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 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 92.6%, outperforming baseline algorithms