ChangeSim

Dataset Information
Modalities
Images, Videos, Point cloud, Time series, RGB-D
Languages
English
Introduced
2021
License
MIT
Homepage

Overview

ChangeSim is a dataset 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.

Variants: ChangeSim

Associated Benchmarks

This dataset is used in 2 benchmarks:

Recent Benchmark Submissions

Task Model Paper Date
Scene Change Detection C-3PO How to Reduce Change Detection … 2022-06-15
Change Detection C-3PO How to Reduce Change Detection … 2022-06-15
Scene Change Detection RTABMAP+CSCDNet ChangeSim: Towards End-to-End Online Scene … 2021-03-09
Scene Change Detection RTABMAP+ChangeNet ChangeSim: Towards End-to-End Online Scene … 2021-03-09

Research Papers

Recent papers with results on this dataset: