The representation of 3D data is the key issue for shape analysis. However, most of the existing representations suffer from high computational cost and structure information loss. This paper presents a novel sequential slice representation with an attention-embedding network, named RSSNet, for 3D point cloud recognition and retrieval in road environments. RSSNet has two main branches. Firstly, a sequential slice module is designed to map disordered 3D point clouds to ordered sequence of shallow feature vectors. A gated recurrent unit (GRU) module is applied to encode the spatial and content information of these sequential vectors. The second branch consists of a key-point based graph convolution network (GCN) with an embedding attention strategy to fuse the sequential and global features to refine the structure discriminability. Three datasets were used to evaluate the proposed method, one acquired by our mobile laser scanning (MLS) system and two public datasets (KITTI and Sydney Urban Objects). Experimental results indicated that the proposed method achieved better performance than recognition and retrieval state-of-the-art methods. RSSNet provided recognition rates of 98.08%, 95.77% and 70.83% for the above three datasets, respectively. For the retrieval task, RSSNet obtained excellent mAP values of 95.56%, 87.16% and 69.99% on three datasets, respectively.

3D数据的表示形式是形状分析的关键问题。然而，大多数现有的表示遭受高计算成本和结构信息损失的困扰。本文提出了一种新型的具有注意嵌入网络的顺序切片表示形式，名为RSSNet，用于道路环境中的3D点云识别和检索。RSSNet有两个主要分支。首先，顺序切片模块设计为将无序3D点云映射到浅层特征向量的有序序列。门控循环单元（GRU）模块用于对这些顺序向量的空间和内容信息进行编码。第二个分支由基于关键点的图卷积网络（GCN）组成，该图卷积网络具有嵌入注意策略，可融合顺序特征和全局特征以改善结构可分辨性。使用三个数据集来评估该方法，一个是通过我们的移动激光扫描（MLS）系统获取的，另一个是两个公共数据集（KITTI和Sydney Urban Objects）。实验结果表明，该方法取得了比识别和检索最新技术更好的性能。RSSNet对上述三个数据集的识别率分别为98.08％，95.77％和70.83％。对于检索任务，RSSNet在三个数据集上分别获得了95.56％，87.16％和69.99％的出色mAP值。RSSNet对上述三个数据集的识别率分别为98.08％，95.77％和70.83％。对于检索任务，RSSNet在三个数据集上分别获得了95.56％，87.16％和69.99％的出色mAP值。RSSNet对上述三个数据集的识别率分别为98.08％，95.77％和70.83％。对于检索任务，RSSNet在三个数据集上分别获得了95.56％，87.16％和69.99％的出色mAP值。