Given a set of images of a crime scene, we plan on reconstructing the scene in 3D. This is done by Utilising multiple views of a scene to generate a 3D rendering of the same. We will also be looking into further optimizing the baseline output by the neural net with a GCN.
Name | SRN | |
---|---|---|
Dhruval PB | PES1UG19CS313 | dhruvalpb@pesu.pes.edu |
Sai Mihir J | PES1UG19CS418 | saimihir.j@gmail.com |
Akash Mehta | PES1UG19CS040 | akashmehta556@gmail.com |
This folder contains the implementation of a Graph Convolution Neural Network that performs Monocular Depth Estimation.
- PyTorch1.2+, Python3.5+, Cuda10.0+
- mmcv==0.4.4
# This creates a new conda enviroment to run the model
conda create --name gcndepth python=3.7
conda activate gcndepth
# This installs the right pip and dependencies for the fresh python
conda install ipython
conda install pip
# Install required packages from requirements.txt
pip install -r requirements.txt
conda activate gcndepth
cd ./GCNDepth
python3 infer.py
This will generate depth maps which will be stored in the GCNDepth/assets/Outputs/Grayscale
folder.
We've written scripts that back project these disparity maps into point clouds and save them as npy files in 3DRenders/PointClouds
folder.
This folder contains an implementation of a Monocular SLAM algorithm which we use to estimate camera position and orientation.
xhost +local:docker
sudo apt install nvidia-docker2
sudo systemctl daemon-reload
sudo systemctl restart docker
docker build -t twitchslam .
chmod +x ./Run.sh
./Run.sh # This will start the docker container
cd twitchslam # Once the container is up and running, go to the twitchslam directory
chmod +x ./Predict.py
./Predict.py # This generates the camera orientation predictions on the dataset we've used
This folder contains the code for plotting the point clouds in 3D using pyQt.
pip install numpy pyqtgraph
python3 Plotter.py