Paper Info
Title
ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments
Abstract
We present a challenging dataset, ChangeSim, aimed at online scene change detection (SCD) and more. The data is collected in photo-realistic simulation environments with the presence of environmental non-targeted variations, such as air turbidity and light condition changes, as well as targeted object changes in industrial indoor environments. By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and 3D reconstructions. While the previous online SCD datasets evaluate models given well-aligned image pairs, ChangeSim also provides raw unpaired sequences that present an opportunity to develop an online SCD model in an end-to-end manner, considering both pairing and detection. Experiments show that even the latest pair-based SCD models suffer from the bottleneck of the pairing process, and it gets worse when the environment contains the non-targeted variations. Our dataset is available at https://sammica.github.io/ChangeSim/.
Year
DOI
Venue
2021
10.1109/IROS51168.2021.9636350
2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
DocType
ISSN
Citations 
Conference
2153-0858
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jin-Man Park100.34
Jae-Hyuk Jang200.34
Sahng-Min Yoo300.34
Sun-Kyung Lee400.34
Ue-Hwan Kim500.34
Jong-Hwan Kim6215.15