ChangeSim：産業屋内環境におけるエンドツーエンドのオンラインシーン変化検出に向けて
ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments
産業屋内環境のフォトリアルなシミュレーションで、環境変動や物体変化を含むマルチモーダルな変化検出データセットChangeSimを構築し、未整列の生シーケンスからペアリングと検出を一括で行うエンドツーエンド手法の必要性を示した。
著者: Jin-Man Park, Jae-Hyuk Jang, Sahng-Min Yoo, Sun-Kyung Lee, Ue-Hwan Kim, Jong-Hwan Kim
分類: cs.CV, cs.RO
原文アブストラクト
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/.
