Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss function is utilized to learn to produce pixel-wise feature embeddings such that pixels from the same object are close to each other and pixels from different objects are separated in the embedding space. With the learned feature embeddings, a mean shift clustering algorithm can be applied to discover and segment unseen objects. We further improve the segmentation accuracy with a new two-stage clustering algorithm. Our method demonstrates that non-photorealistic synthetic RGB and depth images can be used to learn feature embeddings that transfer well to real-world images for unseen object instance segmentation.

翻译：在杂乱的场景中分割看不见的物体是机器人为了在新环境中执行任务而需要获得的重要技能。 在这项工作中,我们提出一种新的方法,通过从合成数据中学习 RGB-D 特性嵌入功能来进行不可见的物体分解。 使用一个衡量学习损失功能来学习产生像素特性嵌入, 以便同一物体的像素彼此接近, 并且将不同物体的像素分离在嵌入空间中。 有了所学的特性嵌入, 一种平均转移组合算法可以应用于发现和分解不可见的物体。 我们用一个新的两阶段组合算法来进一步提高分解的精确性。 我们的方法表明, 非光学合成RGB 和深度图像可以用来学习特性嵌入, 从而顺利地传输到真实世界的图像, 用于隐蔽的物体分解 。