PERFORMANCES EVALUATION OF A LOW COST ACTIVE SENSOR FOR CULTURAL HERITAGE DOCUMENTATION

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1 Guidi, Remondino, et al. 59 PERFORMANCES EVALUATION OF A LOW COST ACTIVE SENSOR FOR CULTURAL HERITAGE DOCUMENTATION Gabriele GUIDI 1, Fabio REMONDINO 2, Giorgia MORLANDO 1 Andrea DEL MASTIO 3, Francesca UCCHEDDU 3, Anna PELAGOTTI 4 1 Reverse Modeling Lab, Politecnico of Milan, Italy, <gabriele.guidi><giorgia.morlando>@polimi.it 2 Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, fabio@geod,baug.ethz.ch 3 Dept. of Electronics and Telecommunications, University of Florence, Italy, <delmastio><uccheddu>@lci.det.unifi.it 4 Art-Test, Pisa, Italy, pelagotti@art-test.com KEY WORDS: Active Sensor, MLT, Cultural Heritage, 3D data comparison, 3D data evaluation. ABSTRACT In the last decade, 3D active sensors, like laser scanners or structured light systems, have been used for both documentation and analysis of historical and archaeological finds. Although remarkable results have been obtained, the high cost of the 3D sensors and of the processing software required has been a limiting factor for an extensive application of these technologies to the Cultural Heritage field. Recently, however, a very low-cost 3D scanning device has been introduced to the market, with nominal performances comparable with those of far more expensive systems. The objective of this contribution is the evaluation and characterization of such type of sensor, by means of a set of comparative tests with two other widely diffused laser scanners. The tests are focused on their instrumental performances, in terms of precision and accuracy, as well as on actual results on a few small archeological finds.. 1 INTRODUCTION Despite their first use dates more than 20 years ago, presently there are no standard test protocols for evaluating the performance of terrestrial 3D imaging systems such as laser scanners or 3D range cameras as well as there are no methods for assessing the accuracy of the derived output such as 3D models, volumes, or geometric dimensions (Beraldin and Gaiani, 2005). However, there is a definitive need to evaluate (i) operational performances; (ii) stability and repeatability; (iii) spatial/temporal coverage/resolution). The understanding of the basic theory and best practices associated with active sensors are then fundamental to be able to assess such characteristics and realized which device is best suited for a specific application. In this contribution we address the evaluation of the low-cost NextEngine 3D laser scanner based on the Multistripe Laser Triangulation (MLT) technology, comparing its performances against that of other well known triangulation-based range sensors, especially focusing on digitization of small archeological finds. 2 OPTICAL ACTIVE SENSORS 2.1 Overview Active sensors based on light waves and employed for 3D modeling issues can be divided in two classes, according to their measurement principle (Beraldin, 2004; Blais, 2004):

2 60 Structured Light and Laser Scanning - triangulation: they can use a single spot of light (1D), a stripe or profile (2D) or an area (2.5D). They are generally constituted by a camera and a sensor (e.g. laser or projector), spaced by a certain distance (baseline). The working range is about 0.1 cm to ca 500 cm. These systems provide a lateral spatial (x and y) resolution up to 50 μm and a depth uncertainty starting from 10 μm. Generally, the error in triangulation-based systems (uncertainty of the distance Z) is proportional to the uncertainty of the laser spot position σ P and to the square of the object distance while it is inversely proportional to system baseline B and effective position of the laser spot sensor f. 2 Z σ Z = σ P fb The baseline B is limited by the mechanical structure of the optical set-up and self-occlusions (that increase with B). σ P depends on the type of laser spot used, the peak detector algorithm, the signalto-noise ratio of the involved optoelectronics and the imaged laser beam shape. To our knowledge, the longest range of triangulation-based laser scanners does not exceed 10 m (requiring a baseline larger than 1 m). The X-Y spatial resolution of optical triangulation-based laser sensors is limited by the diffraction of the laser light. Even in the best emitting conditions (single mode) the laser light does not maintain collimation with the range. In fact, the smaller the laser beam, the bigger is the divergence produced by diffraction. Diffraction limits impose a constraint also along the range axis: increasing the laser power, we could expect an improvement of the accuracy but the coherence of the laser light produces damaging interference effects that limit the resolving power of a position sensor. This is known as the speckle effect. - time delay and light coherence: as the light wave s speed is constant and well known, the delay of a light impulse traveling from a source towards a reflecting object and back to the source is a very easy way to determine the distance. Knowing the medium where the light is traveling (to use the correct refraction index), the most employed strategies are Time of Flight (ToF), Amplitude Modulation (AM) and Frequency Modulation (FM). The working range is about 1 m to some km. Commercial systems provide a measurement uncertainty as a function of the range, typically from few mm to some cm. Worth to be mentioned that there is a limited number of available commercial systems working in the range between 2 and 10 m. This is a transition range, where triangulation-based sensors would require very large baseline (generally unpractical) while time delay-based sensors could achieve relatively low measurement uncertainty but have to face other problems like higher manufacturing costs and in some cases limited operating depth of field (Blais, 2004). 2.2 System errors Any measurement system involves errors that can be roughly divided in systematic and random errors. Each type of error originates a corresponding wrong behavior which is identified as accuracy and uncertainty (or precision), respectively. The approach used in metrology for experimentally estimating the two parameters is usually to complete a set of N repeated measurements over a parameter known in advance thanks to a metrological sample (x true ). Such set of values {x 1, x 2, x N } with a perfect measurement system are all equal to the value of the sample, but, due to the effect of the limited accuracy and precision of a real system, these will represent a set of random values distributed around an average value. If the number of repetitions is reasonably high, the set of values can be statistically analyzed by means of a histogram, whose position over the horizontal axis will represent the average (i.e. the measured N xi i = value) x = 1. Due to systematic errors such value will be biased respect to the true value; its N deviation is identified as accuracy error: Δ x = x x. On the other hand the spreading of the a true (1)

