Structure from Motion (SfM) has its roots in the well-established spatial measurement method of photogrammetry, but is becoming increasingly recognised as a means to capture dense 3D data to represent real-world objects, both natural and man-
made. This capability has conventionally been the domain of the terrestrial laser scanner (TLS), a mature and easy to understand method used to generate millions of 3D point coordinates in a form known as a “point cloud”. Each technique is described and noted for its strengths and weaknesses

Jim Chandler (1250355)

Simon Buckley (7179293)

English

Loughborough University Institutional Repository

1  Structure	from	motion	(SfM)	photogrammetry	vs	terrestrial	laser	scanning‐	Jim	Chandler1	&	Simon	Buckley2	1. School of Civil & Building Eng., Loughborough University, LE11 3TU, UK: j.h.chandler@lboro.ac.uk 2. Uni Research CIPR, Bergen, Norway:  Simon.Buckley@uni.no Structure from Motion (SfM) has its roots in the well-established spatial measurement method of photogrammetry, but is becoming increasingly recognised as a means to capture dense 3D data to represent real-world objects, both natural and man-made. This capability has conventionally been the domain of the terrestrial laser scanner (TLS), a mature and easy to understand method used to generate millions of 3D point coordinates in a form known as a “point cloud”. Each technique is described and noted for its strengths and weaknesses. Terrestrial Laser Scanning TLS, commonly known by the technique’s measurement principle lidar (light detection and ranging), has been used for topographic mapping since the mid-late 1990s (e.g. Kraus, 2007). Lidar itself uses a number of laser-based measurement techniques to determine 3D point coordinates on a surface object relative to the instrument. For earth science and topographic mapping, the “time-of-flight” principle is most commonly used, as it allows for longer ranges than phase-based (very fast capture and dense point clouds, but range limited) and triangulation (high accuracy and density, but very short range, <2 m) methods. Time-of-flight implementations on TLS instruments use pulses of laser light and reflectorless electromagnetic distance measurement (EDM) to determine a range to the object, while a scanning mechanism provides deflection angles using a mirror system and/or rotating head. These known vectors allow individual 3D coordinates to be determined and when combined, enable a dense point cloud to be captured quickly in an arbitrary but scaled 3D coordinate system (Buckley et al., 2008). Tens to hundreds of thousands of points are collected per second in modern pulsed time-of-flight instruments. Multiple scans are collected from different positions to obtain full coverage of an object or landform. Early development to TLS systems, pioneered by companies such as Cyra (USA, now Leica Geosystems) and Riegl (Austria), was characterised by rapid and significant advances, such as digital camera integration to provide colour-coded geometry, massive increases in data acquisition rates, and increased portability, ruggedness, and battery life. Recently, developments have been more incremental, with longer measurement ranges (>5 km for scanners optimised for snow and ice measurement) and full waveform technology to allow multiple returns within a single laser footprint to be analysed. This is especially useful for vegetation studies (Mallet and Bretar, 2009). Integration with other sensors, such as higher spectral resolution cameras, is also a developing area (Kurz et al., 2012; Kurz et al., 2013). Because of the different measurement principles and laser classes used, no single TLS instrument will fit all applications over the range of scales and accuracy requirements, making it less flexible as an overall approach. A compromise of range versus point precision is required. TLS technology and market penetration has matured to the extent that many see the technology as the obvious choice for capturing our world in 3D at close range, with applications including city modelling and building information modelling (BIM), architecture and crime scene recording. Within the geosciences, TLS has been heavily used in landform measurement and monitoring (Montreuil et al., 2013; Dewez, et al., 2013), geology (Bellian et al., 2005; Remondino et al., 2010), and change detection (Rosser et al., 2005; Nield et al., 2011). Despite this increasing number of applications, significant disadvantages of the method remain. These are largely practical and relate to the cost of equipment (typically US$30,000-80,000) and the size and weight of equipment can mean that some field sites remain inaccessible.   