3DKeypoints-DA
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Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
This repository is the PyTorch implementation for the network presented in:
Xingyi Zhou, Arjun Karpur, Chuang Gan, Linjie Luo, Qixing Huang, Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency ECCV 2018(arXiv:1712.05765)
Contact: [email protected]
Requirements
- cudnn
- PyTorch
- Python with h5py, opencv and progress
- Optional: tensorboard
Data
- The following datasets are used in this repo. If you use the data provided, please also consider citing them:
- ModelNet
- ShapeNet and keypoint annotation provided in SyncSpecCNN.
- Redwood Dataset
- Download the pre-processing data and annotations here, and un-zip them on
data
.
Testing
python main.py -expID demo -loadModel ../models/Redwood.pth.tar -test
- Visualize the results.
python tools/vis.py ../exp/Chair/demo/img_valTarget ../exp/Chair/demo/valTarget.txt
Training
- Stage1: Train the source model.
python main.py -expID Source -epochs 120 -dropLR 90
Our results of this stage is provided here.
- Stage2: Adapt to the target domain with shape consistency loss.
python main.py -expID Redwood -targetDataset Redwood -targetRatio 1 -shapeWeight 1 -loadModel ../models/ModelNet120.tar -LR 0.01