Abstract The acquisition of point clouds with a 3D scanner often yields large-scale, irregular, and unordered raw data, which hinders the classification of objects from these data. Some studies have introduced a method of applying the point clouds to convolutional neural networks (CNNs). This is achieved after preprocessing the volume metrics or multi-view images. However, this method has a limited resolution and a low classification accuracy in comparison to heavy computation in object classification. In this paper, DenX-Conv is proposed to improve the accuracy of object classification while securing the connectivity of points from the raw point cloud. DenX-Conv can extract effective local geometric features by finding the neighbor connectivity based on the geometric topology information of the points. In addition, stable feature learning is made possible by applying a densely connected network to PointCNN's χ-Conv. Application of DenX-Conv to the ModelNet40 dataset resulted in a classification accuracy of 92.5%.

中文翻译：

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用于分类点云的基于连通性的卷积神经网络

摘要 用3D扫描仪采集点云往往会产生大规模、不规则和无序的原始数据，这阻碍了从这些数据中对对象进行分类。一些研究介绍了一种将点云应用于卷积神经网络 (CNN) 的方法。这是在预处理体积指标或多视图图像后实现的。然而，与对象分类中的大量计算相比，该方法具有有限的分辨率和低的分类精度。在本文中，DenX-Conv 被提出来提高对象分类的准确性，同时确保原始点云中点的连通性。DenX-Conv 可以通过基于点的几何拓扑信息寻找邻居连通性来提取有效的局部几何特征。此外，通过将密集连接的网络应用于 PointCNN 的 χ-Conv，可以实现稳定的特征学习。DenX-Conv 在 ModelNet40 数据集上的应用导致了 92.5% 的分类准确率。