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Accuracy Evaluation of Videogrammetry Using A Low-Cost Spherical Camera for Narrow Architectural Heritage: An Observational Study with Variable Baselines and Blur Filters

Three-dimensional (3D) reconstruction using video frames extracted from spherical cameras introduces an innovative measurement method in narrow scenes of architectural heritage, but the accuracy of 3D models and their correlations with frame extraction ratios and blur filters are yet to be evaluated...

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Detalles Bibliográficos
Autores principales: Sun, Zheng, Zhang, Yingying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386977/
https://www.ncbi.nlm.nih.gov/pubmed/30691033
http://dx.doi.org/10.3390/s19030496
Descripción
Sumario:Three-dimensional (3D) reconstruction using video frames extracted from spherical cameras introduces an innovative measurement method in narrow scenes of architectural heritage, but the accuracy of 3D models and their correlations with frame extraction ratios and blur filters are yet to be evaluated. This article addresses these issues for two narrow scenes of architectural heritage that are distinctive in layout, surface material, and lighting conditions. The videos captured with a hand-held spherical camera (30 frames per second) are extracted to frames with various ratios starting from 10 and increasing every 10 frames (10, 20, …, n). Two different blur assessment methods are employed for comparative analyses. Ground truth models obtained from terrestrial laser scanning and photogrammetry are employed for assessing the accuracy of 3D models from different groups. The results show that the relative accuracy (median absolute errors/object dimensions) of spherical-camera videogrammetry range from 1/500 to 1/2000, catering to the surveying and mapping of architectural heritage with medium accuracy and resolution. Sparser baselines (the length between neighboring image pairs) do not necessarily generate higher accuracy than those from denser baselines, and an optimal frame network should consider the essential completeness of complex components and potential degeneracy cases. Substituting blur frames with adjacent sharp frames could reduce global errors by 5–15%.