通过时空多普勒扩散模型，同步提升雷达点云与自车速度估计精度。
SDDiff: Boost Radar Perception via Spatial-Doppler Diffusion
- 设计时空多普勒扩散模型，联合建模雷达空间与多普勒特征
- 自车速度估计精度提升59%，有效点云生成密度达基线4倍
- 适合需要高精度感知的自动驾驶系统研发人员
点云提取（PCE）与自车速度估计（EVE）是3D雷达感知中的关键能力。现有方法通常独立处理这两项任务，忽略了雷达在空间域与多普勒域特征间的相互作用，可能引入额外偏差。本文发现3D点云与自车速度间存在潜在关联，可为两者提供互益。为此，我们首次提出时空多普勒扩散（SDDiff）模型，实现密集点云生成与精准自车速度估计的联合优化。SDDiff从三个方面改进传统隐变量扩散过程：首先，引入融合空间占据与多普勒特征的表示；其次，设计基于雷达先验的方向性扩散以加速采样；最后，提出迭代多普勒精修机制，增强对密度变化与伪影的适应能力。大量实验表明，SDDiff相比最优基线，在EVE精度上提升59%，有效生成点云密度提高4倍，同时显著提升点云提取的有效性与可靠性。
原文摘要 · Abstract (English)
Point cloud extraction (PCE) and ego velocity estimation (EVE) are key capabilities gaining attention in 3D radar perception. However, existing work typically treats these two tasks independently, which may neglect the interplay between radar's spatial and Doppler domain features, potentially introducing additional bias. In this paper, we observe an underlying correlation between 3D points and ego velocity, which offers reciprocal benefits for PCE and EVE. To fully unlock such inspiring potential, we take the first step to design a Spatial-Doppler Diffusion (SDDiff) model for simultaneously dense PCE and accurate EVE. To seamlessly tailor it to radar perception, SDDiff improves the conventional latent diffusion process in three major aspects. First, we introduce a representation that embodies both spatial occupancy and Doppler features. Second, we design a directional diffusion with radar priors to streamline the sampling. Third, we propose Iterative Doppler Refinement to enhance the model's adaptability to density variations and ghosting effects. Extensive evaluations show that SDDiff significantly outperforms state-of-the-art baselines by achieving 59% higher in EVE accuracy, 4X greater in valid generation density while boosting PCE effectiveness and reliability.
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