用扩散模型将雷达点云稀疏数据变稠密，生成接近激光雷达质量的4D点云。
R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model
- 基于体素特征和潜在空间扩散模型，实现雷达点云超分辨率重建。
- 雷达点云密度提升6至10倍，点云配准召回率提升31.7%。
- 适合需要高精度雷达感知的自动驾驶场景，尤其关注点云质量提升。
我们提出R2LDM，一种利用对应激光雷达点云引导生成密集且精确4D雷达点云的新方法。不同于传统的距离图像或俯视图表示，本方法采用体素特征表示激光雷达与4D雷达点云，更有效捕捉三维形状信息。进一步地，提出潜在体素扩散模型（LVDM），在潜在空间中进行扩散过程，并引入新型潜在点云重建（LPCR）模块，从高维潜在体素特征中重建点云。结果表明，R2LDM能有效从原始雷达数据生成类激光雷达点云。我们在两个不同数据集上评估该方法，实验显示模型实现雷达点云6至10倍的密度提升，优于现有最先进基线。此外，生成的增强雷达点云显著提升下游任务性能，点云配准召回率最高提升31.7%，目标检测准确率提升24.9%。
原文摘要 · Abstract (English)
We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or bird's eye view (BEV) images, we represent both LiDAR and 4D radar point clouds using voxel features, which more effectively capture 3D shape information. Subsequently, we propose the Latent Voxel Diffusion Model (LVDM), which performs the diffusion process in the latent space. Additionally, a novel Latent Point Cloud Reconstruction (LPCR) module is utilized to reconstruct point clouds from high-dimensional latent voxel features. As a result, R2LDM effectively generates LiDAR-like point clouds from paired raw radar data. We evaluate our approach on two different datasets, and the experimental results demonstrate that our model achieves 6- to 10-fold densification of radar point clouds, outperforming state-of-the-art baselines in 4D radar point cloud super-resolution. Furthermore, the enhanced radar point clouds generated by our method significantly improve downstream tasks, achieving up to 31.7% improvement in point cloud registration recall rate and 24.9% improvement in object detection accuracy.
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