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)
LINEMOD:
- https://cvarlab.icg.tugraz.at/projects/3d_object_detection/ObjRecPoseEst.tar.gz
- https://github.com/AdithyaK243/Linemod
Dataset(used in paper)
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.
- Python 3.7
- Tensorflow (2.x)
- PyWavelet (Wavelet Decomposition)
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>
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 |
For any queries, please contact or Adithya K Krishna or Lalithkumar