Papers › ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments

ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments

9 Mar 2021arXiv:2103.05368archive 2025-07-28

Jin-Man Park, Jae-Hyuk Jang, Sahng-Min Yoo, Sun-Kyung Lee, Ue-Hwan Kim, Jong-Hwan Kim

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 http://sammica.github.io/ChangeSim/.

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SAMMiCA/ChangeSim mentioned on GitHubpytorch report

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Tasks

Change DetectionScene Change DetectionSegmentationSemantic Segmentation

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ChangeSim

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Scene Change Detection ChangeSim RTABMAP+CSCDNet Category mIoU 26.1 #2 of 3 Archive leaderboard report
Scene Change Detection ChangeSim RTABMAP+CSCDNet macro F1 30.6 #2 of 3 Archive leaderboard report
Scene Change Detection ChangeSim RTABMAP+ChangeNet Category mIoU 23.0 #3 of 3 Archive leaderboard report
Scene Change Detection ChangeSim RTABMAP+ChangeNet macro F1 29.8 #3 of 3 Archive leaderboard report

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