Technische Universität Berlin 10/10/12 Implementation of a Self-Consistent Stereo Processing Chain...

Technische Universität Berlin

10/10/12

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

10/10/12

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


10/10/12

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


10/10/12

Matching Algorithms

Reference Image Search Image

• where is covariance

are variances

Area-based Matching (ABM)

source: Rodehorst, 2004

Normalized Cross-Correlation (NCC)


10/10/12

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


10/10/12

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


10/10/12

Matching Types

Type3: Grid-based matchingGrid-based projective transformation

GRIDDING


10/10/12

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


10/10/12

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)


10/10/12

LRO NAC Images for Copernicus Crater Resulting Disparity Map

49750593correspondences

1km

-500PX

150PX


10/10/12

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


10/10/12

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 noisesource: Wang, 2012


10/10/12

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.


10/10/12

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.


10/10/12

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


10/10/12

Visualization Tool

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

with the increasing distance to the

viewer

source: Bekiaris, 2009


10/10/12

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


10/10/12

Visualization Tool

LOD 1

Viewer

LOD 2

LOD 0 Representation

Rendering wrt. viewing direction


10/10/12

Landing Module

72.195 km


10/10/12

Landing Module

~1000m

@Landing ModuleThe position of Apollo 17 landing module


10/10/12

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


10/10/12

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

Technische Universität Berlin 10/10/12 Implementation of a Self-Consistent Stereo Processing Chain...

Documents

Transcript of Technische Universität Berlin 10/10/12 Implementation of a Self-Consistent Stereo Processing Chain...