master thesis ariel bustamante trista digital preservation ......geoengine - msc in geomatics...

GEOENGINE - MSc in Geomatics Engineering, Master Thesis Ariel Bustamante Trista

Digital Preservation of the Calw Market by Means of Automated HDS and Photogrammetric Texture Mapping

Master Thesis Ariel Bustamante Trista

Digital Preservation of the Calw Market by Means of Automated HDS and Photogrammetric Texture Mapping Duration of the Thesis: 6 months

Completion: May 31, 2013

Supervisor: Dr.-Ing. habil Dieter Fritsch Examiner: Dr.-Ing. habil Dieter Fritsch Background 3D digital preservation of cultural heritage sites is today encouraged by many institutions. To start with some object information the acquisition of accurate point clouds of the site is quite often the first step. The combination of 3D laser scanner and digital photogrammetry data is known to deliver high definition point clouds for the digitization of urban scenes [1]. It is also necessary to overcome the weaknesses of both technologies : laser scanners are not efficient in reconstructing for example corners, and the collection of digital close range imagery needs well-trained operators and a corresponding software suite. On the other hand, digital images are known to deliver the highest spatial resolution for 3D reconstruction of roof landscapes using airborne cameras. Objectives This thesis will cover an approach to reconstruct 3D photorealistic models of outdoors objects by means of automatically georeferenced High Definition Surveying (HDS) point clouds and still video images. On the one hand, the images are used to detect point features which are also found in the 3D HDS point cloud, and, on the other hand serve for photorealistic 3D models. Absolute oriented images from close range and airborne applications can be used to compute very dense point clouds. Methods The static terrestrial laser scanner is used to reconstruct main facades. In addition, digital images are taken from close range in a special arrangement for the derivation of dense point clouds by the following main steps:

1. Feature extraction and matching using scale-invariant keypoints (SIFT) [2] 2. Selection of points in a RANSAC [3] robust estimation procedure 3. Camera self-calibration, position and orientation via Structure from Motion (SFM) 4. Automatic registration of images 5. Very dense 3D reconstruction

We use “One Panorama Each Step” Acquisition Technique [4] for the acquisition of digital images. SIFTGPU and Multicore Bundle Adjustment (PBA) [5] are GPU implementations of both methods in VisualSFM open source package for solving large scale problems using SFM. For the registration of images, we applied the SIFT operator for feature extraction and matching between the images, and a laser RGB image generated by projecting the laser point cloud into a central perspective representation [1]. Detected control points are used for parameters estimation using the Gauss-Helmert model:



……. laser measurement point error

……. photogrammetry point error in arbitrary scale We assume both types of error can be drawn from Normal distributions, therefore the residuals distribute Normal:

The data is obtained from a least squares adjustment (LS). We choose the Extreme Studentized Deviate many-outlier test (ESD) [6] for outlier detection and to check via Maximum Likelihood Estimation (MLE). Absolute oriented images can be used to enhance the laser RGB color by projecting from object to image space using the adjusted camera model. After computing very dense point clouds, the georeferencing is done using the Iterative Closest Point algorithm (ICP) [7] by selecting corresponding regions, for what GPS supported airborne images with 10cm ground resolution were provided for the automatic reconstruction of point clouds of the roof landscapes via dense matching. Results

Table 1. Accuracy Analysis

Laser Stations Control Points RMS Gauss-Helmert

Leica HDS 3000 11 70 0.004m

Camera Images Measurements Reprojection Error

Nikon 20mm 750 1346769 1.1pix

Aerial Camera Coord. System

Std. Dev. Objet points

Std. Dev. Object Z points

UltraCam Xp, S/N UC-SXp-1-

30019136

Gauss-Kruger 3

0.02m 0.067m



Figure 1. Automatic detection of control points using SIFT operator.

Laser RGB image

Probability Plot. Before Probability Plot. After

Figure 2. MLE before and after ESD test for outliers assuming Normal distributed residuals in

Gauss-Helmert model

Table 2. Registration results

MLE Result

Distribution: Normal Log Likelihood: 1053.27 Domain: -Inf , Inf Parameter Estimate [m] Std. Err. μ 0 0.0012 σ 0.0263 0.0009 Control points 190 Removed via ESD 34 % 18

-4 -2 0 2 4 6 8 10

0.00010.00050.0010.0050.010.050.1

0.250.5

0.750.9

0.950.990.995

0.9990.99950.9999

Data

Pro

babili

ty

data

fit

-0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08

0.0001

0.00050.001

0.0050.01

0.05

0.1

0.25

0.5

0.75

0.9

0.95

0.990.995

0.9990.9995

0.9999

Data

Pro

babili

tydata

fit 4



Figure 3. Registration results. Laser point cloud distance scalar field

Figure 4. Very dense 3D reconstruction using absolute oriented images



Laser RGB Image Projected points

Before After

Figure 5. Laser point cloud RGB color correction using absolute oriented images

Before After Figure 6. Laser scanning occlussion correction using digital images data



Figure 7. Automatic reconstruction of point clouds of the roof landscapes using airborne

cameras, showing types of region from the laser point clouds used for georeferencing via ICP

Laser scanning Model Image texture mapping

Figure 8. Photorealistic model

Conclusions

1. The camera system was compared with the LiDAR output with a mean positioning

error of 2.6cm via automatic registration using the SIFT operator. Considering all

steps in between which can be improved, from the data acquisition to the camera

calibration, digital image data has shown to be useful when post-processing

terrestrial laser point clouds.

2. The performed terrestrial laser scanning delivered high accuracy point clouds. The

laser RGB color however had to be enhanced by using other methods.

3. A final analysis of the covariance matrix has to be done by bringing the output

solution from VisualSFM into a full photogrammetry error model, what will require

scaling and a sophisticated algorithm due to the involved high dimensional problem.



References

[1] Moussa, W., Abdel-Wahab, M., and Fritsch, D.: An Automatic Procedure for Combining

Digital Images and Laser Scanner Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,

XXXIX-B5, 229-234, doi:10.5194/isprsarchives-XXXIX-B5-229-2012, 2012.

[2] Lowe, D.G.: Distinctive Image Features from Scale-invariant Keypoints . International

Journal of Computer Vision, 2004.

[3] Fischler, M.A. and Robert C. Bolles, R.C.: Random Sample Consensus: A Paradigm for

Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the

ACM 24 (6), pp. 381–395, 1998

[4] Wenzel, K., Rothermel, M., Fritsch, D., and Haala, N.: IMAGE ACQUISITION AND MODEL

SELECTION FOR MULTI-VIEW STEREO, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,

XL-5/W1, 251-258, doi:10.5194/isprsarchives-XL-5-W1-251-2013, 2013.

[5] Wu, C., Agarwal, S. and Curless, B., Seitz, S.M., Multicore Bundle Adjustment", CVPR,

pp.3057 – 3064, 2011

[6] Rosner, B.:. Percentage Points for a Generalized ESD Many-Outlier Procedure. Technometrics 25(2), pp. 165-172, 1983

[7] Besl, P. J. and McKay, N.D.: "A Method for Registration of 3-D Shapes". IEEE Trans. on Pattern Analysis and Machine Intelligence, 14 (2), pp. 239–256, 1992

http://www.tu-chemnitz.de/etit/proaut/paperdb/download/fischler81.pdf

http://www.tu-chemnitz.de/etit/proaut/paperdb/download/fischler81.pdf

master thesis ariel bustamante trista digital preservation ......geoengine - msc in geomatics...

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