论文标题
MessyTable：多个相机视图中的实例关联
MessyTable: Instance Association in Multiple Camera Views
论文作者
论文摘要
我们提出了一个有趣且具有挑战性的数据集，其中包含许多场景，并从多个相机视图中捕获了凌乱的桌子。该数据集中的每个场景都非常复杂，其中包含多个可能与其他实例相同，堆叠和遮挡的对象实例。关键挑战是将所有视图的RGB图像关联。看似简单的任务令人惊讶地使我们在对象关联中假定表现良好的许多流行方法或启发式方法。数据集在挖掘微妙的外观差异，基于上下文的推理以及与几何线索融合以建立关联的情况下挑战现有方法。我们报告了一些流行的基线的有趣发现，并讨论该数据集如何帮助激发新问题并催化更强大的配方以解决现实世界实例关联问题。项目页面：$ \ href {https://caizhongang.github.io/projects/messytable/} {\ text {messytable}} $
We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. Project page: $\href{https://caizhongang.github.io/projects/MessyTable/}{\text{MessyTable}}$
