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,
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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)<br>
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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.<br>
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Matching Software Overview of the software Supports multithreading
Improved performance
Memory management for large images
Image formats
Vicar, ISIS cube, TIFF<br>
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Matching Algorithms Reference Image Search Image where is covariance
are variances Area-based Matching (ABM) source: Rodehorst, 2004 Normalized Cross-Correlation (NCC)<br>
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<br>
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 Relation Epipolar Error Check<br>
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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)<br>
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<br>
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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<br>
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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.<br>
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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.<br>
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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 Visualization Tool [1] source: Wang, 2012<br>
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Visualization Tool Level Of Detail (LOD) Algorithm
Decreasing the complexity of the object with the increasing distance to the viewer source: Bekiaris, 2009<br>
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Visualization Tool Surface Representation Simplification Level Of Detail (LOD) Algorithm
Based on Quad Trees Each child chunk represent a more detailed version of one of its parents quarters Each segment is called as a chunk source: Ulrich, 2002<br>
Landing Module ~1000m @Landing Module The position of Apollo 17 landing module<br>
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Landing Module ~1000m @Landing Module The position of Apollo 17 landing module<br>
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A look towards south from the position of Apollo 17 landing module<br>
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A look towards north from the position of Apollo 17 landing module<br>
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Matching Algorithms NCC: correlation coefficient correlation position 0.0 1.0 0.5 Maximum
Correlation Problems:
NCC is not defined for homogeneous image areas(variance is zero)!
NCC is not invariant to geometrical distortions
Pixel accuracy! Threshold = 0.8<br>
