WaveLightNet: A Wavelet Decomposition Filter based CNN-LSTM Network for 6DOF Pose Estimation of Origami Robot

Adithya K Krishna, Seenivasan lalithkumar, Hongliang Ren

Wavelet Decomposition

Proposed Architecture

Datasets

Download the datasets from these repositories and place them under the main repository, path of Data and Linemod are added by default and can be used for training

Base Dataset(Used in SOTA & Referred in paper)

• https://gitlab.com/ruphan/origami-worm-pose-dataset
  

LINEMOD:

• https://cvarlab.icg.tugraz.at/projects/3d_object_detection/ObjRecPoseEst.tar.gz
  
• https://github.com/AdithyaK243/Linemod
  

Dataset(used in paper)

• https://github.com/AdithyaK243/Data

Directory setup

The structure of the repository is as follows:

• dataset/: Contains the data needed to train the network.
• checkpoints/: Contains trained weights for WavelightNet, ablation study, linemod .
• models/: Contains base CNN-LSTM network and Wavelet Feature Extraction codes.
• utils/: Contains utility tools used noise and occlusion study.
• loader: Data loader for training and testing for data used in paper.
• main : Main python file to run training and testing.

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Dependencies

• Python 3.7
• Tensorflow (2.x)
• PyWavelet (Wavelet Decomposition)

Training and Testing

Arguments

• lr
• epoch
• batch_size
• case : Boolean value to decide whether to add or remove Wavelet feature extraction (default:True)
• shuffle_data : Shuffle data while loading (default :False)
• shuffle_train: Shuffle while forming batches (default :False)
• test_model: Whether to carry out training or testing (default: False)
• model_name : ['Cnn', 'CnnLstm', 'Linemod'] one of the three names can be used (default: CnnLstm)
• chkpt_path: Path to checkpoint file when test_model is True

For Training:

python main.py --case --True --test_model False

For Testing:

python main.py --test_model True chkpt_path <path to h5 saved weights>

Results

Comparison with SOTA:

6-D	ScoopNet	Ours
Pose Vecor	Mean(cm)-Mean Dev	Mean(cm)-Mean Dev
Tx	0.0018 - 0.0007	0.0004 - 0.0001
Ty	0.0017 - 0.0006	0.0006 - 0.0002
Tz	0.0607 - 0.0038	0.0738 - 0.0117
Rx	4.8255 - 0.7527	4.4678 - 0.1163
Ry	0.4279 - 0.0233	0.2963 - 0.1050
Rz	0.5173 - 0.1706	0.0606 - 0.0039

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Contact

For any queries, please contact or Adithya K Krishna or Lalithkumar