The millimeter-wave radar sensor maintains stable performance under adverse
environmental conditions, making it a promising solution for all-weather
perception tasks, such as outdoor mobile robotics. However, the radar point
clouds are relatively sparse and contain massive ghost points, which greatly
limits the development of mmWave radar technology. In this paper, we propose a
novel point cloud super-resolution approach for 3D mmWave radar data, named
Radar-diffusion. Our approach employs the diffusion model defined by
mean-reverting stochastic differential equations(SDE). Using our proposed new
objective function with supervision from corresponding LiDAR point clouds, our
approach efficiently handles radar ghost points and enhances the sparse mmWave
radar point clouds to dense LiDAR-like point clouds. We evaluate our approach
on two different datasets, and the experimental results show that our method
outperforms the state-of-the-art baseline methods in 3D radar super-resolution
tasks. Furthermore, we demonstrate that our enhanced radar point cloud is
capable of downstream radar point-based registration tasks.

本论文提出了一种新的点云超分辨率方法，名为雷达扩散。该方法使用受均值回归的随机微分方程定义的扩散模型，并通过与对应的 LiDAR 点云的监督来处理雷达幽灵点，将稀疏的毫米波雷达点云增强为稠密的类似 LiDAR 的点云。通过在两个不同的数据集上评估，实验结果表明我们的方法在 3D 雷达超分辨率任务中优于现有的基准方法。此外，我们证明了我们增强的雷达点云能够进行下游的基于点云的雷达配准任务。

基于扩散的毫米波雷达点云超分辨率

Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data

Point cloud super-resolution is a fundamental problem for 3D reconstruction
and 3D data understanding. It takes a low-resolution (LR) point cloud as input
and generates a high-resolution (HR) point cloud with rich details. In this
paper, we present a data-driven method for point cloud super-resolution based
on graph networks and adversarial losses. The key idea of the proposed network
is to exploit the local similarity of point cloud and the analogy between LR
input and HR output. For the former, we design a deep network with graph
convolution. For the latter, we propose to add residual connections into graph
convolution and introduce a skip connection between input and output. The
proposed network is trained with a novel loss function, which combines Chamfer
Distance (CD) and graph adversarial loss. Such a loss function captures the
characteristics of HR point cloud automatically without manual design. We
conduct a series of experiments to evaluate our method and validate the
superiority over other methods. Results show that the proposed method achieves
the state-of-the-art performance and have a good generalization ability to
unseen data.

本论文提出了一种基于图网络和对抗性损失的数据驱动的点云超分辨率方法，利用局部相似性和输入输出之间的类比，在图卷积中添加残差连接和输入输出之间的跳跃连接，结合了 Chamfer Distance 和对抗性损失的新型损失函数，实现了自动提取高分辨率点云的特征，实验证明该方法的性能优于其他方法并具有较好的泛化能力。