  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/.
