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MARKLESS REGISTRATION FOR SCANS OF FREE-FORM OBJECTS  Laboratory of photogrammetry of NTUAArtemis Valanis, PhD StudentCharalambos Ioannidis, Professor
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
Motivation Initial state Front view Side view
Motivation Result of ICP - no prior processing Front view Side view
Related Literature Various approaches for automatic ICP initialization: Bae & Lichti, 2004	Geometric primitives Gelfand, 2005		Feature points Hansen, 2006		Plane-matching Makadia, 2006		Extended Gaussian Images Biswas, 2006		Isosurfaces
Example Objects Bae & Lichti, 2004	Geometric primitives  Gelfand, 2005	Feature points  Hansen, 2006		Plane-matching Makadia, 2006	Extended Gaussian Images Biswas, 2006		Isosurfaces
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
Worked cases
Worked cases
Equipment used HDS2500 FOV 40ox40o spot size = 6mm  position accuracy = ±6mm (50m range)
Key Idea Y Y ω Z Z X X Y Z X Acquisition scenario
Key Idea Y Y Y Y Z ω ω X Z X Z Z X X Acquisition scenario Acquired data Proposed approach
Initial state Front view Side view
Result of ICP combined with the proposed method Front view Front view Side view
Proposed algorithm Data imported: 2 scans acquired either by rotating the scan head  vertically (ω angle) or horizontally (φ angle) Process: 	The space of the unknown parameter (ω or φ 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
Sampling process 1/2 If the unknown rotation is ω The ω is given an initial value 0 that is increased by 5g in every loop For every ω 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
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
Method Validation 2 scans acquired by different ω angle 5 targets used to evaluate the results Algorithm implemented in Matlab  Calculation of the unknown transform in Cyclone and in Matlab
Initial State
Target distances as calculated for the original scans
Results of the sampling process
Results after the approximate alignment
Results after the approximate alignment
Result of ICP after the application of the proposed algorithm
Results of ICP after the application of the proposed algorithm
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)
Accuracy evaluation for 2 scan-pairs
Accuracy evaluation for a scan-triplet
Registration results
3D surface model
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
Thank you for your attention!

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Markless registration for scans of free form objects

  • 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, 2004 Geometric primitives Gelfand, 2005 Feature points Hansen, 2006 Plane-matching Makadia, 2006 Extended Gaussian Images Biswas, 2006 Isosurfaces
  • 6. Example Objects Bae & Lichti, 2004 Geometric primitives Gelfand, 2005 Feature points Hansen, 2006 Plane-matching Makadia, 2006 Extended Gaussian Images Biswas, 2006 Isosurfaces
  • 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
  • 10. Equipment used HDS2500 FOV 40ox40o spot size = 6mm position accuracy = ±6mm (50m range)
  • 11. Key Idea Y Y ω Z Z X X Y Z X Acquisition scenario
  • 12. Key Idea Y Y Y Y Z ω ω 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 head vertically (ω angle) or horizontally (φ angle) Process: The space of the unknown parameter (ω or φ 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 ω The ω is given an initial value 0 that is increased by 5g in every loop For every ω 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 ω angle 5 targets used to evaluate the results Algorithm implemented in Matlab Calculation of the unknown transform in Cyclone and in Matlab
  • 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
  • 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!