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

Technische Universität Berlin

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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 Berlin2 German Aerospace Center (DLR)

2 E. Tasdelen – Stereo Processing Chain

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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.

ImageMatching

3D Point Calculation

DTMInterpolation Visualization


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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

Matching Software

Stereo Images

Parameters

TP File


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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)


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Matching Algorithms

Reference Patch Compared Patches

Least-Squares Matching (LSM):

source: Bethmann et al., 2010

Functional Model:

f(x,y) + e(x,y) = g(x’,y’)

Transformation Model:

x = a0 + a1x’ + a2y’

y = b0 + b1x’ + b2y’

a0 + a1x’ + a2y’1 + c1x’ + c2y’

x =

b0 + b1x’ + b2y’1 + c1x’ + c2y’

y =

Projective transformation


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Matching Types

Type1: Matching images without pre-processing Same search space for each pixel

Type2: Coarse-to-fine hierarchical matchingResults from the pyramids override the search space boundaries


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Matching Types

Type3: Grid-based matchingGrid-based projective transformation

GRIDDING


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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 RelationEpipolar Error Check


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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 di erent sizes on the left ffimage (300, 200 and 100 from d to f, respectively)


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LRO NAC Images for Copernicus Crater Resulting Disparity Map

49750593correspondences

1km

-500PX

150PX


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3D Point Calculation

Forward Ray IntersectionComputation of spatial object

coordinates X from measured image points x and x’ as well as the camera matrices P and P’.

source: Rodehorst, 2004


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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


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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.


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DTM Interpolation

1: X Y Z2: X Y Z3: X Y Z4: X Y Z5: X Y Z

[...]n: X Y Z

Conversion: from

3D Coordinates (Body-centric)

toMap Coordinates

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.


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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


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Visualization Tool

Level Of Detail (LOD) AlgorithmDecreasing the complexity of the object

with the increasing distance to the viewer

source: Bekiaris, 2009


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Visualization Tool

Surface Representation Simplification

Level Of Detail (LOD) AlgorithmBased 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


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Visualization Tool

LOD 1

Viewer

LOD 2

LOD 0 Representation

Rendering wrt. viewing direction


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Landing Module

72.195 km


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Landing Module

~1000m

@Landing ModuleThe position of Apollo 17 landing module


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


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

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

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