Service robots, in general, have to work independently and adapt to the dynamic changes in the environment. One important aspect in such scenarios is to continually learn to recognize new objects when they become available. This combines two main research problems namely continual learning and 3D object recognition. Most of the existing research approaches include the use of deep Convolutional Neural Networks (CNNs) focusing on image datasets. A modified approach might be needed for continually learning 3D objects. A major concern in using CNNs is the problem of catastrophic forgetting when a model tries to learn new data. In spite of various recent proposed solutions to mitigate this problem, there still exist a few side-effects (such as time/computational complexity) of such solutions. We propose a model capable of learning 3D objects in an open-ended fashion by employing deep transfer learning-based approach combined with dynamically expandable layers, which also makes sure that these side-effects are minimized to a great extent. We show that this model sets a new state-of-the-art standard not only with regards to accuracy but also for computational complexity.

中文翻译：

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3D_DEN：使用动态可扩展网络的开放式3D对象识别

通常，服务机器人必须独立工作并适应环境的动态变化。在这种情况下，一个重要方面是不断学习在新对象可用时识别它们。这结合了两个主要的研究问题，即持续学习和3D对象识别。现有的大多数研究方法包括使用专注于图像数据集的深度卷积神经网络（CNN）。不断学习3D对象可能需要一种改进的方法。使用CNN的一个主要问题是模型尝试学习新数据时灾难性的遗忘问题。尽管最近提出了各种缓解该问题的解决方案，但是这些解决方案仍然存在一些副作用（例如时间/计算复杂性）。我们提出了一种模型，该模型可以通过采用基于深度转移学习的方法与动态可扩展图层相结合的方式，以开放式方式学习3D对象，该模型还可以确保最大程度地减少这些副作用。我们表明，该模型不仅在准确性方面而且在计算复杂性方面都设置了新的最新标准。