The major challenge of 3D point cloud classification using a deep neural network is to handle the naturally unordered data structure. Recently, the PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of its local neighborhood that contains fine-grained structural information, which turns out to be helpful toward better semantic learning. To tackle this challenge, this paper proposes a new deep neural network architecture, called RadialNet , which applies radial basis function to exploit local structure representation point cloud data and then further incorporates into the conventional neural network classification framework. More specifically, the proposed architecture exploits a novel type of representation that uses evenly spaced centroids, which uses radial basis function to get weight for each input point to have order invariant and retain the global information of the point cloud. By the effective exploration of the point cloud local structure using the RadialNet, the proposed architecture achieves competitive performance on the 3D object classification benchmark ModelNet dataset.

中文翻译：

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RadialNet：一种使用具有径向基函数的局部结构表示的点云分类方法

使用深度神经网络进行 3D 点云分类的主要挑战是处理自然无序的数据结构。最近，PointNet 通过直接在点集上学习取得了可喜的成果。然而，它没有充分利用包含细粒度结构信息的局部邻域，这有助于更好地学习语义。为了应对这一挑战，本文提出了一种新的深度神经网络架构，称为 RadialNet ，它应用径向基函数来利用局部结构表示点云数据，然后进一步融入传统的神经网络分类框架。更具体地说，所提出的架构利用了一种使用均匀分布的质心的新型表示，它使用径向基函数为每个输入点获取权重以具有顺序不变性并保留点云的全局信息。通过使用 RadialNet 对点云局部结构的有效探索，所提出的架构在 3D 对象分类基准 ModelNet 数据集上实现了具有竞争力的性能。