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

By Tian Zheng^; Guoqing Zhang^; Lei Han^; Lan Xu; Lu Fang* (^ equal contribution, * 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 data streams from Xtion RGBD cameras or offline sequences.

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, which should be changed according to the dataset. calib provides some calibration parameters used in our experiments.

Runtime parameters are in setting.yaml.