SN-NIR

This is the official implementation of Normal-guided Detail-Preserving Neural Implicit Function for High-Fidelity 3D Surface Reconstruction.

Aarya Patel, Hamid Laga, and Ojaswa Sharma

Project page | Paper

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Installation

git clone https://github.com/sn-nir/sn-nir.git
cd sn-nir
pip install -r requirements.txt

Usage

Data Convention

Our 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.

Run

Train

python run_experiments.py --mode train --conf ./confs/CONF_NAME.conf --case CASE_NAME

Extract mesh

python run_experiments.py --mode validate_mesh --conf ./confs/CONF_NAME.conf --case CASE_NAME --is_continue

Citation

If you find our code useful for your research, please cite as

@Article{Patel25,
    title={Normal-guided Detail-Preserving Neural Implicit Function for High-Fidelity 3D Surface Reconstruction},
    author={Patel, Aarya and Laga, Hamid and Sharma, Ojaswa},
    journal = {Proceedings of the ACM on Computer Graphics and Interactive Techniques},
    number = {1},
    volume = {8},
    article = {12},
    month = {May},
    doi = {https://doi.org/10.1145/3728293},
    year={2025}
  }