Gravillons tutorial

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Révision de 4 février 2016 à 10:48 par BorisLeroux (discussion | contributions) (3 Visualize Relative Orientation)

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Presentation

This dataset was created by L.Girod in Norway. This dataset was acquired to modelise a volcanoes model realised by O.Galland. File present in the directory are :

  • 4 images : 1.JPG, 2.JPG, 3.JPG, 4.JPG.
  • GCPs coordinates : Dico-Appuis.xml.
  • Mesure of GCPs in images : Mesure-Appuis.xml.
  • 1 Mask : 1_Masq.tif/1_Masq.xml
  • 2 scripts de commandes : gravillons.sh (Linux) et gravillons.bat (Windows)

During this tutorial, we will approach general concepts, for more details, go further in tutorials or directly in commands pages.

Tutorial

1 Tie-Points research

The first step of each MicMac uses is to determine images which are looking at the same scene, this step is call image matching and performed by the command Tapioca :

mm3d Tapioca MulScale ".*.JPG" 500 1500

The MulScale option is generally faster, because it match images at high resolution (1500px here) only between images which are matching at low resolution (500px here).

2 Internal Orientation+Relative Orientation

Photogrammetry is composed of three steps :

  • Internal Orientation : which consist to determine camera's paremeter (focal length, PPA, PPS, distorsion center, or distorsion parameters).
  • Relative Orientation : which consist to determine position of each camera's from each other in an arbitrary system.
  • Absolute Orientation : which consist to bascule the relative orientation to a scaled and oriented system (typically WGS84)

In digital photogrammetry, the two first steps are generally processed at the same time. In MicMac, the tools which perform internal and relative orientation is call Tapas :

mm3d Tapas FraserBasic ".*.JPG" Out=Arbitrary

This tools use a compensation by least squares to determine camera's parameter and relative orientation. The option "FraserBasic", correspond to a model of disorsion for our camera. The "option" Out specify the name of the orientation directory (here it will be Ori-Arbitrary).

3 Visualize Relative Orientation

MicMac include a tools which create a sparse point cloud (TPs) for visualization. This tool is AperiCloud :

mm3d AperiCloud ".*.JPG" Arbitrary

After this step, a ".ply" file will appear in your working directory, open it with Meshlab (see Useful softwares for MicMac)

4 Absolute Orientation

For this datasets, Ground Control Points, are already measured in images (file "Mesure-Appuis.xml"). With 3 points (X,Y,Z) we can determine the 3D transformation between the arbitrary system (Relative Orientation) and the georeferenced system, this operation is call "Bascule" and can be performed by the command GCPBascule :

mm3d GCPBascule ".*.JPG" Arbitrary Ground_Init Dico-Appuis.xml Mesure-Appuis.xml

This tool process a first Bascule only with the GCPs (Directory Ori-Ground_Init), we will now calculate a second Bascule with GCPs and TPs. To do that, we use the command Campari :

mm3d Campari" ".*.JPG" Ground_Init Ground

The new orientation is stocked in the directory "Ori-Ground". We can visualize it with AperiCloud :

mm3d AperiCloud ".*JPG" Ground

You can visualize the points cloud created in Meshlab.

5 Create a Dense Points Cloud

With any orientation directory, you can generate a dense point cloud. The method which consist to use the content of all images to create a 3D model is call dense correlation or densification. In MicMac, it's performed by the command Malt :

mm3d Malt GeomImage ".*.JPG" Ground Master="1.JPG" ZoomF=2

This last tool doesn't create directly a 3D model, to generate it, you have to run an other tools Nuage2Ply :

mm3d Nuage2Ply" "MM-Malt-Img-1/NuageImProf_STD-MALT_Etape_7.xml" Attr="1.JPG" Out=1.ply RatioAttrCarte=2

Then visualize the 3D model "1.ply" in Meshlab.

Conclusion

With this tutorial, you have realised a complete photogrammetric process with MicMac. This the first tutorial was willingly easy, to start rapidly with MicMac. If you want to go further, you can try the next tutorials.