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OMAD: Object Model with Articulated Deformations

This repository is the official implementation of the paper OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval. This paper has been accepted to BMVC 2021.

Overview Visulization

Datasets

ArtImage dataset contains the synthetic images generated from Unity along with the following annotations:

  • RGB image
  • depth map
  • part mask
  • part pose

This dataset also contains URDF articulated object models of five categories from PartNet-Mobility, which is re-annotated by us to align the rest state in the same category.

Usage

Installation

Environments:

  • Python >= 3.7
  • CUDA >= 10.0
git clone https://github.com/xiaoxiaoxh/OMAD.git
cd OMAD

Install the dependencies listed in requirements.txt

pip install -r requirements.txt

Then, compile CUDA module - index_max:

cd models/index_max_ext
python setup.py install

Finally, download ArtImage Dataset and put it in OMAD/data folder.

Now you are ready to go!

Training of OMAD-PriorNet

python train_omad_priornet.py --num_kp  24  --work_dir  work_dir/omad_priornet_laptop  --category 1 --num_parts 2  --use_relative_coverage  --symtype shape

Testing of OMAD-PriorNet

python test_omad_priornet.py --num_kp  24 --checkpoint  model_current_laptop.pth  --work_dir  work_dir/omad_priornet_laptop  --bs  16  --workers  0  --use_gpu  --symtype shape --out  --mode train

python test_omad_priornet.py --num_kp  24 --checkpoint  model_current_laptop.pth  --work_dir  work_dir/omad_priornet_laptop  --bs  16  --workers  0  --use_gpu  --symtype shape --out  --mode val

Training of OMADNet

python  train_omad_net.py --num_kp 24  --work_dir  work_dir/omad-net_laptop  --params_dir  work_dir/omad_priornet_laptop  --num_basis  10  --symtype shape

Testing of OMADNet

python test_omad_net.py  --num_kp 24 --checkpoint model_current_laptop.pth --work_dir work_dir/omad-net_laptop   --params_dir work_dir/omad_priornet_laptop  --category 1 --num_basis 10 --num_parts 2 --symtype shape --kp_thr 0.1 --reg_weight 0  --out raw_results.pkl --num_process 8 --use_gpu  --data_postfix final_test --shuffle