1. MARKLESS REGISTRATION FOR SCANS OF FREE-FORM OBJECTS
Laboratory of photogrammetry of NTUAArtemis Valanis, PhD StudentCharalambos Ioannidis, Professor

2. Problem identification
Target:to initialize the ICP algorithm
in order to register partial scans
of uniform or free-form objects
Difficulty:no targets present
no characteristic points identifiable
in the area of overlap
3. Motivation
Initial state
Front view
Side view
4. Motivation
Result of ICP - no prior processing
Front view
Side view
5. Related Literature
Various approaches for automatic ICP initialization:
Bae & Lichti, 2004Geometric primitives
Gelfand, 2005Feature points
Hansen, 2006Plane-matching
Makadia, 2006Extended Gaussian Images
Biswas, 2006Isosurfaces
6. Example Objects
Bae & Lichti, 2004Geometric primitives
Gelfand, 2005Feature points
Hansen, 2006Plane-matching
Makadia, 2006Extended Gaussian Images
Biswas, 2006Isosurfaces
7. Proposed approach
Constrained acquisition process
Properly adjusted methods that:
Recover the relative transformation between two or more partial scans
Approximately align the point clouds
Enable the initialization of ICP
Achieve the optimal alignment of partial scans without the use of targetsor the identification of conjugate points
8. Worked cases
9. Worked cases
10. Equipment used
HDS2500
FOV 40ox40o
spot size = 6mm
position accuracy = 6mm (50m range)
11. Key Idea
Y
Y

Z
Z
X
X
Y
Z
X
Acquisition scenario
12. Key Idea
Y
Y
Y
Y
Z


X
Z
X
Z
Z
X
X
Acquisition scenario
Acquired data
Proposed approach
13. Initial state
Front view
Side view
14. Result of ICP combined with the proposed method
Front view
Front view
Side view
15. Proposed algorithm
Data imported:
2 scans acquired either by rotating the scan headvertically ( angle) or horizontally ( angle)
Process:
The space of the unknown parameter ( or angle) is sequentially sampled in order to obtain an approximation of the unknown angle. If the value of the evaluated measure is minimized then an approximate value is derived
16. Sampling process 1/2
If the unknown rotation is
The is given an initial value 0 that is increased by 5g in every loop
For every value, a rotation matrix is calculated and applied to the point-cloud that needs to be registered
After the transformation, the area of overlap between the reference and the moving scan is calculated and a rectangular grid is defined
17. Sampling process 2/2
The evaluated function i.e. the median of the distances of the two point clouds at the nodes of the grid along the Z direction, is derived based on 2D tesselations created for each point-cloud
Once the comparison measure reaches a minimum, the process is repeated at the respective interval with a step of 1g
When another minimum is detected, the final value is derived by a simple interpolation
18. Method Validation
2 scans acquired by different angle
5 targets used to evaluate the results
Algorithm implemented in Matlab
Calculation of the unknown transform in Cyclone and in Matlab
19. Initial State
20. Target distances as calculated for the original scans
21. Results of the sampling process
22. Results after the approximate alignment
23. Results after the approximate alignment
24. Result of ICP after the application of the proposed algorithm
25. Results of ICP after the application of the proposed algorithm
26. Application of the method for the monument of Zalongon
9 set-ups
14 scans in total
4 scans with no tagets
Back
3 set-ups
4 scans (2 single and a scan-pair)
Front
6 set-ups
10 scans (3 single, 2 scan-pairs and a scan-triplet)
27. Accuracy evaluation for 2 scan-pairs
28. Accuracy evaluation for a scan-triplet
29. Registration results
30. 3D surface model
31. Merits of the proposed approach
With minor modifications, it is as easily applied for horizontal rotations
Applicable also for sequences of scans acquired under the described conditions
Provides a solution in cases of serious space limitations
A non-elaborate and effective solution for all of those who have invested on similar equipment
32. Thank you for your attention!