E. Tasdelen 1 , H. Unbekannt 1 , M. Yildirim 1 , K. Willner 1 and J. Oberst 1,2 - PowerPoint PPT Presentation

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E. Tasdelen 1 , H. Unbekannt 1 , M. Yildirim 1 , K. Willner 1 and J. Oberst 1,2

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  1. E. Tasdelen1, H. Unbekannt1, M. Yildirim1, K. Willner1 and J. Oberst1,2 Implementation of a Self-Consistent Stereo Processing Chain for 3D Stereo Reconstruction of the Lunar Surface 1 Department of Geodesy and Geoinformation Science, Technical University of Berlin 2 German Aerospace Center (DLR)

  2. Motivation The department for Planetary Geodesy at TU Berlin is developing routines for photogrammetric processingof planetary image data to derive 3D representations of planetary surfaces. Aim: An independent generic 3D reconstruction pipeline Integrated Software for Imagers and Spectrometers(ISIS)developed by USGS Flagstaff, was chosen as a prime processing platform and tool kit. Image Matching 3D Point Calculation DTM Interpolation Visualization

  3. Matching Software Overview of the software Stereo Images Matching Software TP File Parameters • Supports multithreading • Improved performance • Memory management for large images • Image formats • Vicar, ISIS cube, TIFF

  4. Matching Algorithms Area-based Matching (ABM) source: Rodehorst, 2004 Reference Image Search Image • Normalized Cross-Correlation (NCC) • where is covariance are variances

  5. Matching Algorithms Least-Squares Matching (LSM): source: Bethmann et al., 2010 Reference Patch Compared Patches • Functional Model: Projective transformation f(x,y) + e(x,y) = g(x’,y’) • Transformation Model: a0 + a1x’ + a2y’ x = 1 + c1x’ + c2y’ x = a0 + a1x’ + a2y’ y = b0 + b1x’ + b2y’ b0 + b1x’ + b2y’ y = 1 + c1x’ + c2y’

  6. Matching Types • Type1:Matching images without pre-processing • Same search space for each pixel • Type2:Coarse-to-fine hierarchical matching • Results from the pyramids override the search space boundaries

  7. GRIDDING Matching Types • Type3:Grid-based matching • Grid-based projectivetransformation

  8. Blunder Detection • The main reasons of blunders • occlusions, depth discontinuities, repetitive patterns, inadequate texture, etc. • Filters • Epipolar Check: With the help of epipolar geometrical relation, all the matched points are controlled and the distances of the points to the corresponding epipolar lines are calculated. Points exceeding a set threshold distance to the epipolar line are discarded. Epipolar Error Check Epipolar Relation

  9. Blunder Detection • Overlapping Area Check: divide the reference image into regular sized grids and check if there are adequate numbers of tie-points within each grid. (a-b) left and right pair of stereo images, (c) actual overlapping area visualized on the left image, (d-f) grids with different sizes on the left image (300, 200 and 100 from d to f, respectively)

  10. N 150PX 49750593 correspondences -500PX 1km LRO NAC Images for Copernicus Crater Resulting Disparity Map

  11. 3D Point Calculation • Forward Ray Intersection • Computation of spatial object coordinatesX from measured image points x and x’ as well as the camera matrices P and P’. source: Rodehorst, 2004

  12. Blunder Detection • Filters on 3D point data • Octree Filter: uses octree data structure created from 3D point cloud data. • Nodes with low density, containing only few points, are considered as noise source: Wang, 2012

  13. Blunder Detection • Filters on 3D point data • Delaunay Triangles: Each point is connected by lines to its closest neighbors, in such a way • The points which contributes triangles with edge length exceeding a threshold indicates the possible outliers.

  14. 1: X Y Z 2: X Y Z 3: X Y Z 4: X Y Z 5: X Y Z [...] n: X Y Z Conversion: from 3D Coordinates (Body-centric) to Map Coordinates DTM Interpolation • 3D point coordinates are first map-projected to a grid based images • Colliding points are interpolated • IDW, nearest neighbor, mean or median • A customized search radius can be applied to define the pixel value.

  15. Visualization Tool • Main Challenges: • Rendering capabilities of graphics hardware • Limited to several millions of primitives per second • Geometry throughput effects the performance • Tremendous size of data does not fit into memory • Ex: 15km x 15km area with 1.5m res. > 5 GB of data, simply cannot be placed into memory at once [1] source: Wang, 2012

  16. Visualization Tool • Level Of Detail (LOD) Algorithm • Decreasing the complexity of the object with the increasing distance to the viewer source: Bekiaris, 2009

  17. Visualization Tool • Level Of Detail (LOD) Algorithm • Based on Quad Trees Each segment is called as a chunk source: Ulrich, 2002 Surface Representation Simplification • Each child chunk represent a more detailed version of one of its parents quarters

  18. Visualization Tool • Rendering wrt. viewing direction LOD 2 LOD 1 Viewer LOD 0 Representation

  19. N Landing Module 72.195 km

  20. N Landing Module ~1000m The position of Apollo 17 landing module @Landing Module

  21. N Landing Module ~1000m The position of Apollo 17 landing module @Landing Module

  22. N A look towards south from the position of Apollo 17 landing module

  23. N A look towards north from the position of Apollo 17 landing module