The feature analysis of point clouds, a popular representation of three-dimensional (3D) objects, is rising as a hot research topic nowadays. Point cloud data bear a sparse and unordered nature, making many commonly used feature extraction methods, for example, Convolutional Neural Networks (CNNs) inapplicable, while previous models suitable for the task are usually complex. We aim to reduce model complexity by reducing the number of parameters while achieving better (or at least comparable) performance. We propose an Interpolation Graph Convolutional Network (IGCN) for extracting features of point clouds. IGCN uses the point cloud graph structure and a specially designed Interpolation Convolution Kernel to mimic the operations of CNN for feature extraction. On the basis of weight postfusion and multilevel-resolution aggregation, IGCN not only reduces the cost of calculating the interpolation operation but also improves the model's performance. We validate the performance of IGCN on both point cloud classification and segmentation tasks and explore the contribution of each module of our model through ablation experiments. Furthermore, we embed the IGCN point cloud feature extraction module as a plug-and-play module into other frameworks and perform point cloud registration experiments.

中文翻译：

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用于 3D 点云分析的插值图卷积网络

点云是三维 (3D) 对象的一种流行表示，其特征分析正在成为当今的热门研究课题。点云数据具有稀疏和无序的性质，使得许多常用的特征提取方法，例如卷积神经网络（CNN）不适用，而以前适用于该任务的模型通常很复杂。我们的目标是通过减少参数数量来降低模型复杂性，同时实现更好（或至少可比）的性能。我们提出了一种用于提取点云特征的插值图卷积网络 (IGCN)。IGCN 使用点云图结构和专门设计的插值卷积核来模仿 CNN 的操作进行特征提取。在权重后融合和多级分辨率聚合的基础上，IGCN不仅降低了插值运算的计算成本，还提高了模型的性能。我们验证了 IGCN 在点云分类和分割任务上的性能，并通过消融实验探索了我们模型的每个模块的贡献。此外，我们将 IGCN 点云特征提取模块作为即插即用模块嵌入到其他框架中，并进行点云配准实验。