Thesis-NeRFs

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Denoising 3D TEM tomography via Advanced Neural Radiance Fields(NeRF)

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# Thesis Title: Denoising 3D TEM tomography via Advanced Neural Radiance Fields(NeRF)

## Thesis Presentation [Slides](https://docs.google.com/presentation/d/12Wyv8tTjPnTopcQcVolASewZ1mb3J9Qr/edit?usp=sharing&ouid=117540787695449847995&rtpof=true&sd=true) 💥

## Architecture 🧩
![Thesis Architecture](https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/0763edb5-8c52-4102-9a76-0730262c3c53)

## Result Denoising

### TEM Dataset 1
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/e8e882d7-68fb-48c2-94f2-6adcf75ad62e" height="250">

### TEM Dataset 2
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/57bd937c-614e-4087-aad5-112b042fa1ac" height="250">

### TEM Dataset 3
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/6683d109-fdfe-4436-81df-ff20cc327e54" height="250">
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/5a1fd5a8-1dc7-401a-b326-00b2c40ccb58" height="250">

### TEM Dataset 4
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/087960e9-3532-4e23-a1b7-ebba31761c3b" height="250">

### STEM Dataset 1
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/dff0f77c-8243-4903-bc54-c9c34347b5a2" height="250">

### STEM Dataset 2
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/087960e9-3532-4e23-a1b7-ebba31761c3b" height="250">

### Synthetic Dataset
<img src="https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/e01fb136-8be5-4117-9ca4-3902834f8d00" height="250">

## 3D Construction of TEM image(Dataset 4)
![TEM_Data_img](https://github.com/Mithunjack/Thesis-NeRFs/assets/23437429/8099128c-2ef9-47ff-bf0e-3814772018f8)

## Usefull tools for Literature Review 🛠️
<details>
  <summary>Click to expand!</summary>

1. It provides an overview of any article and all related research with the assistance of AI -> [Paper Digest](https://www.paperdigest.org/review/).
  2. The AI Research Assistant -> [Elicit](https://elicit.org/) | [Scispace](https://typeset.io/)
  3. An app that visualizes all of the related papers for a specific paper -> [Litmap](https://www.litmaps.com/)
  4. Similar to Litmap, but using a different linked paper visualization website-> [Connected paper](https://www.connectedpapers.com/)
  5. For improved writing efficiency -> [Writefull](https://www.writefull.com/)
  6. For creating custom vectors or biologically themed graphics -> [Bio render](https://biorender.com/)
  7. Similarly, litmap and connected paper provide another option -> [scite](https://scite.ai/)
  8. Reference Manager -> [Zotero](https://www.zotero.org/)
  9. Alternative Reference Manager -> [Mandele](https://www.mendeley.com/?interaction_required=true)
</details>

## Useful Commands 👽
<details>
  <summary>Click to expand!</summary>

## Linux 🤸
  
  1. CUDA Version `nvidia-smi` // 11.8
  2. Python Version `python3 --version` // 3.8.15
  3. Clear cmd `reset`
  4. Conda path setup [path](https://askubuntu.com/questions/849470/how-do-i-activate-a-conda-environment-in-my-bashrc)
  5. Ubuntu architecture `uname -m` // x86_64
  6. Ubuntu version and machine id `hostnamectl`
  7. NVCC version `nvcc -V`
  8. To find or locate file `locate {nvcc}`
  9. PyTorch version `pip3 show torch` // 1.12.1+cu113
  10. Linux background processing `htop`
  11. Kill VS Code server `Remote-SSH: kill VS Code Server on Host` [Documentation](https://github.com/microsoft/vscode-remote-release/issues/4307)
  12. Installing stuff without `sudo` [Documentation](https://askubuntu.com/questions/339/how-can-i-install-a-package-without-root-access)
  13. Extract file `tar -xvf cmake-3.x.x.tar.gz`
  14. Debian-based Linux Distribution: `cat /etc/os-release`
  15. Change GPU `export CUDA_VISIBLE_DEVICES=1`
  16. For building CMAKE `cmake . -B build -DCMAKE_CUDA_COMPILER:STRING="/usr/local/cuda-11.8/bin/nvcc"`
  
  #### [NeRFstudio Documentation](https://docs.nerf.studio/en/latest/quickstart/installation.html) 📄
  
  **NerfStudio ✈️**
  
  1. Check available model `ns-train --help`
  2. Torch version check in Anaconda `pip3 show torch`
  3. With a specified websocket port `ns-train nerfacto --vis viewer --viewer.websocket-port=7008`
  4. Resume training from one certain point `ns-train nerfacto --data data/nerfstudio/poster --trainer.load-dir {outputs/.../nerfstudio_models}`
  5. Pre processing custom data `ns-process-data images --data data/custom_data --output-dir outputs/custom_data_preprocessed --no-gpu`
  
  **Anaconda 🐍**
  
  1. Creating conda env `conda create --name nerfstudio -y python=3.8`
  2. Activate env `conda activate nerfstudio`
  3. Active env `conda info -e`
  4. Jupyter notebook password [setup](https://jupyter-notebook.readthedocs.io/en/stable/public_server.html)
  5. Remove env `conda env remove -n ENV_NAME`
  6. Remove all env `conda remove --name myenv --all`
  7. Env list `conda env list`

</details>

## 📝 License
This project is licensed under the **MIT License** - see the [LICENSE](./LICENSE) file for details.

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