Downloading and preparing the data

All data used (datasets, models, results, ...) are stored in a directory $HAPPYPOSE_DATA_DIR that you created in the Readsme. We provide the utilities for downloading required data and models. All of the files can also be downloaded manually.

BOP Datasets

For both T-LESS and YCB-Video, we use the datasets in the BOP format. If you already have them on your disk, place them in $HAPPYPOSE_DATA_DIR/bop_datasets. Alternatively, you can download it using :

python -m happypose.toolbox.utils.download --bop_dataset ycbv tless

Additional files that contain information about the datasets used to fairly compare with prior works on both datasets.

python -m happypose.toolbox.utils.download --bop_extra_files ycbv tless

We use pybullet for rendering images which requires object models to be provided in the URDF format. We provide converted URDF files, they can be downloaded using:

python -m happypose.toolbox.utils.download --urdf_models ycbv tless.cad

In the BOP format, the YCB objects 002_master_chef_can and 040_large_marker are considered symmetric, but not by previous works such as PoseCNN, PVNet and DeepIM. To ensure a fair comparison (using ADD instead of ADD-S for ADD-(S) for these objects), these objects must not be considered symmetric in the evaluation. To keep the uniformity of the models format, we generate a set of YCB objects models_bop-compat_eval that can be used to fairly compare our approach against previous works. You can download them directly:

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

Notes:

The URDF files were obtained using these commands (requires meshlab to be installed):

python -m happypose.pose_estimators.cosypose.cosypose.scripts.convert_bop_ds_to_urdf --ds_name=ycbv
python -m happypose.pose_estimators.cosypose.cosypose.scripts.convert_bop_ds_to_urdf --ds_name=tless.cad

Compatibility models were obtained using the following script:

python -m happypose.pose_estimators.cosypose.cosypose.scripts.make_ycbv_compat_models

Models for minimal version

# hope
python -m happypose.toolbox.utils.download --cosypose_models \
          detector-bop-hope-pbr--15246 \
          coarse-bop-hope-pbr--225203 \
          refiner-bop-hope-pbr--955392

# ycbv
python -m happypose.toolbox.utils.download --cosypose_models \
          detector-bop-ycbv-pbr--970850 \
          coarse-bop-ycbv-pbr--724183 \
          refiner-bop-ycbv-pbr--604090

# tless
python -m happypose.toolbox.utils.download --cosypose_models \
          detector-bop-tless-pbr--873074 \
          coarse-bop-tless-pbr--506801 \
          refiner-bop-tless-pbr--233420

Pre-trained models for single-view estimator

The pre-trained models of the single-view pose estimator can be downloaded using:

# YCB-V Single-view refiner
python -m happypose.toolbox.utils.download --cosypose_models ycbv-refiner-finetune--251020

# YCB-V Single-view refiner trained on synthetic data only
# Only download this if you are interested in retraining the above model
python -m happypose.toolbox.utils.download --cosypose_models ycbv-refiner-syntonly--596719

# T-LESS coarse and refiner models
python -m happypose.toolbox.utils.download --cosypose_models tless-coarse--10219 tless-refiner--585928

2D detections

To ensure a fair comparison with prior works on both datasets, we use the same detections as DeepIM (from PoseCNN) on YCB-Video and the same as Pix2pose (from a RetinaNet model) on T-LESS. Download the saved 2D detections for both datasets using

python -m happypose.toolbox.utils.download --detections ycbv_posecnn

# SiSo detections: 1 detection with highest per score per class per image on all images
# Available for each image of the T-LESS dataset (primesense sensor)
# These are the same detections as used in Pix2pose's experiments
python -m happypose.toolbox.utils.download --detections tless_pix2pose_retinanet_siso_top1

# ViVo detections: All detections for a subset of 1000 images of T-LESS.
# Used in our multi-view experiments.
python -m happypose.toolbox.utils.download --detections tless_pix2pose_retinanet_vivo_all

If you are interested in re-training a detector, please see the BOP 2020 section.