Given the prominence of 3D sensors in recent years, 3D point cloud scene data are worthy to be further investigated. Point cloud scene understanding is a challenging task because of its characteristics of large-scale and discrete. In this study, we propose a network called LEARD-Net, focuses on semantic segmentation for the large-scale point cloud scene data with color information. The proposed network contains three main components: (1) To fully utilize color information of point clouds rather than just as initial input features, we propose a robust local feature extraction module (LFE) to benefit the network focus on both spatial geometric structure, color information and semantic features. (2) We propose a local feature aggregation module (LFA) to benefit the network to focus on the local significant features while also focus on the entire local neighbor. (3) To allow the network to focus on both local and comprehensive features, we use residual and dense connections (ResiDense) to connect different-level LFE and LFA modules. Comparing with state-of-the-art networks on several large-scale benchmark datasets, including S3DIS, Toronto3D and Semantic3D, we demonstrate the effectiveness of our LEARD-Net.

鉴于近年来 3D 传感器的突出地位，3D 点云场景数据值得进一步研究。点云场景理解具有大规模和离散的特点，是一项具有挑战性的任务。在这项研究中，我们提出了一个名为 LEARD-Net 的网络，专注于对具有颜色信息的大规模点云场景数据进行语义分割。所提出的网络包含三个主要组件：（1）为了充分利用点云的颜色信息而不仅仅是作为初始输入特征，我们提出了一个鲁棒的局部特征提取模块（LFE），以使网络关注空间几何结构、颜色信息和语义特征。（2）我们提出了一个局部特征聚合模块（LFA），使网络能够专注于局部重要特征，同时也关注整个局部邻居。（3）为了让网络既关注局部特征又关注综合特征，我们使用残差和密集连接（ResiDense）来连接不同级别的 LFE 和 LFA 模块。与几个大型基准数据集（包括 S3DIS、Toronto3D 和 Semantic3D）上的最先进网络进行比较，我们证明了 LEARD-Net 的有效性。