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TODE-Trans: Transparent Object Depth Estimation with Transformer
PyTorch implementation of paper "TODE-Trans: Transparent Object Depth Estimation with Transformer"
Dataset Preparation
ClearGrasp Dataset
ClearGrasp can be downloaded at their official website (Both training and testing dataset are needed). After you download zip files and unzip them on your local machine, the folder structure should be like
${DATASET_ROOT_DIR}
├── cleargrasp
│ ├── cleargrasp-dataset-train
│ ├── cleargrasp-dataset-test-val
Omniverse Object Dataset
Omniverse Object Dataset can be downloaded here. After you download zip files and unzip them on your local machine, the folder structure should be like
${DATASET_ROOT_DIR}
├── omniverse
│ ├── train
│ │ ├── 20200904
│ │ ├── 20200910
TransCG Dataset
TransCG dataset is now available on official page.
Requirements
The code has been tested under
- Ubuntu 18.04 + NVIDIA GeForce RTX 3090
- PyTorch 1.11.0
System dependencies can be installed by:
sudo apt-get install libhdf5-10 libhdf5-serial-dev libhdf5-dev libhdf5-cpp-11
sudo apt install libopenexr-dev zlib1g-dev openexr
Other dependencies can be installed by
pip install -r requirements.txt
Testing
We provide transcg pretrained checkpoints at checkpoints/.
Training
#Train on transcg dataset and test on transcg
python train.py -c ./configs/train_transcg_val_transcg.yaml
#Tran on CGsyn+ood and test on CGreal
python train.py -c ./configs/train_cgsyn+ood_val_cgreal.yaml
#Tran on CGsyn+ood and test on Transcg
python train.py -c ./configs/train_cgsyn+ood_val_transcg.yaml
Citation
@article{2022tode,
title = {TODE-Trans: Transparent Object Depth Estimation with Transformer},
author = {Kang Chen, Shaochen Wang, Beihao Xia, Dongxu Li, Zhen Kan, and Bin Li},
journal = {arXiv preprint arXiv:2209.08455}
year = {2022}
}