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

Thesis Presentation Slides 💥

Architecture 🧩

Result Denoising

TEM Dataset 1

TEM Dataset 2

TEM Dataset 3

TEM Dataset 4

STEM Dataset 1

STEM Dataset 2

Synthetic Dataset

3D Construction of TEM image(Dataset 4)

Usefull tools for Literature Review 🛠️

Click to expand!

1. It provides an overview of any article and all related research with the assistance of AI -> Paper Digest.
2. The AI Research Assistant -> Elicit | Scispace
3. An app that visualizes all of the related papers for a specific paper -> Litmap
4. Similar to Litmap, but using a different linked paper visualization website-> Connected paper
5. For improved writing efficiency -> Writefull
6. For creating custom vectors or biologically themed graphics -> Bio render
7. Similarly, litmap and connected paper provide another option -> scite
8. Reference Manager -> Zotero
9. Alternative Reference Manager -> Mandele

Useful Commands 👽

Click to expand!

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
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
12. Installing stuff without sudo Documentation
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 📄

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
5. Remove env conda env remove -n ENV_NAME
6. Remove all env conda remove --name myenv --all
7. Env list conda env list