2  Structure from Motion The phrase “Structure-from-Motion” evolved from the machine vision community, specifically for tracking points across sequences of images captured at different positions (e.g. Szeliski and Kang, 1994). However, SfM owes its existence to mathematical models developed many years ago in photogrammetry, including: coplanarity and collinearity, specifically the self-calibrating bundle adjustment (Kenefick et al., 1972; Faig, 1975). Photogrammetry is a long established method that has been used to compile the world’s maps since the 1920s and has evolved to take advantage of digital image processing to automatically generate digital elevation models and orthophotos, such as those now used in Google Earth. The SfM approach has been explained by a range of authors (e.g. Westoby et al., 2012; James and Robson, 2012; Fonstad et al., 2013; Micheletti et al., 2015), but in essence it involves acquiring images from a number of positions relative to the object of interest. An interest operator, such as the scale invariant feature transform (SIFT) identifies distinctive features appearing upon multiple images and establishes the spatial relationships between the original camera positions in an arbitrary and unscaled coordinate system. A self-calibrating bundle adjustment is then used to calibrate the camera(s) and derive a sparse set of coordinates to represent the object. Point density is then intensified using “multi-view stereo” (MVS) techniques, to generate a very high resolution point cloud, which is colour-coded using the original image data. Figures 1-3 demonstrate the type of high resolution point clouds achievable to represent a natural feature, here an eroded dune system (Figure 1), using both TLS (Figure 2) and SfM  photogrammetry (Figure 3). SfM photogrammetry can eradicate two of the challenges associated with TLS: cost and occlusions. As the method simply requires images acquired from a digital camera or smartphone and access to cheap or even free software solutions, it provides a very cost-effective solution to create a 3D point cloud. The need to acquire images from multiple positions also addresses some of the issues related to occlusions. To this, should be added the flexibility of scale. The precision of any photogrammetric solution is directly related to scale of the imagery and the geometry of the images captured. Precision is therefore commensurate with scale and if image geometry is appropriate and camera calibration effective, then an SfM photogrammetric solution can generate data that is of higher accuracy than achievable using  Figure 1 Digital SLR image of an eroded  dune system  Figure 2 3D TLS data of sand dune  Figure 3 3D SfM photogrammetry data, using Figure 1 imagery 3  standard TLS. Despite these apparent advantages there are some dangers. To be effective, imagery has to be obtained which is of high image quality (i.e. sharply focused, no motion blur) and obtained from locations that provide appropriate coverage (each desired point on the object must appear on at least three frames) and with an appropriate geometry (images must be acquired from spatially different positions) (Micheletti et al., 2015). Failure to achieve the latter is particularly problematic, with many users using UAVs or drones to acquire vertical aerial imagery of an object. This configuration generates imagery which has inherently weak geometry, and inaccuracies in the calibration of the camera (specifically the lens model determined) will generate inaccuracies within the object (Wackrow and Chandler, 2008). This may not always be detected but will manifest itself as a highly systematic domed surface, which has been reported recently by a number of authors (James and Robson, 2014; Woodgett et al., 2014). Finally, as SfM photogrammetry relies on identical features being found between multiple images, object surfaces with uniform colour or texture may not be suitable for generating 3D data, unlike the active TLS method (compare bottom parts of Figures 2 and 3). Conclusion The purpose of this short review has been to outline the basic approaches used by these two technologies and identify strengths and weaknesses between the two. Speed of acquisition is significantly slower for TLS than image-based techniques (often hours vs minutes) for comparable data resolutions. However, as an active range measurement technique TLS offers advantages in terms of accuracy, repeatability and reliability, and can still be viewed as the “gold standard” for 3D measurement. SfM is both cost-effective, automated and allows consistent image-to-geometry registration, suggesting that structure from motion photogrammetry can rival terrestrial laser scanning for many applications.  Both techniques can generate very high resolution point clouds consisting of millions of 3-D points. This creates challenges in terms of data storage and processing hardware and users must consider what information is to be extracted beyond simply visualisation. References Bellian, J.A., Kerans, C., and Jennette, D.C., 2005. Digital outcrop models: applications of terrestrial scanning lidar technology in stratigraphic modeling. Journal of Sedimentary Research, 75(2): 166-176.El-Hakim, S.F., Beraldin, J.