This is the official implementation of Normal-guided Detail-Preserving Neural Implicit Functions for High-Fidelity 3D Surface Reconstruction.
git clone https://github.com/sn-nir/sn-nir.git
cd sn-nir
pip install -r requirements.txtOur data format is inspired from IDR as follows:
CASE_NAME
|-- cameras.npz # camera parameters
|-- image
|-- 000.png # image for each view
|-- 001.png
...
|-- normal
|-- 000.png # normal map for each view
|-- 001.png
...
|-- depth
|-- 000.png # depth for each view
|-- 001.png
...
|-- mask
|-- 000.png # mask for each view
|-- 001.png
...
One can create folders with different data in it, for instance, a normal folder for each normal estimation method.
The name of the folder must be set in the used .conf file.
Train
python run_experiments.py --mode train --conf ./confs/CONF_NAME.conf --case CASE_NAMEExtract mesh
python run_experiments.py --mode validate_mesh --conf ./confs/CONF_NAME.conf --case CASE_NAME --is_continue