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

Overview

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.
Owner
François Darmon
PhD student in 3D computer vision at Imagine team ENPC and Thales LAS FRANCE
François Darmon
Contrastive Loss Gradient Attack (CLGA)

Contrastive Loss Gradient Attack (CLGA) Official implementation of Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, WWW22 Bu

12 Dec 23, 2022
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

AntonMu 616 Jan 08, 2023
A collection of SOTA Image Classification Models in PyTorch

A collection of SOTA Image Classification Models in PyTorch

sithu3 85 Dec 30, 2022
TensorFlow-based implementation of "ICNet for Real-Time Semantic Segmentation on High-Resolution Images".

ICNet_tensorflow This repo provides a TensorFlow-based implementation of paper "ICNet for Real-Time Semantic Segmentation on High-Resolution Images,"

HsuanKung Yang 406 Nov 27, 2022
Educational API for 3D Vision using pose to control carton.

Educational API for 3D Vision using pose to control carton.

41 Jul 10, 2022
Single cell current best practices tutorial case study for the paper:Luecken and Theis, "Current best practices in single-cell RNA-seq analysis: a tutorial"

Scripts for "Current best-practices in single-cell RNA-seq: a tutorial" This repository is complementary to the publication: M.D. Luecken, F.J. Theis,

Theis Lab 968 Dec 28, 2022
EGNN - Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch

EGNN - Pytorch Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch. May be eventually used for Alphafold2 replication. This

Phil Wang 259 Jan 04, 2023
PN-Net a neural field-based framework for depth estimation from single-view RGB images.

PN-Net We present a neural field-based framework for depth estimation from single-view RGB images. Rather than representing a 2D depth map as a single

1 Oct 02, 2021
Group Fisher Pruning for Practical Network Compression(ICML2021)

Group Fisher Pruning for Practical Network Compression (ICML2021) By Liyang Liu*, Shilong Zhang*, Zhanghui Kuang, Jing-Hao Xue, Aojun Zhou, Xinjiang W

Shilong Zhang 129 Dec 13, 2022
Anomaly detection related books, papers, videos, and toolboxes

Anomaly Detection Learning Resources Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify

Yue Zhao 6.7k Dec 31, 2022
Real-time analysis of intracranial neurophysiology recordings.

py_neuromodulation Click this button to run the "Tutorial ML with py_neuro" notebooks: The py_neuromodulation toolbox allows for real time capable pro

Interventional Cognitive Neuromodulation - Neumann Lab Berlin 15 Nov 03, 2022
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Alexis David Jacq 163 Dec 26, 2022
HuSpaCy: industrial-strength Hungarian natural language processing

HuSpaCy: Industrial-strength Hungarian NLP HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing faciliti

HuSpaCy 120 Dec 14, 2022
TensorFlow tutorials and best practices.

Effective TensorFlow 2 Table of Contents Part I: TensorFlow 2 Fundamentals TensorFlow 2 Basics Broadcasting the good and the ugly Take advantage of th

Vahid Kazemi 8.7k Dec 31, 2022
Benchmark datasets, data loaders, and evaluators for graph machine learning

Overview The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover

1.5k Jan 05, 2023
A PyTorch implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

Caiyong Wang 14 Sep 20, 2022
Search and filter videos based on objects that appear in them using convolutional neural networks

Thingscoop: Utility for searching and filtering videos based on their content Description Thingscoop is a command-line utility for analyzing videos se

Anastasis Germanidis 354 Dec 04, 2022
Labelbox is the fastest way to annotate data to build and ship artificial intelligence applications

Labelbox Labelbox is the fastest way to annotate data to build and ship artificial intelligence applications. Use this github repository to help you s

labelbox 1.7k Dec 29, 2022
Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks (paper) By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software T

Qing-Long Zhang 199 Jan 08, 2023
[ICCV 2021] HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration Introduction The repository contains the source code and pre-tr

Intelligent Sensing, Perception and Computing Group 55 Dec 14, 2022