Kathmandu University, Fifth Semester, Geomatics Engineering Project

1. Project Supervisors:
Mr. Subash Ghimire
Mr. Sumesh KC
Group Members:
Sunil Bogati (Roll no. 2)
Kushal KC (Roll no. 3)
Sujan Nepali (Roll no. 12)
Srijit Sharma (Roll no. 24)
“3D Modeling of
Archeological Site Using Unmanned Aerial
Vehicle”

2. INTRODUCTION
• Importance of archaeological sites for Nepal
• Reflection of socio-cultural values
• Notion of past history
• Are cultural heritage of the country
• Their vulnerability to disasters
• Textured 3D model a better visual representation
• Preservative for reconstruction in future

3. OBJECTIVES
The objective of our project is
“3D modeling of archeological structure”
Sub objective:
-Optimal flight plan for aerial data capture
-Image acquisition from terrestrial platform
-Image processing
-Accuracy assessment and field Verification

4. Project Site
• Name: Ugratirtha Mahadev Temple
• Location: Budol, Banepa Municipality, Kavre
• Archaeologically important
Source: Google Earth
Accessed on: 20th Jan, 2016
Image: Project Site,
Captured by DJI on 2nd Jan!

5. Planning and Reconnaissance
• Site selection
-Ugratirtha Temple
• Flight Permission
-Letter from Kathmandu University
• Site visit
-Study of the area and the structure
-Information about possible air obstacles
-Image acquisition planned for a clear, bright day

6. Compass Calibration
• An open area was selected
• The aircraft was held horizontally and rotated 360 degrees until the
aircraft status indicators displayed a solid green light.
• Then the aircraft was held vertically, with nose pointing downward
and rotated again 360 degree around the center axis in same
direction as before until the aircraft status indicator glows solid red.

7. Image: Compass Calibration done by Srijit Sharma
Photo taken by: Kushal KC

8. Flight Plan for Aerial platform
• Resource used: DJI Phantom 3 advanced
• Optimal flight plan for better result and overall accuracy of the
project
• Optimal flight plan for getting enough matches
• Flight plan selected:
1. Grid Mission
2. Point of Interest
DJI Phantom and the site

9. Grid Mission
• Flight Planned through Pix4D capture app
• Aerial Nadir images were captured
• Area coverage: 126m x 116m
• 80% front overlap maintained
• Speed of UAV: 4.8 m/s
• Flight done three times(due to signal obstruction)
• Same parameters in every flight
1. Flight1: 79 images
2. Flight2: 23 images
3. Flight3: 93 images
Nadir image taken by DJI Phantom3

10. Point of Interest
• Flight Planned through DJI Go app
• Aerial Oblique images were taken
• Images were manually taken through RC
• In counterclockwise direction
• Return to home altitude =30m
• Photographs were taken in different radius
1. Case A: 6m radius, vehicle speed= 1m/s
2. Case B: 10m radius, vehicle speed= 2m/s
3. Case C: 15.5m radius, vehicle speed= 2m/s
4. Case D: 20.3m radius, vehicle speed= 2m/s
5. Case E: 31m radius, vehicle speed= 2m/s

11. Terrestrial Platform
• Resource used: SONY DSC W310
• For capturing occluded parts in aerial images
• Images taken parallel on a identical base line
Few terrestrial Images captured
Photo taken by: Kushal K.CRepresentation of terrestrial image acquisition

12. Field measurement
• Resource used: measuring tape
• Measurement across 3 dimensions of the temple
• Measurements of features like doors
• For validation of final outcome
Photo taken during Field measurement

13. CAD PLAN

14. Image Processing
• Resource used: Pix4D mapper software
• Pix4D converts 2D images into 3D point cloud
• Pix4D able to compute precise camera calibration and relative model
from image contents
• Image selection
• Processing initialized with uploading EXIF file
• 3 main processing steps
1. Initial Processing
• Generate keypoints and compute matches
• Camera calibration and Exterior orientation
EXIF file of an image
Source: https://www.pix4d.com/

