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/.
翻译：我们提出了一个具有挑战性的数据集“变化Sim ” ( ChangeSim ), 目标是在线场景变化检测(SCD)等等。这些数据是在光现实模拟环境中收集的,并存在环境非目标变异,如空气发热和光质状况变化,以及工业室内环境的定向对象变化。通过在模拟中收集数据,多式传感器数据和精确的地面真实标签(如 RGB 图像、深度图像、语义分解、变化区块、相机显示和3D重建等)是可以获取的。虽然先前的SCD 在线数据集评估了配对图像的模型,但 ChangSim 也提供了原始的无目标序列,为以端对齐和检测的方式开发在线SCD模型提供了机会。实验显示,即使是最新的双型SCD模型也因配对过程的瓶颈而受害,当环境包含非目标变异时,情况也更糟。我们的数据集可以在 http://sammica.github.io/CHangeSim/上查阅。
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