thibaultgroueix / neuralwarp

Code release of paper Improving neural implicit surfaces geometry with patch warping

NeuralWarp: Improving neural implicit surfaces geometry with patch warping

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 then run the extraction script

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 either fountain or herzjesu 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 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.