3 Guidi, Remondino, et al. 61 measured values around the average will be calculated through their standard deviation σ = x 1 N 2 ( xi x) N i = 1. The uncertainty in general tends to be identified with the standard deviation or a reasonable multiple of it (e.g. 3 to 5 σ x ). Hereafter, we will indicate as uncertainty error the standard deviation itself: Δ x = σ. u x 2.3 Physical effects generally investigated Among most important sources of error affecting accuracy and uncertainty of an active sensor are: - laser surface penetration: rarely explored, this effect (Figure 1, left) causes the reflection of part of the signal at a different depth, due to material s proprieties. A study on marble surface is presented in (Godin et al., 2001). - range artifacts: they are induced by a shift in the measurement of the centroid of the imaged laser spot from a perfect surface, therefore they can be generated by (i) range or (ii) intensity discontinuities (Figure 1; Blais et al., 2005). Range artifacts compensation is a complex illconditioned problem of deconvolution; these artifacts are directly linked to the capabilities of the optical system to resolve the illuminated point on the object (Nyquist criteria). Although the effects related with these sorts of problems are certainly affecting the results of all devices, it not the goal of this work to analyze in details what is the influence of each of them on the final results. We preferred for this preliminary investigation a black box approach. Figure 1: The effect of surface penetration on the laser spot (left), range artifacts created by a shift in the centroid of the laser spot due to a depth discontinuities (center) or color variation (right). 2.4 MLT sensors The NextEngine laser scanner here investigated is based on the Multistripe Laser Triangulation (MLT) technology. The instrument (Figure 2) is a low cost scanner, presented as a desktop scanner able to digitize small objects in basically two categories: shoe box and soda can size. It is very portable, being small and of light weight, and has a turning table supplied together with it, which is wired to the scanner and is controlled by the proprietary software included. Not much information is available, reading the technical specs provided, about the used device scanning principles. However we found in a public database, a patent [Ser. No. 09/660,809 filed on Sep. 13, 2000 now U.S. Pat. NO. 6,639,684 entitled Digitizer using intensity gradient to image features of three-dimensional objects ] issued in 2000 and granted in 2005, by the company producing the scanner, which in the embodiment describes a device very similar to the one commercialized. The acquisition principle there described is a new one and is based on the two considerations: (i) depth data for a three-dimensional object may be calculated from an acquired intensity difference, resulting from an intensity gradient projected onto the object; (ii) existing low cost imaging devices,

