J]. Journal of Computing in Civil Engineering, 2022, 36(2): 04021034 . [CrossRef] [Google Scholar]
- Su D, Yan W M. 3D characterization of general-shape sand particles using microfocus X-ray computed tomography and spherical harmonic functions, and particle regeneration using multivariate random vector[J]. Powder Technology, 2018, 323: 8-23. [CrossRef] [Google Scholar]
- Liu B, Fan H M, Jiang Y, et al. Evaluation of soil macro-aggregate characteristics in response to soil macropore characteristics investigated by X-ray computed tomography under freeze-thaw effects[J]. Soil and Tillage Research, 2023, 225: 105559. [CrossRef] [Google Scholar]
- Zhao L H, Zhang S H, Huang D L, et al. 3D shape quantification and random packing simulation of rock aggregates using photogrammetry-based reconstruction and discrete element method[J]. Construction and Building Materials, 2020, 262: 119986. [CrossRef] [Google Scholar]
- Chen Z Q, Jia Y S, Wang S Q, et al. Image-based methods for automatic identification of elongated and flat aggregate particles[J]. Construction and Building Materials, 2023, 382: 131187. [CrossRef] [Google Scholar]
- Pei L L, Sun Z Y, Yu T, et al. Pavement aggregate shape classification based on extreme gradient boosting[J]. Construction and Building Materials, 2020, 256: 119356. [CrossRef] [Google Scholar]
- Jin C, Wang S L, Liu P F, et al. Virtual modeling of asphalt mixture beam using density and distributional controls of aggregate contact[J]. Computer-Aided Civil and Infrastructure Engineering, 2023, 38(16): 2242-2256. [CrossRef] [Google Scholar]
- Jin C, Wang P S, Yang X, et al. Analysis on gradation parameters of asphalt mixture based on 3D virtual measurement[J]. Journal of Highway and Transportation Research and Development, 2019, 36(8): 1-8(Ch). [Google Scholar]
- Peng Y P, Wu Z B, Cao G Z, et al. Three-dimensional reconstruction of wear particles by multi-view contour fitting and dense point-cloud interpolation[J]. Measurement, 2021, 181: 109638. [Google Scholar]
- Liu C Q, Li J, Gao J, et al. Three-dimensional texture measurement using deep learning and multi-view pavement images[J]. Measurement, 2021, 172: 108828. [CrossRef] [Google Scholar]
- Wu X B, Wang J S, Li J J, et al. Retrieval of siltation 3D properties in artificially created water conveyance tunnels using image-based 3D reconstruction[J]. Measurement, 2023, 211: 112586. [NASA ADS] [CrossRef] [Google Scholar]
- Wang Y L, Deng N, Xin B J. Investigation of 3D surface profile reconstruction technology for automatic evaluation of fabric smoothness appearance[J]. Measurement, 2020, 166: 108264. [CrossRef] [Google Scholar]
- Ju X Y, Henseler H, Peng M J Q, et al. Multi-view stereophotogrammetry for post-mastectomy breast reconstruction[J]. Medical & Biological Engineering & Computing, 2016, 54(2): 475-484. [CrossRef] [PubMed] [Google Scholar]
- Carvajal-Ramírez F, Navarro-Ortega A D, Agüera-Vega F, et al. Virtual reconstruction of damaged archaeological sites based on Unmanned Aerial Vehicle Photogrammetry and 3D modelling. Study case of a southeastern Iberia production area in the Bronze Age[J]. Measurement, 2019, 136: 225-236. [CrossRef] [Google Scholar]
- Li T Y, Liu S C, Bolkart T, et al. Topologically consistent multi-view face inference using volumetric sampling[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2021: 3804-3814. [Google Scholar]
- Xiong J, Zhong S, Zheng L. An automatic 3D reconstruction method based on multi-view stereo vision for the mogao grottoes[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, XL-4/W5: 171-176. [CrossRef] [Google Scholar]
- Zhao J, Xu P, Huang S L, et al. Underwater 3D reconstruction based on multi-view stereo[C]//Ocean Optics and Information Technology. New York: SPIE, 2018: 117-123. [Google Scholar]
- Ito K, Ito T, Aoki T. PM-MVS: PatchMatch multi-view stereo[J]. Machine Vision and Applications, 2023, 34(2): 32. [CrossRef] [Google Scholar]
- Zhang Z. A flexible new technique for camera calibration system[J]. IEEE Transactions on Pattern Analysis and Machine, 2000, 22(1): 1330-1334. [Google Scholar]
- Schönberger J L, Frahm J M. Structure-from-motion revisited[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 4104-4113. [CrossRef] [Google Scholar]
- Perez A J, Perez-Soler J, Perez-Cortes J C, et al. Improving multi-view camera calibration using precise location of sphere center projection[J]. Computers, 2022, 11(6): 84. [Google Scholar]
- Furukawa Y, Hernández C. Multi-view stereo: A tutorial[J]. Foundations and Trends® in Computer Graphics and Vision, 2015, 9(1/2): 1-148. [CrossRef] [Google Scholar]
- Perez A J, Perez-Soler J, Perez-Cortes J C, et al. Alignment and improvement of shape-from-silhouette reconstructed 3D objects[J]. IEEE Access, 2024, 12: 76975-76985. [NASA ADS] [CrossRef] [Google Scholar]
- Lorensen W E. History of the marching cubes algorithm[J]. IEEE Computer Graphics and Applications, 2020, 40(2): 8-15. [CrossRef] [PubMed] [Google Scholar]
- Zhao W. A Broyden-Fletcher-Goldfarb-Shanno algorithm for reliability-based design optimization[J]. Applied Mathematical Modelling, 2021, 92: 447-465. [CrossRef] [MathSciNet] [Google Scholar]
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