Implementation for the "Surface Reconstruction from 3D Line Segments" paper.

Overview

Surface Reconstruction from 3D Line Segments

Surface reconstruction from 3d line segments.
Langlois, P. A., Boulch, A., & Marlet, R.
In 2019 International Conference on 3D Vision (3DV) (pp. 553-563). IEEE. Project banner

Installation

  • [IMPORTANT NOTE] The plane arrangement is given as a Linux x64 binary. Please let us know if you need it for an other platform/compiler or if you have issues with it.

  • MOSEK 8 :

    • Download
    • Installation instructions.
    • Request a license (free for academics), and put it in ~/mosek/mosek.lic.
    • Set the mosek directory in the MOSEK_DIR environment variable such that <MOSEK_DIR>/8/tools/platform/linux64x86/src/fusion_cxx is a valid path:

    export MOSEK_DIR=/path/to/mosek

    • Make sure that the binaries are available at runtime:

    export LD_LIBRARY_PATH=$MOSEK_DIR/8/tools/platform/linux64x86/bin:$LD_LIBRARY_PATH

  • Clone this repository: git clone https://github.com/palanglois/line-surface-reconstruction.git

  • Go to the directory: cd line-surface-reconstruction

  • CGAL : Version 4.11 is required:

git clone https://github.com/CGAL/cgal.git external/cgal
cd external/cgal
git checkout releases/CGAL-4.11.3
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make
cd ../../..
  • Make a build directory: mkdir build
  • Go to the build directory: cd build
  • Prepare the project with cmake: cmake -DCMAKE_BUILD_TYPE=Release ..
  • Compile the project: make

Examples

  • Out of the box examples are available in demo.sh

  • An example of a full reconstruction procedure from a simple set of images is available here

  • A benchmark example for an artificial textureless scene (with quantitative evaluation) is available here.

Programs

For every program, a simple documentation is available by running ./<program_name> -h

  • ransac_on_lines detects planes in a line set.
  • line_based_recons_param performs reconstruction out of a set of lines and detected planes. Computing the linear program is time consuming, but optimizing is way faster. Therefore, this program 1st computes the linear program and enters a loop in which you can manually set the optimization parameters in order to find the optimal ones for your reconstruction.
  • line_based_recons does the same as line_based_recons_param but the optimization parameters are set directly in the command line. Use it only if you know the optimal parameters for the reconstruction.
  • mesh_metrics provides evaluation metrics between two meshes.

Visualization

Reconstruction .ply files can be visualized directly in programs such as Meshlab or CloudCompare.

A simple OpenGL viewer is available to directly visualize the json line files.

Raw data

The raw data for Andalusian and HouseInterior is available here. For both examples, it includes the raw images as well as the full calibration in .nvm (VisualSFM) format.

For HouseInterior, a ground truth mesh is also available.

License

Apart from the code located in the external directory, all the code is provided under the GPL license.

The binaries and code provided in the external/PolyhedralComplex directory is provided under the Creative Commons CC-BY-SA license.

If these licenses do not suit your needs, please get in touch with us.

Citing this work

@inproceedings{langlois:hal-02344362,
TITLE = {{Surface Reconstruction from 3D Line Segments}},
AUTHOR = {Langlois, Pierre-Alain and Boulch, Alexandre and Marlet, Renaud},
URL = {https://hal.archives-ouvertes.fr/hal-02344362},
BOOKTITLE = {{2019 International Conference on 3D Vision (3DV)}},
ADDRESS = {Qu{\'e}bec City, Canada},
PUBLISHER = {{IEEE}},
PAGES = {553-563},
YEAR = {2019},
MONTH = Sep,
DOI = {10.1109/3DV.2019.00067},
} 
General Vision Benchmark, a project from OpenGVLab

Introduction We build GV-B(General Vision Benchmark) on Classification, Detection, Segmentation and Depth Estimation including 26 datasets for model e

174 Dec 27, 2022
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression

Delving into Deep Imbalanced Regression This repository contains the implementation code for paper: Delving into Deep Imbalanced Regression Yuzhe Yang

Yuzhe Yang 568 Dec 30, 2022
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 168 Dec 28, 2022
Implementation of average- and worst-case robust flatness measures for adversarial training.

