Algorithm to texture 3D reconstructions from multi-view stereo images

Overview

MVS-Texturing

Welcome to our project that textures 3D reconstructions from images. This project focuses on 3D reconstructions generated using structure from motion and multi-view stereo techniques, however, it is not limited to this setting.

The algorithm was published in Sept. 2014 on the European Conference on Computer Vision. Please refer to our project website (http://www.gcc.tu-darmstadt.de/home/proj/texrecon/) for the paper and further information.

Please be aware that while the interface of the texrecon application is relatively stable the interface of the tex library is currently subject to frequent changes.

Dependencies

The code and the build system have the following prerequisites:

  • cmake (>= 3.1)
  • git
  • make
  • gcc (>= 5.0.0) or a compatible compiler
  • libpng, libjpg, libtiff, libtbb

Furthermore the build system automatically downloads and compiles the following dependencies (so there is nothing you need to do here):

Compilation Build Status

  1. git clone https://github.com/nmoehrle/mvs-texturing.git
  2. cd mvs-texturing
  3. mkdir build && cd build && cmake ..
  4. make (or make -j for parallel compilation)

If something goes wrong during compilation you should check the output of the cmake step. CMake checks all dependencies and reports if anything is missing.

If you think that there is some problem with the build process on our side please tell us.

If you are trying to compile this under windows (which should be possible but we haven't checked it) and you feel like we should make minor fixes to support this better, you can also tell us.

Execution

As input our algorithm requires a triangulated 3D model and images that are registered against this model. One way to obtain this is to:

A quick guide on how to use these applications can be found on our project website.

By starting the application without any parameters and you will get a description of the expected file formats and optional parameters.

Troubleshooting

When you encounter errors or unexpected behavior please make sure to switch the build type to debug e.g. cmake -DCMAKE_BUILD_TYPE=DEBUG .., recompile and rerun the application. Because of the computational complexity the default build type is RELWITHDEBINFO which enables optimization but also ignores assertions. However, these assertions could give valuable insight in failure cases.

License, Patents and Citing

Our software is licensed under the BSD 3-Clause license, for more details see the LICENSE.txt file.

If you use our texturing code for research purposes, please cite our paper:

@inproceedings{Waechter2014Texturing,
  title    = {Let There Be Color! --- {L}arge-Scale Texturing of {3D} Reconstructions},
  author   = {Waechter, Michael and Moehrle, Nils and Goesele, Michael},
  booktitle= {Proceedings of the European Conference on Computer Vision},
  year     = {2014},
  publisher= {Springer},
}

Contact

If you have trouble compiling or using this software, if you found a bug or if you have an important feature request, please use the issue tracker of github: https://github.com/nmoehrle/mvs-texturing

For further questions you may contact us at mvs-texturing(at)gris.informatik.tu-darmstadt.de

Owner
Nils Moehrle
Nils Moehrle
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The

Benedek Rozemberczki 188 Dec 29, 2022
Keras code and weights files for popular deep learning models.

Trained image classification models for Keras THIS REPOSITORY IS DEPRECATED. USE THE MODULE keras.applications INSTEAD. Pull requests will not be revi

François Chollet 7.2k Dec 29, 2022
Image-Scaling Attacks and Defenses

Image-Scaling Attacks & Defenses This repository belongs to our publication: Erwin Quiring, David Klein, Daniel Arp, Martin Johns and Konrad Rieck. Ad

Erwin Quiring 163 Nov 21, 2022
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters"

Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters" Pipeline of CLIP-Adapter CLIP-Adapter is a drop-in modul

peng gao 157 Dec 26, 2022
A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).

A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).

Yinqiong Cai 189 Dec 28, 2022
Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file.

Attendance-System-based-on-Facial-recognition-Attendance-data-stored-in-csv-file- Simple Python project using Opencv and datetime package to recognise

3 Aug 09, 2022
The comma.ai Calibration Challenge!

Welcome to the comma.ai Calibration Challenge! Your goal is to predict the direction of travel (in camera frame) from provided dashcam video. This rep

comma.ai 697 Jan 05, 2023
A Comprehensive Study on Learning-Based PE Malware Family Classification Methods

A Comprehensive Study on Learning-Based PE Malware Family Classification Methods Datasets Because of copyright issues, both the MalwareBazaar dataset

8 Oct 21, 2022
PyG (PyTorch Geometric) - A library built upon PyTorch to easily write and train Graph Neural Networks (GNNs)

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

PyG 16.5k Jan 08, 2023
A simple baseline for 3d human pose estimation in PyTorch.

3d_pose_baseline_pytorch A PyTorch implementation of a simple baseline for 3d human pose estimation. You can check the original Tensorflow implementat

weigq 312 Jan 06, 2023
Official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR)

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

12 Jan 13, 2022
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper

phoenix 3 Feb 27, 2022
Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training

SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training Introduction This is a PyTorch implementation of "

weijiawu 34 Nov 09, 2022
Starter kit for getting started in the Music Demixing Challenge.

Music Demixing Challenge - Starter Kit 👉 Challenge page This repository is the Music Demixing Challenge Submission template and Starter kit! Clone th

AIcrowd 106 Dec 20, 2022
A Python package to create, run, and post-process MODFLOW-based models.

Version 3.3.5 — release candidate Introduction FloPy includes support for MODFLOW 6, MODFLOW-2005, MODFLOW-NWT, MODFLOW-USG, and MODFLOW-2000. Other s

388 Nov 29, 2022
Remote sensing change detection tool based on PaddlePaddle

PdRSCD PdRSCD(PaddlePaddle Remote Sensing Change Detection)是一个基于飞桨PaddlePaddle的遥感变化检测的项目,pypi包名为ppcd。目前0.2版本,最新支持图像列表输入的训练和预测,如多期影像、多源影像甚至多期多源影像。可以快速完

38 Aug 31, 2022
Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.

Optimum Transformers Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗 Transformers, Optimum and ONNX runtime. Installatio

Aleksey Korshuk 115 Dec 16, 2022
Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)

Open Compound Domain Adaptation [Project] [Paper] [Demo] [Blog] Overview Open Compound Domain Adaptation (OCDA) is the author's re-implementation of t

Zhongqi Miao 137 Dec 15, 2022
Official code for the paper "Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks".

Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks This repository contains the official code for the

Linus Ericsson 11 Dec 16, 2022
Code for our paper 'Generalized Category Discovery'

Generalized Category Discovery This repo is a placeholder for code for our paper: Generalized Category Discovery Abstract: In this paper, we consider

107 Dec 28, 2022