1.
Implementation of a Self-Consistent Stereo Processing Chain for 3D Stereo Reconstruction of the Lunar Surface
E. Tasdelen1, H. Unbekannt1, M. Yildirim1, K. Willner1 and J. Oberst1,2
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 processing of 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 Matching Software
Matching Software
Overview of the software
Matching Software
Stereo Images
Parameters
TP File
Supports multithreading
Improved performance
Memory management for large images
Image formats
Vicar, ISIS cube, TIFF

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

5. Projective transformation
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
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. Matching Types Type3: Grid-based matching
Matching Types
Type3: Grid-based matching
Grid-based projective transformation
GRIDDING

8. Blunder Detection The main reasons of blunders Filters
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 49750593 correspondences LRO NAC Images for Copernicus Crater
150PX
correspondences
-500PX
1km
LRO NAC Images for Copernicus Crater
Resulting Disparity Map

11. 3D Point Calculation Forward Ray Intersection
Computation of spatial object coordinates X 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. 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.
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

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. Surface Representation Simplification
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. Landing Module km N

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

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

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

24. correlation coefficient
Matching Algorithms
NCC:
Maximum
Correlation
1.0
Threshold = 0.8
correlation coefficient
0.5
0.0
correlation position
Problems:
NCC is not defined for homogeneous image areas(variance is zero)!
NCC is not invariant to geometrical distortions
Pixel accuracy!

25. Visualization Tool