Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application

Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientist...

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Published inJournal of Geophysical Research: Earth Surface Vol. 117; no. F3
Main Authors James, M. R., Robson, S.
Format Journal Article
LanguageEnglish
Published Washington, DC Blackwell Publishing Ltd 01.09.2012
American Geophysical Union
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Abstract Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer‐grade camera. The approach integrates structure‐from‐motion (SfM) and multiview‐stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM‐MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close‐range photogrammetry. The relative precision ratio achieved by SfM‐MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter‐level precision over measurement distances of 10 s of meters). We apply SfM‐MVS at an intermediate scale, to determine erosion rates along a ∼50‐m‐long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a−1. Sequential erosion maps (at ∼0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM‐MVS produced comparable data and reduced data collection time by ∼80%. Key Points Computer vision techniques can be used to derive DEMs from photographs Surface models of coastal cliffs permit geostatistical analysis of erosion Model precision ratios generally exceed 1:1000 thus are useful in geosciences
AbstractList Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer‐grade camera. The approach integrates structure‐from‐motion (SfM) and multiview‐stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM‐MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close‐range photogrammetry. The relative precision ratio achieved by SfM‐MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter‐level precision over measurement distances of 10 s of meters). We apply SfM‐MVS at an intermediate scale, to determine erosion rates along a ∼50‐m‐long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a−1. Sequential erosion maps (at ∼0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM‐MVS produced comparable data and reduced data collection time by ∼80%. Key Points Computer vision techniques can be used to derive DEMs from photographs Surface models of coastal cliffs permit geostatistical analysis of erosion Model precision ratios generally exceed 1:1000 thus are useful in geosciences
Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer-grade camera. The approach integrates structure-from-motion (SfM) and multiview-stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM-MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close-range photogrammetry. The relative precision ratio achieved by SfM-MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter-level precision over measurement distances of 10 s of meters). We apply SfM-MVS at an intermediate scale, to determine erosion rates along a 50-m-long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a-1. Sequential erosion maps (at 0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM-MVS produced comparable data and reduced data collection time by 80%. Key Points Computer vision techniques can be used to derive DEMs from photographs Surface models of coastal cliffs permit geostatistical analysis of erosion Model precision ratios generally exceed 1:1000 thus are useful in geosciences
Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer‐grade camera. The approach integrates structure‐from‐motion (SfM) and multiview‐stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM‐MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close‐range photogrammetry. The relative precision ratio achieved by SfM‐MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter‐level precision over measurement distances of 10 s of meters). We apply SfM‐MVS at an intermediate scale, to determine erosion rates along a ∼50‐m‐long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a −1 . Sequential erosion maps (at ∼0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM‐MVS produced comparable data and reduced data collection time by ∼80%. Computer vision techniques can be used to derive DEMs from photographs Surface models of coastal cliffs permit geostatistical analysis of erosion Model precision ratios generally exceed 1:1000 thus are useful in geosciences
Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer-grade camera. The approach integrates structure-from-motion (SfM) and multiview-stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM-MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close-range photogrammetry. The relative precision ratio achieved by SfM-MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter-level precision over measurement distances of 10 s of meters). We apply SfM-MVS at an intermediate scale, to determine erosion rates along a 50-m-long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a1. Sequential erosion maps (at 0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM-MVS produced comparable data and reduced data collection time by 80%.
Author Robson, S.
James, M. R.
Author_xml – sequence: 1
  givenname: M. R.
  surname: James
  fullname: James, M. R.
  email: m.james@lancaster.ac.uk, m.james@lancaster.ac.uk
  organization: Lancaster Environment Centre, Lancaster University, Lancaster, UK
– sequence: 2
  givenname: S.
  surname: Robson
  fullname: Robson, S.
  organization: Department of Civil, Environmental and Geomatic Engineering, UCL, London, UK
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2015 INIST-CNRS
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Issue F3
Keywords algorithms
cliffs
mass movements
laser methods
maps
software
photogrammetry
topography
accuracy
cartography
erosion rates
coastal zone
correlation
collections
Photogrammetric survey
semivariograms
three-dimensional models
spatiotemporal variations
erosion
craters
calibration
computers
Language English
License http://onlinelibrary.wiley.com/termsAndConditions#vor
CC BY 4.0
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PublicationDate September 2012
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PublicationTitle Journal of Geophysical Research: Earth Surface
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Publisher Blackwell Publishing Ltd
American Geophysical Union
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– reference: Quinn, J. D., N. J. Rosser, W. Murphy, and J. A. Lawrence (2010), Identifying the behavioural characteristics of clay cliffs using intensive monitoring and geotechnical numerical modelling, Geomorphology, 120, 107-122, doi:10.1016/j.geomorph.2010.03.004.
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– reference: Wilcock, P. R., D. S. Miller, R. H. Shea, and R. T. Kerkin (1998), Frequency of effective wave activity and the recession of coastal bluffs: Calvert Cliffs, Maryland, J. Coastal Res., 14, 256-268.
– reference: Gruen, A. W. (1985), Adaptive least squares correlation: A powerful image matching technique, S. Afr. J. Photogramm. Remote Sens. Cartogr., 14, 175-187.
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Snippet Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous...
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SubjectTerms Bundler
Cameras
coastal erosion
Data collection
Data reduction
DEM
Earth science
Earth sciences
Earth, ocean, space
Erosion rates
Exact sciences and technology
Geology
Geomorphology
Hydrology
Photogrammetry
Scientific apparatus & instruments
Soil erosion
structure from motion
surface model
Volcanoes
Title Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application
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