Point clouds with rich local geometric information have potentially huge implications in several applications, especially in areas of robotic manipulation and autonomous driving. However, most point cloud processing methods cannot extract enough geometric features from a raw point cloud, which restricts the performance of their downstream tasks such as point cloud classification, shape retrieval and part segmentation. In this paper, the authors propose a new method where a convolution based on geometric primitives is adopted to accurately represent the elusive shape in the form of a point cloud to fully extract hidden geometric features. The key idea of the proposed approach is building a brand-new convolution net named ResSANet on the basis of geometric primitives to learn hierarchical geometry information. Two different modules are devised in our network, Res-SA and Res­SA­2, to achieve feature fusion at different levels in ResSANet. This work achieves classification accuracy up to 93.2% on the ModelNet40 dataset and the shape retrieval with an effect of 87.4%. The part segmentation experiment also achieves an accuracy of 83.3% (class mIoU) and 85.3% (instance mIoU) on ShapeNet dataset. It is worth mentioning that the number of parameters in this work is just 1.04M while the network depth is minimal. Experimental results and comparisons with state-of-the-art methods demonstrate that our approach can achieve superior performance.

中文翻译：

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学习几何信息以进行点云处理

具有丰富的本地几何信息的点云在多种应用中具有潜在的巨大意义，特别是在机器人操纵和自动驾驶领域。但是，大多数点云处理方法无法从原始点云中提取足够的几何特征，这限制了其下游任务（例如点云分类，形状检索和零件分割）的性能。在本文中，作者提出了一种新方法，该方法采用基于几何图元的卷积以点云的形式准确表示难以捉摸的形状，从而充分提取隐藏的几何特征。提出的方法的关键思想是在几何图元的基础上构建一个名为ResSANet的全新卷积网络，以学习分层几何信息。我们的网络中设计了两个不同的模块Res-SA和ResSA2，以实现ResSANet中不同级别的功能融合。这项工作在ModelNet40数据集和形状检索上实现了高达93.2％的分类精度，效果为87.4％。在ShapeNet数据集上，零件分割实验还实现了83.3％（类mIoU）和85.3％（实例mIoU）的精度。值得一提的是，这项工作中的参数数量仅为1.04M，而网络深度却很小。实验结果和与最先进方法的比较表明，我们的方法可以实现卓越的性能。在ShapeNet数据集上，零件分割实验还实现了83.3％（类mIoU）和85.3％（实例mIoU）的精度。值得一提的是，这项工作中的参数数量仅为1.04M，而网络深度却很小。实验结果和与最先进方法的比较表明，我们的方法可以实现卓越的性能。在ShapeNet数据集上，零件分割实验还实现了83.3％（类mIoU）和85.3％（实例mIoU）的精度。值得一提的是，这项工作中的参数数量仅为1.04M，而网络深度却很小。实验结果和与最先进方法的比较表明，我们的方法可以实现卓越的性能。