Comparative Analysis of Various Camera Input for Videogrammetry

Ahmad, Nor Ezzaty; Azri, Suhaibah; Ujang, Uznir; Cuétara, M. G.; Retortillo, G. M.; Salleh, Syahiirah

doi:10.5194/isprs-archives-xlii-4-w16-63-2019

Cited by 5 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the large thematic groups described above, one can find a number of publications in which 3D models of various objects were made using images taken from a phone [ 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 ]. Smartphones were also used in radioactivity detection [ 94 ] or in forensic science [ 95 , 96 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

Jasińska

Pyka

Pastucha

et al. 2023

Sensors

View full text Add to dashboard Cite

Recently, the term smartphone photogrammetry gained popularity. This suggests that photogrammetry may become a simple measurement tool by virtually every smartphone user. The research was undertaken to clarify whether it is appropriate to use the Structure from Motion—Multi Stereo View (SfM-MVS) procedure with self-calibration as it is done in Uncrewed Aerial Vehicle photogrammetry. First, the geometric stability of smartphone cameras was tested. Fourteen smartphones were calibrated on the checkerboard test field. The process was repeated multiple times. These observations were found: (1) most smartphone cameras have lower stability of the internal orientation parameters than a Digital Single-Lens Reflex (DSLR) camera, and (2) the principal distance and position of the principal point are constantly changing. Then, based on images from two selected smartphones, 3D models of a small sculpture were developed. The SfM-MVS method was used, with self-calibration and pre-calibration variants. By comparing the resultant models with the reference DSLR-created model it was shown that introducing calibration obtained in the test field instead of self-calibration improves the geometry of 3D models. In particular, deformations of local concavities and convexities decreased. In conclusion, there is real potential in smartphone photogrammetry, but it also has its limits.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The most common solution is on-the-job calibration. Among the many smartphone photogrammetry publications, only a small part of them contain information on calibration [ 30 , 40 , 41 , 59 , 64 , 67 , 72 , 74 , 76 , 78 , 90 , 93 , 97 , 98 , 99 , 100 , 101 , 102 , 103 ].…”

Section: Introductionmentioning

confidence: 99%

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

Jasińska

Pyka

Pastucha

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The 3D building model was created by drone point clouds. Point clouds have been utilised in many 3D city modelling research starting from data acquisition, modelling, and applications [29,30,31,32]. In this research, a simple building model is used to visualise the noise level of road traffic.…”

Section: Building Modelmentioning

confidence: 99%

3D Kriging interpolation for traffic noise visualization: designing noise observation points and valuation of spatial interpolation accuracy

Wickramathilaka,

Ujang

2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Identifying the risk of traffic noise is vital in minimizing traffic noise pollution in urban areas. As noise travels in every direction, 3D visualization of traffic noise is essential, which involves visualising traffic noise along the facades of buildings. A standard traffic noise model is necessary to calculate traffic noise levels, as several factors affect traffic noise. Moreover, designing noise observation points in 3D and spatial interpolation play significant roles in 3D noise visualisation. Therefore, this study demonstrates the results by elaborating on the spatial interpolation and designing noise observation points. A noise observation point consists of four parameters in 3D space. Generally, Inverse Distance Weighted (IDW), Triangular Irregular Network (TIN), and Kriging do not support the interpolation of four parameters in 3D. However, 3D Kriging in Empirical Bayesian Kriging provides significant opportunities to interpolate noise levels in 3D. However, the elements of the function of spatial interpolations are vital for accuracy. The 3D Kriging uses different variograms according to semivariance. This variogram directly impacts the weighting factor of 3D Kriging. Therefore, this study develops a comparison to identify the impact of different variograms on the accuracy of 3D Kriging interpolation on traffic noise.

show abstract

“…Positioned within the realm of photogrammetry, this discipline presents appealing functionalities, rendering it a captivating option for 3D data acquisition. Nonetheless, variations in camera inputs and specifications can lead to varying qualities of resulting point clouds [26]. In videogrammetry, successive videos need to have overlapped for the resultant point clouds of a building to be complete and to facilitate the generation of a detailed 3D model.…”

Section: Data Collectionmentioning

confidence: 99%

A review on the GIS usage in spatio-temporal risk assessment in asset management

Syafiq,

Azri

2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Risk assessment for asset management is a process to analyse the potential risk that could affect the efficiency of operating assets. An effective risk assessment strategy depends on high-quality data and effective data analysis. To do so, risk assessment should take into consideration the spatial and temporal aspects of risks of physical assets in asset management for a data-driven decision-making. Failure to incorporate the spatial and temporal aspects in risk, when the risk possess spatio-temporal correlation, could lead to a defective estimation of assets risks. GIS can be used to analyse multidimensional information which includes 3D GIS, and spatio-temporal data (2D+t and 3D+t). Due to its superiority in handling multidimensional data, GIS is often a preferable tool to collect, manage, manipulate, and visualize spatio-temporal data to allow an accurate and informed analysis. The aim of this study is to uncover the use of GIS for risk assessment in asset management when spatial and temporal data are concerned. This study conduct a thorough review of previous research to look into the current development of asset management that utilizes a spatio-temporal approach for data collection, analysis, and 3D visualization for risk assessment. At the end of this study, we found that physical assets are affected by risk that possess spatio-temporal correlation. Several GIS techniques for data collection, analysis and visualization are found to provide a more effective analysis for assessing spatio-temporal risks. This paper concludes by providing a workflow that can be used by asset managers to assess risk spatio-temporally based on the findings of this study.

show abstract

Comparative Analysis of Various Camera Input for Videogrammetry

Cited by 5 publications

References 10 publications

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

3D Kriging interpolation for traffic noise visualization: designing noise observation points and valuation of spatial interpolation accuracy

A review on the GIS usage in spatio-temporal risk assessment in asset management

Contact Info

Product

Resources

About