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

服务机器人一般需要独立工作，适应环境的动态变化。此类场景中的一个重要方面是不断学习在新对象可用时识别它们。这结合了两个主要研究问题，即持续学习和 3D 对象识别。大多数现有的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)。可能需要改进的方法来持续学习 3D 对象。使用 CNN 的一个主要问题是模型尝试学习新数据时的灾难性遗忘问题。尽管最近提出了各种解决方案来缓解这个问题，但这些解决方案仍然存在一些副作用（例如时间/计算复杂性）。我们提出了一种能够以开放式方式学习 3D 对象的模型，通过采用基于深度迁移学习的方法结合动态可扩展层，这也确保了这些副作用在很大程度上被最小化。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设定了新的最先进标准。