-A., 2007. Sensor integration and visualisation. In: Applications of 3D Measurement from Images (Eds. J.G. Fryer, H.L. Mitchell, J.C. Chandler), Whittles, Caithness, pp. 259-298. Buckley, S.J., Howell, J.A., Enge, H.D., and Kurz, T.H., 2008. Terrestrial laser scanning in geology: data acquisition, processing and accuracy considerations, Journal of the Geological Society, 165(3): 625-638. Dewez, T., Rohmer, J., Regard, V., and Cnudde, C. 2013. Probabilistic coastal cliff collapse hazard from repeated terrestrial laser surveys: case study from Mesnil Val (Normandy, northern France). Journal of Coastal Research, Coastal Education and Research Foundation (CERF), 2013, Journal of Coastal Research, 65 (Special Issue), pp. 702-707. Faig, I.W., 1975. Calibration of close range photogrammetric systems. Photogrammetric Engineering and Remote Sensing, 41(12): 1479-1486. Fonstad, M.A, Dietrich, J.T., Courville, B.C., Jensen, L. and Carbonneau, P.E., 2013. Topographic structure from motion: a new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38(4), pp.421–430. Available at: http://doi.wiley.com/10.1002/esp.3366 [Accessed May 28, 2013]. James, M.R. and Robson, S., 2012. Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application. Journal of Geophysical Research, 117(F3), p.F03017. Available at: http://doi.wiley.com/10.1029/2011JF002289 [Accessed November 20, 2013]. James, M.R. and Robson, S., 2014. Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surface Processes and Landforms, 39(June), pp.1413–1420. Kenefick, J.F., Harp, B.F. and Gyer, M.S., 1972. Analytical self-calibration. Photogrammetric Engineering, 38(11): 1117-1126. Kraus, K., 2007. Photogrammetry: Geometry from Images and Laser Scans, Second Edition, Walter de Gruyter, Berlin, 459 pages. Kurz, T.H., Buckley, S.J. and Howell, J.A., 2013. Close-range hyperspectral imaging for geological field studies: workflow and methods. International Journal of Remote Sensing, 34(5): 1798-1822. 4  Kurz, T.H., Dewit, J., Buckley, S.J., Thurmond, J.B., Hunt, D.W., and Swennen, R., 2012. Hyperspectral image analysis of different carbonate lithologies (limestone, karst and hydrothermal dolomites): the Pozalagua Quarry case study (Cantabria, North-west Spain). Sedimentology, 59(2): 623-645. Mallet, C. and Bretar, F., 2009. Full-waveform topographic lidar: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1): 1-16. Micheletti, N., Chandler, J.H. and Lane, S.N., 2015. Investigating the geomorphological potential of freely available and accessible structure-from-motion photogrammetry using a smartphone. , 486(October 2014), pp.473–486. Montreuil, A.-L., Bullard, J., Chandler., J., and Millett, J., 2013. Decadal and seasonal development of embryo dunes on an accreting macrotidal beach: North Lincolnshire, UK. Earth Surface Processes and Landforms, 38(15), pp.1851–1868. Available at: http://doi.wiley.com/10.1002/esp.3432 [Accessed December 12, 2013]. Nield, J.M., Wiggs, G.F. and Squirrell, R.S., 2011. Aeolian sand strip mobility and protodune development on a drying beach: examining surface moisture and surface roughness patterns measured by terrestrial laser scanning. Earth Surface Processes and Landforms, 36(2): 273-278. Remondino, F., Rizzi, A., Girardi, S., Petti, F.M. and Avanzini, M., 2010. 3D ichnology – recovering digital 3D models of dinosaur footprints. Photogrammetric Record, 25(131): 266-282. Rosser, N.J., Petley, D.N., Lim, M., Dunning, S.A. and Allison, R.J., 2005. Terrestrial laser scanning for monitoring the process of hard rock coastal cliff erosion. Quarterly Journal of Engineering Geology and Hydrogeology, 38(4): 363-375. Szeliski, R. and Kang, S., 1993. Recovering 3D shape and motion from image streams using nonlinear least squares. Proceedings CVPR’93., 1993 IEEE …. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=341157. Wackrow, R. and Chandler, J.H., 2008. A convergent image configuration for DEM extraction that minimises the systematic effects caused by an inaccurate lens model. Photogrammetric Record, 23(121), pp.6–18. Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., and Reynolds, J.M., 2012. “Structure-from-Motion” photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, pp.300–314. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0169555X12004217 [Accessed May 23, 2013]. Woodget, A.S., Carbonneau, P.E., Visser, F. and Maddock, I.P., 2014. Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surface Processes and Landforms, 64(August 2014), pp.47–64.  

Structure from motion (SFM) photogrammetry vs terrestrial laser scanning

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Structure from motion (SFM) photogrammetry vs terrestrial laser scanning

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