master thesis ariel bustamante trista digital preservation ......geoengine - msc in geomatics...
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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:
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
……. 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
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
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
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
Figure 3. Registration results. Laser point cloud distance scalar field
Figure 4. Very dense 3D reconstruction using absolute oriented images
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
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
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
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.
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
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