Learning discriminative feature directly on point clouds is still challenging in the understanding of 3D shapes. Recent methods usually partition point clouds into local region sets, and then extract the local region features with fixed-size CNN or MLP, and finally aggregate all individual local features into a global feature using simple max pooling. However, due to the irregularity and sparsity in sampled point clouds, it is hard to encode the fine-grained geometry of local regions and their spatial relationships when only using the fixed-size filters and individual local feature integration, which limit the ability to learn discriminative features. To address this issue, we present a novel Local-Region-Context Network (LRC-Net), to learn discriminative features on point clouds by encoding the fine-grained contexts inside and among local regions simultaneously. LRC-Net consists of two main modules. The first module, named intra-region context encoding, is designed for capturing the geometric correlation inside each local region by novel variable-size convolution filter. The second module, named inter-region context encoding, is proposed for integrating the spatial relationships among local regions based on spatial similarity measures. Experimental results show that LRC-Net is competitive with state-of-the-art methods in shape classification and shape segmentation applications.

在理解3D形状时，直接在点云上学习判别特征仍然具有挑战性。最近的方法通常将点云划分为局部区域集，然后使用固定大小的CNN或MLP提取局部区域特征，最后使用简单的最大池将所有单个局部特征聚合为全局特征。但是，由于采样点云中的不规则性和稀疏性，仅使用固定大小的滤镜和单个局部特征积分时，很难对局部区域的细粒度几何及其空间关系进行编码，这限制了学习能力区别特征。为了解决这个问题，我们提出了一个新颖的本地上下文网络（LRC-Net），通过同时编码局部区域内部和局部区域之间的细粒度上下文来学习点云上的判别特征。LRC-Net由两个主要模块组成。第一个模块，名为区域内上下文编码旨在通过新颖的可变大小卷积滤波器捕获每个局部区域内的几何相关性。提出了第二个模块，称为区域间上下文编码，用于基于空间相似性度量来集成局部区域之间的空间关系。实验结果表明，LRC-Net在形状分类和形状分割应用中与最先进的方法相比具有竞争力。