Download example data for minimal testing

cd $HAPPYPOSE_DATA_DIR
wget https://memmo-data.laas.fr/static/examples.tar.xz
tar xf examples.tar.xz

Download pre-trained pose estimation models

Download pose estimation models to $HAPPYPOSE_DATA_DIR/megapose-models:

python -m happypose.toolbox.utils.download --megapose_models

Download pre-trained detection models

Megapose can use pretrained detectors from CosyPose, which can be downloaded to $HAPPYPOSE_DATA_DIR/experiments:

# hope
python -m happypose.toolbox.utils.download --cosypose_models detector-bop-hope-pbr--15246
# ycbv
python -m happypose.toolbox.utils.download --cosypose_models detector-bop-ycbv-pbr--970850
# tless
python -m happypose.toolbox.utils.download --cosypose_models detector-bop-tless-pbr--873074

Dataset

Dataset information

The dataset is available at this url. It is split into two datasets: gso_1M (Google Scanned Objects) and shapenet_1M (ShapeNet objects). Each dataset has 1 million images which were generated using BlenderProc.

Datasets are released in the webdataset format for high reading performance. Each dataset is split into chunks of size ~600MB containing 1000 images each.

We provide the pre-processed meshes ready to be used for rendering and training in this directory:

google_scanned_objects.zip
shapenetcorev2.zip

Important: Before downloading this data, please make sure you are allowed to use these datasets i.e. you can download the original ones.

Usage

We provide utilies for loading and visualizing the data.

The following commands download 10 chunks of each dataset as well as metadatas:

cd $HAPPYPOSE_DATA_DIR
rclone copyto megapose_public_readonly:/webdatasets/ webdatasets/ --include "0000000*.tar" --include "*.json" --include "*.feather" --config $HAPPYPOSE_DATA_DIR/rclone.conf -P

We then download the object models (please make sure you have access to the original datasets before downloading these preprocessed ones):

cd $HAPPYPOSE_DATA_DIR
rclone copyto megapose_public_readonly:/tars tars/ --include "shapenetcorev2.zip" --include "google_scanned_objects.zip" --config $HAPPYPOSE_DATA_DIR/rclone.conf -P
unzip tars/shapenetcorev2.zip
unzip tars/google_scanned_objects.zip

Your directory structure should look like this:

$HAPPYPOSE_DATA_DIR/
    webdatasets/
        gso_1M/
            infos.json
            frame_index.feather
            00000001.tar
            ...
        shapenet_1M/
            infos.json
            frame_index.feather
            00000001.tar
            ...
    shapenetcorev2/
        ...
    googlescannedobjects/
        ...

You can then use the render_megapose_dataset.ipynb notebook to load and visualize the data and 6D pose annotations.