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NeuralWarp: Improving neural implicit surfaces geometry with patch warping (CVPR22)
Project page | Paper
Code release of paper Improving neural implicit surfaces geometry with patch warping
François Darmon, Bénédicte Bascle, Jean-Clément Devaux, Pascal Monasse and Mathieu Aubry
Installation
See requirements.txt
for the python packages.
Data
Download data with
./download_dtu.sh
and ./download_epfl.sh
Extract mesh from a pretrained model
Download the pretrained models with
./download_pretrained_models.sh
Run the extraction script with
python extract_mesh.py --conf CONF --scene SCENE [--OPTIONS]
CONF
is the configuration file (e.g.confs/NeuralWarp_dtu.conf
)SCENE
is the scan id for DTU data and eitherfountain
orherzjesu
for EPFL.- See
python extract_mesh.py --help
for 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 1
for EPFL.
The output mesh will be in evals/CONF_SCENE/ouptut_mesh.ply
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 evals/CONF_SCENE/result.txt
.
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 exps/CONF_SCENE/TIMESTAMP/visu.html
.
Acknowledgments
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 and Bruno Lecouat for valuable feedback, and Jingyang Zhang for sending MVSDF results.
Copyright
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.