Awesome
ChangeSim
This repository provides ChangeSim dataset, codes and files for evaluation. Please refer to our paper (accepted to IROS2021) for more information about the dataset.
Recent updates
- Dataset download links
- Documentation for the dataset
- A tutorial for the visualization of ChangeSim
- A tutorial for the data collection using Airsim and UE4
Dataset download
The data is divided into train/test set and reference/query.
Reference_Sequence_Train(52.8 GB)
Reference_Sequence_Test(30.2 GB)
Query_Sequence_Train(42.8 GB)
Query_Sequence_Test(30.3 GB)
Data directory structure
Ref_Seq_
|
--- Warehouse_0 # Environment folder
| |
| ---- Seq_0 # Sequece
| | |
| | +--- rgb # 0.png - xxxx.png
| | +--- depth # 0.png - xxxx.png
| | +--- semantic_segmentation # 0.png - xxxx.png
| | ---- raw
| | | |
| | | +--- rgb # 0.png - xxxx.png
| | | +--- depth # 0.png - xxxx.png
| | | ---- poses.g2o
| | | ---- rtabmap.yaml
| |
| +--- Seq_1
|
+-- Warehouse_1
.
.
+-- Warehouse_N
Query_Seq_
|
--- Warehouse_0 # Environment folder
| |
| ---- Seq_0 # Sequece
| | |
| | +--- rgb # 0.png - xxxx.png
| | +--- depth # 0.png - xxxx.png
| | +--- semantic_segmentation # 0.png - xxxx.png
| | +--- change_segmentation # 0.png - xxxx.png
| | +--- pose # 0.txt - xxxx.txt
| | ---- t0
| | | |
| | | +--- rgb # 0.png - xxxx.png
| | | +--- depth # 0.png - xxxx.png
| | | +--- idx # 0.txt - xxxx.txt
| | ---- cloud_map.ply
| | ---- trajectory.txt
| |
| +--- Seq_0_dust
| .
| .
| +--- Seq_1_dark
|
+-- Warehouse_1
.
.
+-- Warehouse_N
Citation
If you find this project helpful, please consider citing this project in your publications. The following is the BibTeX of our work.
@inproceedings{park2021changesim,
author = {Park, Jin-Man and Jang, Jae-hyuk, and Yoo, Sahng-Min and Lee, Sun-Kyung and Kim, Ue-hwan and Kim, Jong-Hwan},
title = {ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2021},
organization = {IEEE},
url = {https://arxiv.org/abs/2103.05368},
}
Acknowledgement
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2020-0-00440, Development of artificial intelligence technology that continuously improves itself as the situation changes in the real world).