Semantic segmentation of large unstructured 3D point clouds is important problem for 3D object recognition which in turn is essential to solving more complex tasks such as scene understanding. The problem is highly challenging owing to large scale of data, varying point density and localization errors of 3D points. Nevertheless, with recent successes of deep neural network architectures to solve complex 2D perceptual problems, several researchers have shown interest to translate the developed 2D networks to 3D point cloud segmentation by a prior voxelization step for an explicit neighborhood representation. However, such a 3D grid representation loses the fine details and inherent structure due to quantization artifacts. For this purpose, this paper proposes an approach to performing semantic segmentation of 3D point clouds by exploiting the idea of super-point based graph construction. The proposed architecture is composed of two cascaded modules including a light-weight representation learning module which uses unsupervised geometric grouping to partition the large-scale unstructured 3D point cloud and a deep context aware sequential network based on long short memory units and graph convolutions with embedding residual learning for semantic segmentation. The proposed model is evaluated on two standard benchmark datasets and achieves competitive performance with the existing state-of-the-art datasets. The code and the obtained results have been made public at https://github.com/saba155/DCARN.

大型非结构化3D点云的语义分割是3D对象识别的重要问题，而3D对象识别又是解决诸如场景理解之类的更复杂任务所必不可少的。由于数据规模大，点密度变化和3D点的定位误差，该问题极具挑战性。然而，随着深度神经网络体系结构解决复杂2D感知问题的最新成功，一些研究人员显示出兴趣，可以通过先前的体素化步骤将发达的2D网络转换为3D点云分割，以进行显式的邻域表示。然而，由于量化伪像，这种3D网格表示失去了精细的细节和固有的结构。以此目的，本文提出了一种利用基于超点的图构造思想对3D点云进行语义分割的方法。所提出的架构由两个级联模块组成，其中包括轻量表示学习模块，该模块使用无监督的几何分组来划分大型非结构化3D点云，以及基于长短存储单元和图卷积并嵌入的深度上下文感知顺序网络语义分割的残差学习。所提出的模型在两个标准基准数据集上进行了评估，并与现有的最新数据集实现了竞争性能。该代码和获得的结果已在https://github.com/saba155/DCARN上公开。所提出的架构由两个级联模块组成，其中包括轻量表示学习模块，该模块使用无监督的几何分组来划分大型非结构化3D点云，以及基于长短存储单元和图卷积并嵌入的深度上下文感知顺序网络语义分割的残差学习。所提出的模型在两个标准基准数据集上进行了评估，并与现有的最新数据集实现了竞争性能。该代码和获得的结果已在https://github.com/saba155/DCARN上公开。所提出的体系结构由两个级联模块组成，其中包括轻量表示学习模块，该模块使用无监督的几何分组来划分大型非结构化3D点云，以及基于长短存储单元和图卷积并嵌入的深度上下文感知顺序网络语义分割的残差学习。所提出的模型在两个标准基准数据集上进行了评估，并与现有的最新数据集实现了竞争性能。该代码和获得的结果已在https://github.com/saba155/DCARN上公开。