4 62 Structured Light and Laser Scanning such as CCD or CMOS linear array, present a very accurate and linear response to the intensity of the light received. Moreover, the device combines the results obtained with this method with the ones attainable with fast photogrammetric methods. The usage of a combination of methods to derive more accurate results is also patented in a separate application, as well as the turning table is. In the detailed description of the embodiment is reported that if a light source is placed in fixed relation to the Image Sensing Array (ISA) such that projected light forms an angle with the focal line of the ISA, and a gradient slide, for example, going from dark to light, from left to right, is interposed between the light source and the object, features of the object closer to the ISA are illuminated by greater intensity light then these features further away. Thus the ISA captures a stripe of the object in which different intensity represent different depths of the object in that focal zone. This is generally true for uniformly coloured objects acquired in a dark environment. In order to cope with coloured objects in natural light environment, a number of practices are devised, which are described in the embodiment provided. The images of the object, acquired at different positions, are used, as mentioned before, to help generating the 3D model. The instruments is equipped with a twin array of 4 solid state lasers (class 1M, 10mW) with λ=650 nm and two 3 Mpixel CMOS RGB array sensors. The system acquires in two different modes corresponding to two different baselines: wide mode and macro mode respectively. For each mode, some constraints on the distance between the object and the scanner are given: the ideal position for wide mode requires the object to be 45 cm far from the front of the scanner, while macro mode requires the object to be 16 cm far away. The instrument characteristics reported in the specs is reported in table 1. Macro Mode Wide Mode Field of view 130 x 96 mm 343 x 256 mm Resolution (Geometry point density on target surface) 200 DPI 75 DPI Texture Density (on target surface) 400 DPI 150 DPI Accuracy ±0.127 mm ±0.381 mm Uncertainty not available not available Table 1: Specifications of the NextEngine laser scanner, as given by the producer. 3 PERFORMANCE EVALUATION 3.1 Literature review on active sensors evaluation Performances evaluation is a quite new topic compared to the two decades of range sensors history. They have been often evaluated for specific applications (Buzinski et al., 1992; El-Hakim et al., 1995; Beraldin and Gaiani, 2005; Guidi and Bianchini, 2007) or using self-developed methodologies (Balzani et al., 2001; Tucker, 2002; Boehler et al., 2003; Clark and Robson, 2004; Kersten et al., 2004; Russo et al., 2007). Active sensors have been also compared with image-based methods (Baltsavias, 1999; Boehler and Marbs, 2004; Remondino et al., 2005) but we can definitely conclude that each research was done using different approaches and terminology, leading to general conclusions and with partial descriptions of all the problems and parameters involved in range sensors. This was also underlined in Beraldin et al. (2007) where a summary of all the causes of uncertainty in 3D imaging systems are presented and described. Therefore, due to a lack in the deeply understanding of range technologies and their parameters, some standards are required. A good example is provided by the German VDI/VDE 2634 guideline for close-range optical 3D vision systems (particularly for full-frame range cameras) while the ASTM/E57 committee is trying to develop standards for 3D imaging systems. 3.2 Overview of the experiments Following Beraldin et al. (2007), the scanning results are a function of: intrinsic characteristics of the instrument (calibration, measurement principle, etc.);

5 Guidi, Remondino, et al. 63 characteristics of the scanned material in terms of reflection and light diffusion; characteristics of the working environment; light absorption of the scanned material (amplitude response); coherence of the backscattered light (phase randomization); dependence from the chromatic content of the scanned material (frequency response). Therefore our experiments and tests tried to evaluate the performances of the MLT scanner checking: instrument parameters: range images of a reference plane provide for relative accuracy and standard deviation; instrument behaviour in scanning different objects: aligning the range maps with those provided by other instruments, we estimate the differences in term of standard deviations; range artifacts in correspondence of abrupt reflectivity changes. 3.3 Instruments A part from the tabletop scanner previously described, we employed two other range cameras. The first range camera used is a Minolta Vivid 910, a range camera equipped with three exchangeable lenses characterized by three different focal distances (25, 14, 8 mm). With a baseline of 250 mm, the minimum measuring distance is 600 mm and the DOF reported on the datasheet is 1900 mm, giving a maximum working distance of 2500 mm. The points acquired by the sensor are 640 x 480 for each scan. The range camera is provided with a bi-dimensional CCD, so the dimensional relationship between resolution in x and y axis is fixed, depending only on the CCD geometry. The second range camera is a ShapeGrabber SG100 provided with a mechanical linear rail system; this permits a 60 cm horizontal translation of the sensor side by side the supporting plane. The SG 100 is mounted on a high precision linear rail. It represents an example of precise and expensive scanning platform compared to other range cameras available on the market. The minimum distance (standoff) is 130 mm and the Depth of Field (DOF) is 160 mm. The maximum working distance is 290 mm while the overall scan volume is 590x160x165 mm. The range camera is provided with a linear sensor capable to acquire n=1280 points for each profile. The angle covered by the laser line is ϕ=21.49, hence the resolution along this axis (x) is ϕ π directly related to the camera-to-surface distance d is Δx d and, substituting the actual n 180 numerical values, π 4 Δx d d ( ) For example on a planar object orthogonal to the laser light plane, located at d = 500 mm, we obtain Δx=146 μm. The resolution along the rail (y axis) depends only on the mechanical movement step controllable by an operator, whose value can be set as low as 100 μm. Figure 2: Three different typologies of range cameras used for the experiment: NextEngine (left), Minolta Vivid 910 (center), and ShapeGrabber SG100 mounted on a high precision rail (right). As visible in the left image, the NextEngine has two cameras (i.e. in the two square windows on the left hand side of the image, opposite to the led arrays), indicating the capability to work with two different baselines; the larger one have been used for the experiments.

