THU-luvision / BuildingFusion

This is the official code repository for BuildingFusion, a semantic-aware structural building-scale reconstruction system, which allows collaborative building-scale dense reconstruction, with online semantic and structural understanding. It is able to handle large scale scenes (~1000m^2).

BuildingFusion: Semantic-aware Structural Building-scale 3D Reconstruction (TPAMI 2021)

By Tian Zheng; Guoqing Zhang; Lei Han; Lan Xu; Lu Fang*. (*) Corresponding author. [Paper]

Introduction

This is the official code repository for BuildingFusion, a semantic-aware structural building-scale reconstruction system, which allows collaborative building-scale dense reconstruction, with online semantic and structural understanding. It is able to handle large scale scenes (~1000m^2).

This is a project from LuVision SIGMA, Tsinghua University. Visit our website for more interesting works: http://www.luvision.net/

License

This project is released under the GPLv3 license. We only allow free use for academic use. For commercial use, please contact us to negotiate a different license by: fanglu at tsinghua.edu.cn

Citing

If you find our code useful, please kindly cite our paper:

@ARTICLE{9286413,
  author={Zheng, Tian and Zhang, Guoqing and Han, Lei and Xu, Lan and Fang, Lu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Building Fusion: Semantic-aware Structural Building-scale 3D Reconstruction}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2020.3042881}}

Quickstart with docker

0. Install docker and nvidia runtime following here. A CUDA capable GPU is required.
1. Download the test data from http://153.35.185.228:81/opensource_data/BuildingFusion/test_sequence.zip
2. Modify the correct paths to the data in docker_run.sh
3. Start with bash docker_run.sh

Collaborative Reconstruction

After a server is running with:

roscore
rosrun collaborative_fusion collaborative_fusion_server

We can launch multiple agent with:

export ROS_MASTER_URI=http://<server ip>:<server port>
rosrun collaborative_fusion collaborative_fusion_client <dataset path (optional, no need for online scanning)>

Data

We support both data stream from an Xtion RGBD camera and from a offline dataset.

The format of offline dataset follows TUM RGBD Dataset:

- Dataset
    - sequence 1
        - rgb
        - depth
        - associate.txt
    - sequence 2
        - rgb
        - depth
        - associate.txt
...

Parameters

The calibration information is in calib.txt. Change according to the dataset.

Runtime parameters are in setting.yaml.