Classification of 3D objects - the selection of a category in which each object belongs - is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture and representation of the 3D shape used as an input. To investigate the effectiveness of their approaches, we conduct an extensive survey of existing methods and identify common ideas by which we categorize them into a taxonomy. Second, we evaluate 11 selected classification networks on two 3D object datasets, extending the evaluation to a larger dataset on which most of the selected approaches have not been tested yet. For this, we provide a framework for converting shapes from common 3D mesh formats into formats native to each network, and for training and evaluating different classification approaches on this data. Despite being partially unable to reach the accuracies reported in the original papers, we compare the relative performance of the approaches as well as their performance when changing datasets as the only variable to provide valuable insights into performance on different kinds of data. We make our code available to simplify running training experiments with multiple neural networks with different prerequisites.

中文翻译：

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神经 3D 形状分类方法的调查和评估。

3D 对象的分类——选择每个对象所属的类别——在机器学习领域引起了极大的兴趣。许多研究人员使用深度神经网络来解决这个问题，改变网络架构和用作输入的 3D 形状的表示。为了调查他们的方法的有效性，我们对现有方法进行了广泛的调查，并确定了我们将它们分类为分类法所依据的共同思想。其次，我们在两个 3D 对象数据集上评估 11 个选定的分类网络，将评估扩展到更大的数据集，其中大多数选定的方法尚未经过测试。为此，我们提供了一个框架，用于将形状从常见的 3D 网格格式转换为每个网络的原生格式，并用于训练和评估对这些数据的不同分类方法。尽管部分无法达到原始论文中报告的准确性，但我们比较了这些方法的相对性能以及它们在更改数据集时的性能作为唯一变量，以提供对不同类型数据性能的有价值的见解。我们提供代码以简化使用具有不同先决条件的多个神经网络运行的训练实验。