15. • Key point generation and matching by SIFT
• SIFT (Scale Invariant Feature Transform) workflow:
1. Scale space extrema detection
2. Keypoint localization and filtering
3. Orientation assignment
4. Calculation of descriptor
5. Keypoint matching
• Bundle block adjustment and triangulation to 3D point
• Densification with PMVS
• Mesh generation
• Texturing
Point cloud during processing
Textured model

16. File Uploading

17. Initial Processing
Compute Key points
Compute Matches
Add matches
Optimize with geo
information
Calibrate Images
Analyze tie-points

18. Point Cloud and Mesh Generation
Generate Points
Create Triangle Mesh
Final texture 3D model
Generate textured mesh
Segmentation of mesh
Improve mesh

19. Overall Reconstruction
• Merging existing project
• UAV images have geotags, no GPS information in terrestrial data sets
• Same coordinate system using overlapping images and manual tie
points
• In Manual tie points, Name and position of tie-points were same
• After merging, connected with one another embedded in same
coordinate system of WGS 84

20. Photo: Five Manual Tie-
points defined
Photo: Camera Alignment
Photo: Tie-point merging

21. Result
• A 3D textured model giving the realistic perception of Ugratirtha
Temple is generated using 243 calibrated images
• The total number of 2D keypoints observation for bundle block
adjustment is 4301614
• The number of 3D points for bundle block adjustment is 1685228

22. Accuracy Assessment
Length (X) Breadth (Y) Height (Z) Remarks
Field
Measurem
ent
Pix4D Field
Measureme
nt
Pix4D Field
Measureme
nt
Pix4D
5.620 5.680 5.620 5.670 1.450 1.470 Length and
breadth is of
main
structure,
height up to
a marked
line
Change in X = 6 cm = 1.068%
Change in Y = 5 cm= 0.890%
Change in Z = 2 cm= 1.379%
RMS Error = ((62 + 52
+ 22
)/3) = 4.655%
This shows that our outcome has considerable amount of error
X
Z
Y

23. Difficulties Encountered
• Site selection considering the permission issues.
• Sunlight direction and shadowing problem.
• Uncalibration of terrestrial images initially.
• Unwanted additional points generated.
• Limited resource for processing
• Permission letter from University submitted to the preservation
committee.
• Images taken in multiple days during different durations of a day.
• Images taken with different camera in terrestrial platform.
• Refining the point using clipping tool.
• Personal laptops were used
Solutions

24. Conclusion
• Our project of 3D modeling a cultural heritage site has been
completed
• UAV and terrestrial consumer grade cameras as a source of the data
acquisition
• Image datasets were processed in Pix4D mapper software allowing us
to create projects by merging already computed sub projects
• we assured the accuracy of the final product relating it to the
measured field data

25. Recommendations
• Other image processing software’s like Agisoft photoscan, Visual SFM,
Arc 3D, 3DM Analyst, 123D catch etc. can be used to process the
images and produce different photogrammetric products.
• The quality of the final 3D model can be improved by using high
resolution cameras, more number of overlapping images
• High processing capacity computers should be used for the fast and
efficient processing and merging.
• Indoor mapping can also be done.

26. References
• Moenning, Carsten, and Neil A. Dodgson. "A new point cloud simplification
algorithm." Proc. Int. Conf. on Visualization, Imaging and Image Processing. 2003.
• Juan, Luo, and Oubong Gwun. "A comparison of sift, pca-sift and
surf."International Journal of Image Processing (IJIP) 3.4 (2009): 143-152.
• Muja, Marius, and David G. Lowe. "Fast Approximate Nearest Neighbors with
Automatic Algorithm Configuration." VISAPP (1) 2 (2009).
• Srivastava, Vikram, and Prashant Goyal. "An Efficient Image Identification
Algorithm using Scale Invariant Feature Detection." (2007).
• Lowe, David G. "Object recognition from local scale-invariant features."Computer
vision, 1999. The proceedings of the seventh IEEE international conference on.
Vol. 2. Ieee, 1999.
• Lowe, David. "SIFT: SCALE INVARIANT FEATURE TRANSFORM BY."International
Journal of Computer Vision 2 (1999).

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accessed on jan17,2016
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jan16,2016
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2016
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URL10: http://www.cs.berkeley.edu/~jrs/mesh/ accessed on jan13,2016

28. THANK YOU !!!