This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. Compared with cameras and lidars, millimeter-wave radars have the advantage of not revealing privacy, having a strong anti-interference ability, and having long detection distance. The sparsity and capturing temporal-topological features of mmWave data is still a problem. However, the issue of capturing the temporal-topological coupling features under the human semantic segmentation task prevents previous advanced segmentation methods (e.g PointNet, PointCNN, Point Transformer) from being well utilized in practical scenarios. To address the challenge caused by the sparsity and temporal-topological feature of the data, we (i) introduce graph structure and topological features to the point cloud, (ii) propose a semantic segmentation framework including a global feature-extracting module and a sequential feature-extracting module. In addition, we design an efficient and more fitting loss function for a better training process and segmentation results based on graph clustering. Experimentally, we deploy representative semantic segmentation algorithms (Transformer, GCNN, etc.) on a custom dataset. Experimental results indicate that our model achieves mean accuracy on the custom dataset by $\mathbf{82.31}\%$ and outperforms the state-of-the-art algorithms. Moreover, to validate the model's robustness, we deploy our model on the well-known S3DIS dataset. On the S3DIS dataset, our model achieves mean accuracy by $\mathbf{92.6}\%$, outperforming baseline algorithms.

翻译：本文提出了一种针对毫米波雷达稀疏时序点云的语义分割框架。与摄像头和激光雷达相比，毫米波雷达具有不泄露隐私、抗干扰能力强以及探测距离远等优势。然而，毫米波雷达数据的稀疏性及其时空拓扑特征的捕捉仍是待解决的问题。在人体语义分割任务中，如何提取时空耦合特征限制了现有先进分割方法（如PointNet、PointCNN、Point Transformer）在现实场景中的有效应用。为应对数据稀疏性及时空拓扑特征带来的挑战，我们：(i) 向点云中引入图结构与拓扑特征，(ii) 提出包含全局特征提取模块与时序特征提取模块的语义分割框架。此外，我们设计了一种基于图聚类的高效且更适配的损失函数，以优化训练过程并提升分割效果。在实验中，我们在自建数据集上部署了代表性语义分割算法（如Transformer、GCNN等）。实验结果表明，本模型在自建数据集上的平均准确率达到$\mathbf{82.31}\%$，优于现有最优算法。为进一步验证模型鲁棒性，我们在公开的S3DIS数据集上进行了部署，其平均准确率达$\mathbf{92.6}\%$，同样超越基线算法。