Three-dimensional (3D) object recognition technology is being used as a core technology in advanced technologies such as autonomous driving of automobiles. There are two sets of approaches for 3D object recognition: (i) hand-crafted approaches like Global Orthographic Object Descriptor (GOOD), and (ii) deep learning-based approaches such as MobileNet and VGG. However, it is needed to know which of these approaches works better in an open-ended domain where the number of known categories increases over time, and the system should learn about new object categories using few training examples. In this paper, we first implemented an offline 3D object recognition system that takes an object view as input and generates category labels as output. In the offline stage, instance-based learning (IBL) is used to form a new category and we use K-fold cross-validation to evaluate the obtained object recognition performance. We then test the proposed approach in an online fashion by integrating the code into a simulated teacher test. As a result, we concluded that the approach using deep learning features is more suitable for open-ended fashion. Moreover, we observed that concatenating the hand-crafted and deep learning features increases the classification accuracy.

翻译：三维物体识别技术正作为自动驾驶等先进技术的核心技术被广泛应用。三维物体识别存在两类方法：（i）手工设计方法，如全局正交物体描述符（GOOD）；（ii）基于深度学习的方法，如MobileNet和VGG。然而，在开放领域（已知类别数量随时间增加，且系统需通过少量训练样本学习新类别）中，需明确哪种方法表现更优。本文首先实现了一个离线三维物体识别系统，该系统以物体视图为输入并输出类别标签。在离线阶段，采用基于实例的学习（IBL）形成新类别，并通过K折交叉验证评估所得物体识别性能。随后，我们将代码集成到模拟教师测试中，以在线方式验证所提方法。实验结果表明，基于深度学习特征的方法更适用于开放场景。此外，我们观察到连接手工特征与深度学习特征可提升分类准确率。