标题： IAM：通过新基准增强RGB-D实例分割 显示英文标题

标题： IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

摘要： 图像分割是提供人类辅助并在日常生活中增强自主性的重要任务。 特别是，RGB-D分割——利用视觉和深度线索——因其比仅使用RGB的方法能提供更丰富的场景理解而受到越来越多的关注。 然而，大多数现有的研究主要集中在语义分割上，因此留下了一个关键的空白。 实例级别的RGB-D分割数据集相对稀缺，这使得当前方法只能进行广泛的类别区分，而不是充分捕捉识别单个物体所需的细粒度细节。 为了弥补这一差距，我们引入了三个在实例级别上有所区别的RGB-D实例分割基准。 这些数据集具有通用性，支持从室内导航到机器人操作的广泛应用。 此外，我们在这些基准上对各种基线模型进行了全面评估。 这项综合分析确定了它们的优势和不足，为未来的研究指明了更加稳健、可推广的方向。 最后，我们提出了一种简单但有效的RGB-D数据整合方法。 大量评估验证了我们方法的有效性，为实现更细致的场景理解提供了稳健的框架。 显示英文摘要

摘要： Image segmentation is a vital task for providing human assistance and enhancing autonomy in our daily lives. In particular, RGB-D segmentation-leveraging both visual and depth cues-has attracted increasing attention as it promises richer scene understanding than RGB-only methods. However, most existing efforts have primarily focused on semantic segmentation and thus leave a critical gap. There is a relative scarcity of instance-level RGB-D segmentation datasets, which restricts current methods to broad category distinctions rather than fully capturing the fine-grained details required for recognizing individual objects. To bridge this gap, we introduce three RGB-D instance segmentation benchmarks, distinguished at the instance level. These datasets are versatile, supporting a wide range of applications from indoor navigation to robotic manipulation. In addition, we present an extensive evaluation of various baseline models on these benchmarks. This comprehensive analysis identifies both their strengths and shortcomings, guiding future work toward more robust, generalizable solutions. Finally, we propose a simple yet effective method for RGB-D data integration. Extensive evaluations affirm the effectiveness of our approach, offering a robust framework for advancing toward more nuanced scene understanding.