In many applications, robots can benefit from object-level understanding of their environments, including the ability to distinguish object instances and re-identify previously seen instances. Object re-identification is challenging across different viewpoints and in scenes with significant appearance variation arising from weather or lighting changes. Most works on object re-identification focus on specific classes; approaches that address general object re-identification require foreground segmentation and have limited consideration of challenges such as occlusions, outdoor scenes, and illumination changes. To address this problem, we introduce CODa Re-ID: an in-the-wild object re-identification dataset containing 1,037,814 observations of 557 objects of 8 classes under diverse lighting conditions and viewpoints. Further, we propose CLOVER, a representation learning method for object observations that can distinguish between static object instances. Our results show that CLOVER achieves superior performance in static object re-identification under varying lighting conditions and viewpoint changes, and can generalize to unseen instances and classes.

翻译：在许多应用中，机器人能够从对其环境的对象级理解中获益，包括区分对象实例以及重新识别先前见过的实例的能力。对象重识别在不同视角下以及因天气或光照变化导致显著外观变化的场景中具有挑战性。大多数关于对象重识别的研究集中于特定类别；那些处理通用对象重识别的方法需要前景分割，并且对诸如遮挡、户外场景和光照变化等挑战的考虑有限。为解决此问题，我们引入了CODa Re-ID：一个在真实世界中的对象重识别数据集，包含在多样化光照条件和视角下对8个类别的557个对象的1,037,814次观测。此外，我们提出了CLOVER，一种针对对象观测的表征学习方法，能够区分静态对象实例。我们的结果表明，CLOVER在变化光照条件和视角变化下的静态对象重识别中实现了优越的性能，并且能够泛化到未见过的实例和类别。