This project aims to reconstruct surfaces from point clouds, generate a mesh, and visualize it using 3D viewers such as MeshLab.
First, create an Anaconda environment called conv_onet
using the provided environment.yaml
file.
conda env create -f environment.yaml
conda activate conv_onet
Next, compile the extension modules required for the project.
python setup.py build_ext --inplace
- Place your raw data in the
data/raw
folder. - Place your
.npz
data into thedata
folder, such asdata/daisue/raw
folder. The.npz
file should contain apoints
variable storing each point in an(x, y, z)
format.
To process your raw data by sampling and creating normal vectors, run the following bash script:
bash scripts/dataset_daisue/run_build_dataset.sh
After running the script, a new .npz
file with the normals vector calculated will be generated. You can check the code in scripts/dataset_daisue/build_dataset.py
for more details.
To reconstruct the surface using a pretrained model trained on Matterport3D, run the following command:
python generate.py configs/pointcloud_crop/daisue.yaml
After generating the mesh, you can view it using 3D viewers such as MeshLab.
This project is modified from Convolutional Occupancy Networks.
This project is licensed under the terms of the MIT license.