demo

1. Goal:
Evaluation of tools and method establishment for DTM from stereo
data
Sub goal-1: Evaluation of tools for DTM from stereo data
• All the available tools (10-15 in number)are to be analyzed and generate DTM for a
given cartosat-1 stereo data
• Literature Study report on “Evaluation of tools for DTM from stereo data”
Sub goal-2: Evaluation of method establishment for DTM from stereo data
• A method is to established up to generation of camera calibration for a given
cartosat-1 stereo data
• A method is to be established to generate DTM on a sample point cloud data.
• Literature Study report on “Evaluation of method establishment for DTM from stereo
data in different stages of implementation”
Tools used for generating Point Cloud through investigation
1. VisualSFM
2. Pix4D
3. IMAGINE Photogrammetry (LPS)
4. ContexCapture CENTER
5. Photomodeler
6. Agisoft Photoscan
7. Point Cloud Library
8. SURE
9. Bundler package, a Structure from Motion system with two stereo packages CMVS
and PMVS
10. OSM Bundler
11. Python Photogrammetry Toolbox (PPT
12. MeshLab
13. Cloud Compare
14. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) – simulation
to point cloud

2. Method for generating Point Clouds
1. 3D point cloud generation
Accurate stereo 3D point cloud generation suitable for multi-view stereo
reconstruction (VCIP 2014)
Steps followed in the paper Methodology used Paper references
Selection of Stereo Pair Quasi-Euclidean epipolar
rectification
A. Fusiello and L.Irsara,
Quasi-Euclidean epipolar
rectification of uncalibrated
images, Machine Vision and
Applications, vol. 22, pp. 663-
670, 2010.
Computation of Camera
Parameters
Structure-from-Motion (SfM)
approach (computing camera
parameters)
N. Snavely, S. Seitz, and R.
Szeliski, Modeling the world
from internet photo
collections, IJCV, vol. 80, pp.
189210, 2008
Estimation of Dense
Correspondence between the
stereo pair
DAISY descriptor matching
algorithm
E. Tola, V. Lepetit and P. Fua,
Daisy: an efcient dense
descriptor applied to wide
baseline stereo, PAMI, vol.
32, pp. 815-830, 2010.
Refinement of 3D point cloud Estimating the
correspondences in sub-pixel
accuracy
smoothing the resulting point
cloud using the moving least
squares algorithm
M. Levin, Mesh-independent
surface interpolation, GMSV,
SpringerVerlag, pp. 37-49,
2003.
2. A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote
Sensing Images
Steps followed in the paper Methodology used Paper references
PMVS point cloud generation Generation of Seed Point A Multi-View Dense Point Cloud
Generation Algorithm Based on
Low-Altitude Remote Sensing
Images
patch-based point cloud
expansion
Compute distance from an
image point
Expanded set of
reconstructred patches
point cloud optimization Nelder-Mead method
optimization method
Outliers filters Density Constraint

3. 3. Efficient Point Cloud Pre-processing using The Point Cloud Library
Steps followed in
the paper
Methodology used Paper references
point cloud creation
from disparity of
color image pairs
The PCL provides the
OrganisedConversion<>::conv
ert() method which uses the
disparity map, color image and
the focal length of the camera
to produce a point cloud
• First the input images
are loaded into memory
using OpenCV
• Converts them to
vectors that can be
passed as parameters to
the second stage
(Generation of Point
Cloud)
Efficient Point Cloud Pre-processing
using The Point Cloud Library
http://www.cscjournals.org/manuscript/J
ournals/IJIP/Volume10/Issue2/IJIP-
1063.pdf
voxel grid
downsample
filtering to simplify
point clouds
Helps to reduce the points in
Point Cloud
passthrough
filtering to adjust
the size of the point
cloud
Helps to removal of points
with in the specified range
4. Automatic rooftop segment extraction using point clouds generated from aerial high
resolution photography (SURE - Photogrammetric Surface Reconstruction from
Imagery)
Point clouds using stereo-matching for rooftop segmentation
Steps followed in the paper Methodology used Paper references
Feature Detection • Scale Invariant Feature
Transform or
• Speeded Up Robust
Features (SURF) or
• Gradient Location and
Orientation Histogram
(GLOH)
Bundle Adjustment Sparse Point Cloud B. Triggs, P. F. McLauchlan,

4. R. I. Hartley, and A. W.
Fitzgibbon, “Bundle
adjustment—a
modern synthesis,” in Vision
algorithms: theory and
practice. Springer,
2000, pp. 298–372.
Semi Global Matching Dense point Cloud
globally minimize matching
cost between two pixels and
the smoothness constraints
are
called global image matching
Analysis of tools to generate point Cloud /DSM/ DTM/DEM
Open source
Sno Tools To generate Point cloud
1 VisualSFM  Accepts only JPG format
 2-view & N-view 3D points(if we can
convert TIFF to JPG using ERDAS)
2 Python Photogrammetry
Toolbox (PPT)
 Accepts only JPG format
 At least 3 images
3 Pix4D discovery  Accepts TIFF , JPG also
 At least 3 images & Gives DSM also
sno Tools Point cloud to DSM
4 MeshLab  Accepts Point cloud in .PLY format
5 SAGA GIS  Accepts Point cloud in .XYZ format
6 ORFEO tool box  Generates DSM from stereo images
 It needs additional parameters
Commercial tools
sno Tools Status
(all these are working up to some extent need
to verify thoroughly)
7 Photomodeler  Point cloud
 DSM
 DTM
8 IMAGINE Photogrammetry  DSM

5. (LPS)  DTM
9 Pix4D mapper  Point cloud
 DSM
 DTM
10 Agisoft Photoscan  DSM
 DTM
11 SURE  DSM
 DTM
12 Correlator 3D  Point cloud
 DSM
 DTM
12 ContextCapture CENTER  Point cloud
 DSM

6. (LPS)  DTM
9 Pix4D mapper  Point cloud
 DSM
 DTM
10 Agisoft Photoscan  DSM
 DTM
11 SURE  DSM
 DTM
12 Correlator 3D  Point cloud
 DSM
 DTM
12 ContextCapture CENTER  Point cloud
 DSM