NeuralWarp: Improving neural implicit surfaces geometry with patch warping
requirements.txt for the python packages.
Download data with
Extract mesh from a pretrained model
Download the pretrained models with
Run the extraction script with
python extract_mesh.py --conf CONF --scene SCENE [--OPTIONS]
CONFis the configuration file (e.g.
SCENEis the scan id for DTU data and either
python extract_mesh.py --helpfor a detailed explanation of the options.
The evaluation in the papers are with default options for DTU and with
--bbox_size 4 --no_one_cc --filter_visible_triangles --min_nb_visible 1for EPFL.
The output mesh will be in
You can also run the evaluation: first download the DTU evaluation data
./download_dtu_eval then run the evaluation script
python eval.py --scene SCENE
The evaluation metrics will be written in
Train a model from scratch
First train a baseline model (i.e. VolSDF)
python train.py --conf confs/baseline_DATASET --scene SCENE.
Then finetune using our method with
python train.py --conf confs/NeuralWarp_DATASET --scene SCENE.
A visualization html file is generated for each training in
This repository is inspired by IDR
This work was supported in part by ANR project EnHerit ANR-17-CE23-0008 and was performed using HPC resources from GENCI–IDRIS 2021-AD011011756R1.
We thank Tom Monnier for valuable feedback and Jingyang Zhang for sending MVSDF results.
NeuralWarp All rights reseved to Thales LAS and ENPC. This code is freely available for academic use only and Provided “as is” without any warranty. Modification are allowed for academic research provided that the following conditions are met : * Redistributions of source code or any format must retain the above copyright notice and this list of conditions. * Neither the name of Thales LAS and ENPC nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.