[Project Page] [ArXiv]
Isaac Kasahara, Shubham Agrawal,
Selim Engin, Nikhil Chavan-Dafle,
Shuran Song, Volkan Isler
Samsung AI Center, New York
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Make a new directory
mkdir RIC_Project cd RIC_Project
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Install depth completion repo from ClearGrasp in this folder
Follow steps 1, 2, and 4 on their page. (You do not need their data or models)
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Clone our repo inside RIC_Project folder
Navigate back to RIC_Project folder
git clone https://github.com/SamsungLabs/RIC.git
Folder structure should look like this:
RIC_Project # Root folder ├── RIC # Code for RIC (This Repo) └── ClearGrasp # Code for depth completion (ClearGrasp)
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Install our python dependencies
cd RIC python3.8 -m venv venv source venv/bin/activate pip install -r requirements.txt
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Download weights for depth completion
Download the weights for surface normal and occlusion boundary estimation and place them in the RIC/weights/ folder.
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Install HOPE Dataset
Navigate to the HOPE dataset page and follow the download instructions.
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Add OpenAI API Key
If you don't have one already, follow these instructions:
- First make an OpenAI account if you don't have one already
- Next go here https://platform.openai.com/api-keys
- Click on create new secret key
- Copy the key
Once you have the key, paste the key on line 28 of "inference_hope_dataset.py"
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To generate predictions:
python3 inference_hope_dataset.py --data_dir "path/to/hope-dataset/hope_video/" --write_file_root "results/"
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To evaluate predictions:
python3 evaluate_results.py --results_dir "results/"
RIC code makes use of ClearGrasp and Dalle-2 implementations. We sincerely thanks the authors for providing their implementations.
@misc{kasahara2023ric,
title={RIC: Rotate-Inpaint-Complete for Generalizable Scene Reconstruction},
author={Isaac Kasahara and Shubham Agrawal and Selim Engin and Nikhil Chavan-Dafle and Shuran Song and Volkan Isler},
year={2023},
eprint={2307.11932},
archivePrefix={arXiv},
primaryClass={cs.CV}
}