6 64 Structured Light and Laser Scanning 3.4 Objects used for the evaluation The objects used in the experiments are a reference plane available in the Reverse Modeling Lab of the Polytechnic of Milan (Italy) and some archaeological finds of the LAP&T archaeological lab of the University of Siena (Italy) Reference plane. In order to guarantee a small deviation from planarity and a low cost, a thick piece of glass was used as reference plane. The particular manufacture process of this material allows to obtain a flat surface characterized by peak deviation inside a range of few microns, and therefore suitable to be used as test target. The problem with glass is its transparency, hence one side of the plane was painted with matt white varnish layer, achieved with a particular method which includes a furnace treatment, similar to what is used for car painting. This treatment permits to avoid the see-through problem of glass, but it still obtains a perfectly smooth and optically cooperative surface. The dimensions of the rectangular object used as reference are 700 x 528 x 11 mm The archaeological finds. The chosen objects have different dimensions and are made of different materials. In particular we used two ancient artifacts: an ivory flute and a metallic decoration. They represent typical archaeological finds of medium dimensions (10-15 cm), and relative monetary value, where the low cost laser scanner could be really useful to easily catalogue a digital copy of the object with a reasonable budget. 3.5 Methodology The principal methodological problem consisted in obtaining a quality evaluation starting only from a set of measured points, without a corresponding set of reference data measured with an alternative (and far more precise) method, as rigorous metrology would require. As a simplified solution, we choose to carry out test measurements on a planar test object, generating the theoretical ground truth data set as the plane minimizing the least squares deviation from the acquired data. Therefore we made the simplifying assumption that the summation of accuracy and precision errors was a zero mean random process. By comparing the acquired range map with the theoretical (best fit) plane equation, it was possible to perform a statistical analysis of residual deviations that gives a first approximation of the range camera measurement uncertainty. Once the amount of random error is estimated, the cloud of points is processed in order to cancel (or at least strongly attenuate) the small random variation in the measured coordinates, leaving the underlying behavior, presumably characterized by a lower spatial frequency, due to equipment systematic errors. On the processed point clouds, another theoretical vs. real coordinates comparison is done creating color maps of error distribution and calculating average and peak deviations of the residual error. The latter step gives an estimate of the system accuracy. 3.6 Software employed The first is a procedure specifically self-developed in Matlab for performing a statistical analysis of data coming from range camera. The outputs of this program are (i) resolution, evaluated as the average point-to-point difference along x and y, (ii) standard deviation of error (RMS) and (iii) peak deviations. The extraction of such parameters involves both the range map points and a plane created from the best fitting using a plane equation. At the end of the process the program gives also a color map provided with color scale, which describes range map deviations from the reference plane. Furthermore we used Polyworks (InnovMetric Inc.) for data format conversion of the range map and for aligning range maps of the same surfaces generate by different instruments.

7 Guidi, Remondino, et al Experiment setting The experiments were performed at the Reverse Modeling Lab of the Polytechnic of Milan (Italy). It is located underground in order to minimize measurement errors due to external or environment a) b) c) Figure 3: Mapping of the deviation errors of a scanned plane from the best-fitting plane, for: a) Next Engine ; b) Minolta Vivid 910; c) ShapeGrabber 100 factors (building vibrations, sun light, heating, etc). For these reasons this lab was a proper setting to carry on the described experiments, assuring a good reliability level during the scan phases. In this lab it is possible to use different light sources like halogen or incandescent lamp. This condition permits to control the ambient lighting intensity, defining the optimal operating conditions. Since we had to compare instruments with rather different properties, we chose to work at a different distances for each scanner, but so to maintain an almost fixed distance/base ratio (1:2.5).

