SyntheT-Less

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Official repository for the SyntheT-Less dataset

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# SyntheT-Less
Official repository for the SyntheT-Less dataset

used in *On Object Symmetries and 6D Pose Estimation from Images*, G. Pitteri\*, [M. Ramamonjisoa](https://michaelramamonjisoa.github.io)\*, S. Ilic and V. Lepetit

![alt_text](https://raw.githubusercontent.com/MichaelRamamonjisoa/SyntheT-Less/master/animated_samples.gif)

# Download our data
The data is available [here](https://drive.google.com/file/d/1abMrFVqa3e39q0hBUD5jikAAIx2wmn0K/view?usp=sharing) (9.6GB zip file).

# Dataset structure
Here is how the dataset is organised:

```
multi_objects/
|__images
    |__ img_xxxxx.png
|__contours/
    |__ xxxxx.png
|__depth/
    |__ xxxxx.png                         
|__normals
    |__ xxxxx.png
|__mask
    |__ xxxxx.png
|__instances
    |__ xxxxx.png --> uint8 image containing object labels from 1 to 9 (background pixels are annotated with 0) 
|__gt_poses/
    |__ tmp_xxxxx.json --> contains a dictionnary of object poses with keys annotated from 0 to 8 
                           (There is a 1-shift from object instances indices, sorry about that!)
        {
         "0": {"Euler": [theta_x, theta_y, theta_z],
               "T": [Tx, Ty, Tz],
               "type": object type from T-LESS (from 1 to 30)},              
         ...
                  
         "8": {"Euler": [theta_x, theta_y, theta_z],
               "T": [Tx, Ty, Tz],
               "type": object type from T-LESS (from 1 to 30)},
         "Lamp": {"phi": lamp_phi, 
                  "theta": lamp_theta,
                  "strength": lamp_strength}
	  "Ambient Light": ambient occlusion level (0 to 1)
	  "Table": {"distance": distance in meters,
		    "Euler": [theta_table_x, theta_table_y, theta_table_z]}
         }

```

# Data Generation

## Requirements
### 1. Download the DTD dataset
Download the [Describable Textures Dataset (DTD)](https://www.robots.ox.ac.uk/~vgg/data/dtd/) and set its folder using:

```
cd DTD && \
wget https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz && \
tar -xf dtd-r1.0.1.tar.gz --strip-components=1 && cd .. &&\ 
rm -rf DTD/dtd-r1.0.1.tar.gz
```

### 2. Download the T-LESS CAD models
Download the [T-LESS](http://cmp.felk.cvut.cz/t-less/index.html) CAD models:
```
wget http://ptak.felk.cvut.cz/darwin/t-less/v2/t-less_v2_models_cad.zip && \
unzip t-less_v2_models_cad.zip && \  
rm -rf t-less_v2_models_cad.zip
```

### 3. Install Blender
Download [Blender](https://www.blender.org/download/Blender2.80/blender-2.80-linux-glibc217-x86_64.tar.bz2)
 and save it to the *Blender/* path. using the following commands (skip this step if you have previously installed Blender).

```
mv ABSOLUTE_PATH_WHERE_FILE_WAS_DOWNLOADED.tar.bz2 ROOT_OF_THIS_DIRECTORY 
bzip2 -d blender-2.80-linux-glibc217-x86_64.tar.bz2
mkdir blender && tar -xf blender-2.80-linux-glibc217-x86_64.tar -C blender/ --strip-components=1
rm -rf blender-2.80-linux-glibc217-x86_64.tar
```

## Run the generation code

Make sure you have installed the following requirements:
- [Blender](https://www.blender.org/download/Blender2.80/blender-2.80-linux-glibc217-x86_64.tar.bz2/) (in previous step)
- OpenCV
- OpenEXR 
- imageio 
- skimage 
- multiprocessing 
- imath

Most of them can be installed using *pip install "package_name"*

Then run:
```
python3 call_blender_multi.py --blender_path $BLENDER_PATH \
			      --dtd-rootdir DTD \
			      --models_path models_cad\
			      --cpus $NUM_CPUS --size $DATASET_SIZE --cuda $CUDA_DEVICE
```

Variables:

- $BLENDER_PATH: Path to the directory containing your Blender executable (use blender/ if installed with step 3)
- $NUM_CPUS: Number of processes to run data generation in parallel. We recommand setting it to your machine CPUs number.  
- $DATASET_SIZE: Aimed dataset size
- $CUDA_DEVICE: GPU ID for data generation

Call **python3 call_blender_multi.py --help** for help on other parameters.

# Citation
If you use our data generation code or already generated data, please cite our paper:

```
@article{pitteri2019threedv, 
 Title = {On Object Symmetries and 6D Pose Estimation from Images}, 
 Author = {G. Pitteri and M. Ramamonjisoa and S. Ilic and V. Lepetit}, 
 Journal = {International Conference on 3D Vision}, 
 Year = {2019}
 }
```

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