# Zhenjue tutorial : Différence entre versions

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Once you have download it, you have to UnZip the ".zip" archive.

## Description

This dataset contain images with different focal legnth (24 and 100 mm). The purpose of this tutorial is to reconstruct each statue idependently (warrior and musician). We will show two methods to reconstruct object in 3D by image geometry (Malt and PIMs).

## Tutorial

### 1. Relative orientation

#### Compute tie points

As all MicMac process, the pipeline begin by calling the tool Tapioca :

mm3d Tapioca All  ".*JPG" 1500

#### Compute relative orientation for images with specified focal length

For this dataset, image have different focal length, so we have to compute first a orientation for the 24mm focal length images.

mm3d Tapas RadialStd ".*JPG" Focs=[20,30] Out=F24

Check residual and number of points used per images.

#### Compute relative orientation for all images

We use the 24mm orientation as an entry for our command in order to indicate to MicMac there is different focal length :

mm3d Tapas RadialStd ".*JPG" InOri=F24 Out=All

#### Generate a sparse cloud for relative orientation

We will now generate a sparse cloud to visualize the relative orientation.

mm3d AperiCloud ".*JPG" All

### 2. Dense correlation in image geometry (old method)

For this part and the rest of this tutorial, we will focus only on the warrior.

#### Create & edit a mask

Define a mask for dense correlation can be done with the command SaisieMasqQT. Here we define a image mask :

mm3d SaisieMasqQT "DSC_3128.JPG"

#### Dense correlation

The previous tool for dense correlation was Malt. Here we are working in image geometry.

mm3d Malt GeomImage "DSC_313[2-9].JPG" All Master=DSC_3135.JPG ZoomF=4 AffineLast=0

We can compute a dense cloud with the command Nuage2Ply :

mm3d Nuage2Ply cd MM-Malt-Img-DSC_3128/NuageImProf_STD-MALT_Etape_6.xml Attr=../DSC_3135.JPG RatioAttrCarte=4 Out=../Warrior_Malt.ply

### 3. Dense correlation in image geometry (new method)

#### Create & edit a mask

Define a mask for dense correlation can be done with the command SaisieMasqQT. Here we define a 3D mask :

mm3d SaisieMasqQT "DSC_3128.JPG"

#### Dense correlation

The new tool C3DC doesn't need a image master for 3D reconstruction :

mm3d C3DC BigMac "DSC_313[2-9].JPG" All ZoomF=4

#### Comparison

So let's compare the files "Warrior_Malt.ply" and "C3DC-BigMac.ply".

### 4. Compute a depth map

cd MM-Malt-Img-DSC_3135
mm3d Nuage2Ply NuageImProf_STD-MALT_Etape_6.xml Attr=../DSC_3135.JPG RatioAttrCarte=4 Out=../3135.ply
mm3d GrShade Z_Num6_DeZoom4_STD-MALT.tif ModeOmbre=IgnE Mask=AutoMask_STD-MALT_Num_5.tif FZ=2 Out=../Shade3135.tif

### Go further

cd MM-Malt-Img-DSC_3128
mm3d GrShade Z_Num6_DeZoom4_STD-MALT.tif ModeOmbre=IgnE Mask=AutoMask_STD-MALT_Num_5.tif FZ=2 Out=../Shade3128.tif
mm3d Nuage2Ply NuageImProf_STD-MALT_Etape_6.xml Attr=../DSC_3128.JPG RatioAttrCarte=4 Out=../3128.ply