8 66 Structured Light and Laser Scanning 3.8 Results Best fitting to a plane. As shown in (Russo et al., 2007), the 3D analysis of a certified plane allows to estimate simultaneously both uncertainty and relative accuracy. The best fitting plane is used as approximation of the actual position of the physical plane, while the point-to-plane distance of each 3D point belonging to the range map, can be measured and characterized. Since the plane is constructed as that particular plane capable to best fit the original data, the average distance is automatically zero, and no absolute accuracy error can be estimated. However, in this way, the standard deviation gives an estimation of the data spreading around the ideal plane, allowing evaluating the measurement uncertainty. In addition, even if the average is zero, the color coded map of point-to-plain deviations, allows to explore the geometrical data, plotting possible not random patterns, due to accumulation of errors in specific zones and any error coming from systematic causes (relative accuracy). The standard deviation plus the deviation map may then be used as a tool for estimating the range camera performances in terms of both accuracy and uncertainty. Scanner model StdDev NextEngine mm Minolta mm ShapeGrabber mm Table 2: Standard deviations of residual point-to-plane distances For each scanner, the acquired range map is compared with the theoretical (best fit) plane and a statistical analysis of residual deviations is reported (Figure 3). The systematic behavior in the error map of the NextEngine is compared to the results of to the 4 laser device acquisition maps achieved. A systematic wavy error is revealed for the low cost scanner, even if its standard deviation after the best fitting plane process is quite satisfactory (48 μm). Such error distribution is slightly more randomized for the Minolta, which still contains rather evident accumulation of systematic errors (e.g. the blue area in the lower right corner of its error map), together with a larger standard deviation of residual errors (63 μm). Finally the ShapeGrabber shows both a good precision (48 μm) and an extremely regular distribution of systematic errors, due to the possibility of recalibrating it before a measurement session. Looking at the three range deviations reported in Figure 3, we can also notice a blurring effect in the first one (NextEngine ), demonstrating a level of smoothing in the internal instrument processing, definitely higher than the other two scanners Alignment comparison between scanners. These tests aimed at understanding if a low cost 3D scanner is able to provide a good enough sort of data for small heritage documentation. The small objects, which have all different light scattering characteristics, have been scanned with all the instruments and then the range data aligned and compared to estimate both the error distribution (Figure 4 for the flute) and the related standard deviations (Table 3). As shown in Figure 4a the most sophisticate instruments after the alignment give negligible deviations, while some positive peaks are visible in the Minolta -NextEngine alignment (Figure 4b), more evident in the NextEngine -SG 100 comparison (Figure 4c), that, thanks to the dense cloud generated by the ShapeGrabber scanner, shows clearly where the 0.25 mm peak deviations documented by Table 3, are concentrated.

9 Guidi, Remondino, et al. 67 a) b) c) Figure 4: Alignment errors maps of the flute between the employed scanners obtained using PolyWorks/IMAlign : a) Minolta - SG 100; b) Minolta - NextEngine ; c) NextEngine - SG 100. Scanners Flute Decoration StdDev Max error StdDev Max error NextEngine Minolta mm ±0.256 mm mm ±0.179 mm NextEngine SG mm ±0.254 mm mm ±0.199 mm Minolta SG mm ±0.161mm mm ±0.165 mm Table 3: Standard deviations of the single scan alignment between the different scanners Resolution test. These tests have been made on a qualitative basis, in order to visually evaluate the impact of the actual resolution level of the different scanners involved in the tests. a) b) c) Figure 5: Metallic decoration scanned with: a) NextEngine ; b) Minolta ; c) SG 100. Although the nominal resolution used in the three cases is approximately the same (0.2 mm), each laser scanner employs a different algorithm for cleaning the acquired 3D data, which is usually as intense as the intrinsic metrological quality of the measured values gets lower. As a result, each range map, even if characterized by the same data density, is not able to capture the same level of detail. Figure 5 emphasizes this effect showing a low level of detail on the first case (NextEngine ), where some of the little lateral holes are only roughly visible; a better result in the second one (Minolta ), and the best resolution for the third scanner (ShapeGrabber ). 4 CONSIDERATIONS AND CONCLUDING REMARKS In this contribution we have compared a new low cost active sensor 3D acquisition device, based on multi-line triangulation (MLT) technology with some more expensive appliances, by means of their characterization and of a performance evaluation. From the results we can conclude that, although some metrological failures, the MLT scanner is a good instrument for 3D data acquisition and the achieved accuracy and precision are not much different from those of other highly performing and

