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”

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

OBJECTIVESThe 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

Project Site• Name: Ugratirtha Mahadev Temple• Location: Budol, Banepa Municipality, Kavre• Archaeologically important

Source: Google EarthAccessed on: 20th Jan, 2016

Image: Project Site,Captured by DJI on 2nd Jan!

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

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.

Image: Compass Calibration done by Srijit SharmaPhoto taken by: Kushal KC

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 Mission2. Point of Interest

DJI Phantom and the site

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 images3. Flight3: 93 images

Nadir image taken by DJI Phantom3

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/s2. Case B: 10m radius, vehicle speed= 2m/s3. Case C: 15.5m radius, vehicle speed= 2m/s4. Case D: 20.3m radius, vehicle speed= 2m/s5. Case E: 31m radius, vehicle speed= 2m/s

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 capturedPhoto taken by: Kushal K.C Representation of terrestrial image acquisition

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

CAD PLAN

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/

• Key point generation and matching by SIFT• SIFT (Scale Invariant Feature Transform) workflow:

1. Scale space extrema detection2. Keypoint localization and filtering3. Orientation assignment4. Calculation of descriptor5. Keypoint matching

• Bundle block adjustment and triangulation to 3D point• Densification with PMVS• Mesh generation• Texturing

Point cloud during processing

Textured model

File Uploading

Initial ProcessingCompute Key points

Compute Matches

Add matches

Optimize with geo information

Calibrate Images

Analyze tie-points

Point Cloud and Mesh GenerationGenerate Points

Create Triangle Mesh

Final texture 3D model

Generate textured mesh

Segmentation of mesh

Improve mesh

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

Photo: Five Manual Tie-points defined

Photo: Camera Alignment

Photo: Tie-point merging

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

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 = = 4.655%This shows that our outcome has considerable amount of error

X

Z

Y

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

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

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.

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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