Relating Adversarially Robust Generalization to Flat Minima This repository contains code corresponding to the MLSys'21 paper: D. Stutz, M. Hein, B. S

David Stutz 13 Nov 27, 2022
Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

Self-Supervised-MVS This repository is the official PyTorch implementation of our AAAI 2021 paper: "Self-supervised Multi-view Stereo via Effective Co

hongbin_xu 127 Jan 04, 2023
[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

RoSTER The source code used for Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training, p

Yu Meng 60 Dec 30, 2022
KDD CUP 2020 Automatic Graph Representation Learning: 1st Place Solution

KDD CUP 2020: AutoGraph Team: aister Members: Jianqiang Huang, Xingyuan Tang, Mingjian Chen, Jin Xu, Bohang Zheng, Yi Qi, Ke Hu, Jun Lei Team Introduc

96 May 30, 2022
Official Pytorch implementation for video neural representation (NeRV)

NeRV: Neural Representations for Videos (NeurIPS 2021) Project Page | Paper | UVG Data Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav S

hao 214 Dec 28, 2022
Implementation of Lie Transformer, Equivariant Self-Attention, in Pytorch

Lie Transformer - Pytorch (wip) Implementation of Lie Transformer, Equivariant Self-Attention, in Pytorch. Only the SE3 version will be present in thi

Phil Wang 78 Oct 26, 2022
Pytorch implementation of COIN, a framework for compression with implicit neural representations 🌸

COIN 🌟 This repo contains a Pytorch implementation of COIN: COmpression with Implicit Neural representations, including code to reproduce all experim

Emilien Dupont 104 Dec 14, 2022
The implementation of ICASSP 2020 paper "Pixel-level self-paced learning for super-resolution"

Pixel-level Self-Paced Learning for Super-Resolution This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resoluti

Elon Lin 41 Dec 15, 2022
Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling

Permutation Invariant Graph Generation via Score-Based Generative Modeling This repo contains the official implementation for the paper Permutation In

64 Dec 29, 2022
Semantic Edge Detection with Diverse Deep Supervision

Semantic Edge Detection with Diverse Deep Supervision This repository contains the code for our IJCV paper: "Semantic Edge Detection with Diverse Deep

Yun Liu 12 Dec 31, 2022
An efficient and easy-to-use deep learning model compression framework

TinyNeuralNetwork įŽ€äŊ“中文 TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework, which contains features like neura

Alibaba 441 Dec 25, 2022
Revisiting Global Statistics Aggregation for Improving Image Restoration

Revisiting Global Statistics Aggregation for Improving Image Restoration Xiaojie Chu, Liangyu Chen, Chengpeng Chen, Xin Lu Paper: https://arxiv.org/pd

MEGVII Research 128 Dec 24, 2022
TensorFlow Implementation of "Show, Attend and Tell"

Show, Attend and Tell Update (December 2, 2016) TensorFlow implementation of Show, Attend and Tell: Neural Image Caption Generation with Visual Attent

Yunjey Choi 902 Nov 29, 2022
Self-supervised Label Augmentation via Input Transformations (ICML 2020)

Self-supervised Label Augmentation via Input Transformations Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin (KAIST) Accepted to ICML 2020 Install de

hankook 96 Dec 29, 2022
Key information extraction from invoice document with Graph Convolution Network

Key Information Extraction from Scanned Invoices Key information extraction from invoice document with Graph Convolution Network Related blog post fro

Phan Hoang 39 Dec 16, 2022
A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

img_sussifier A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196 Examples How to use install python pip i

41 Sep 30, 2022
D2Go is a toolkit for efficient deep learning

D2Go D2Go is a production ready software system from FacebookResearch, which supports end-to-end model training and deployment for mobile platforms. W

Facebook Research 744 Jan 04, 2023