10 68 Structured Light and Laser Scanning much more expensive scanners. We carried out different tests and comparisons trying to investigate several possible sources of errors of the instruments and their behaviors depending on the scanned material/object. The performance obtained are in general good enough for general purpose 3D scanning (e.g. 3D digital modeling for physical prototypes), but they demonstrate some limitations when acquiring artifacts at very high resolution, like unfortunately it is often needed in cultural heritage applications. In the future we plan to continue the analysis, using more reference data/instruments and more heritage finds in order to determine not only a set of objective scanner parameters, but also the most suitable sensor-material combinations in applications related to cultural heritage. REFERENCES 1. Baltsavias, E. P., 1999: A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, Vol.54(2-3), pp Balzani, M., Pellegrinelli, A., Perfetti, N., Uccelli, F., 2001: A terrestrial 3D laser scanner: Accuracy tests. CIPA 18th Symposium, Potsdam, Germany, pp Beraldin, J.-A., 2004: Integration of Laser Scanning and Close-Range Photogrammetry - The Last Decade and Beyond. IAPRS&SIS, Vol. 34(5), XXth ISPRS Congress, Istanbul, Turkey 4. Beraldin, J.-A., Gaiani, M., 2005: Evaluating the Performance of Close-Range 3D Active Vision Systems for Industrial Design Applications. Videometrics VIII - SPIE Electronic Imaging 2005, Vol Beraldin, J.-A., Rioux, M., Cournoyer, L., Blais, F., Picard, M., Pekelsky, J., 2007: Traceable 3D Imaging Metrology. Videometrics IX - SPIE Electronic Imaging 2007, Vol. 6491, pp. B.1- B Blais, F., 2004: Review of 20 Years of Range Sensor Development. Journal of Electronic Imaging, 13(1): Blais, F., Taylor, J., Cournoyer, L., Picard, M., Borgeat, L., Dicaire, L.-G., Rioux, M., Beraldin, J.-A., Godin, G., Lahnanier, C., Aitken, G., 2005: Ultra-High Resolution Imaging at 50µm using a Portable XYZ-RGB Color Laser Scanner. International Workshop on Recording, Modeling and Visualization of Cultural Heritage, Centro Stefano Franscini, Monte Verita, May 22-27, Ascona, Switzerland 8. Boehler, W, Bordas, M, Marbs A., 2003: Investigating laser scanner accuracy. CIPA 19th Symposium, Antalya, Turkey, pp Boehler, W., Marbs, A., D scanning and photogrammetry for heritage recording: a comparison. Proceedings of 12th Int. Conf. on Geoinformatics, pp , Gävle, Sweden 10. Buzinski, M., Levine, A., Stevenson, W.H., 1992: Performance characteristics of range sensors utilizing optical triangulation. IEEE/AESS Proc. of National Aerospace and Electronics Conference, pp Clark, J., Robson, S., 2004: Accuracy of measurements made with a Cyrax 2500 laser scanner against surface of known colour. IAPRS&SIS, Vol.34(4), XXth ISPRS Congress, Istanbul, Turkey 12. Godin, G., Rioux, M., Beraldin, J.-A., Levoy, M., Cournoyer, L., 2001: An Assessment of Laser Range Measurement of Marble Surfaces. Proceedings of the 5th Conference on Optical 3-D Measurement Techniques, pp , Vienna, Austria 13. Guidi, G., Bianchini C., 2007: TOF laser scanner characterization for low-range applications, Videometrics IX - SPIE Electronic Imaging 2007, Vol. 6491, pp Kersten T, Sternberg H., Mechelke K., Pardo A., 2004: Terrestrial Laser scanning System MENSI GS100/GS200 - Accuracy Tests, Experiences and Projects at the Hamburg University of Applied Sciences. Proc. Panoramic Photogrammetry Workshop, IAPRS&SIS, Vol. 34(5/W16)

11 Guidi, Remondino, et al Remondino, F., Guarnieri, A., Vettore, 2005: 3D Modeling of close-range objects: photogrammetry or laser scanning? Videometrics VIII - SPIE Electronic Imaging 2005, Vol. 5665, pp Russo, M., Morlando G., Guidi G., 2007: Low cost characterization of 3D laser scanners, Videometrics IX - SPIE Electronic Imaging 2007, Vol. 6491, pp Tucker, C., 2002: Testing and verification of the accuracy of laser scanner. Symposium on Geospatial Theory, Ottawa