Multi-view indoor radar perception has drawn attention due to its cost-effectiveness and low privacy risks. Existing methods often rely on {implicit} cross-view radar feature association, such as proposal pairing in RFMask or query-to-feature cross-attention in RETR, which can lead to ambiguous feature matches and degraded detection in complex indoor scenes. To address these limitations, we propose \textbf{REXO} (multi-view Radar object dEtection with 3D bounding boX diffusiOn), which lifts the 2D bounding box (BBox) diffusion process of DiffusionDet into the 3D radar space. REXO utilizes these noisy 3D BBoxes to guide an {explicit} cross-view radar feature association, enhancing the cross-view radar-conditioned denoising process. By accounting for prior knowledge that the person is in contact with the ground, REXO reduces the number of diffusion parameters by determining them from this prior. Evaluated on two open indoor radar datasets, our approach surpasses state-of-the-art methods by a margin of +4.22 AP on the HIBER dataset and +11.02 AP on the MMVR dataset. The REXO implementation is available at https://github.com/merlresearch/radar-bbox-diffusion.

翻译：多视角室内雷达感知因其成本效益高和隐私风险低而受到关注。现有方法通常依赖于隐式的跨视角雷达特征关联，例如RFMask中的候选框配对或RETR中的查询到特征交叉注意力，这在复杂室内场景中可能导致模糊的特征匹配和检测性能下降。为解决这些局限性，我们提出了\textbf{REXO}（基于三维边界框扩散的多视角雷达目标检测），它将DiffusionDet的二维边界框扩散过程提升至三维雷达空间。REXO利用这些含噪声的三维边界框来指导显式的跨视角雷达特征关联，从而增强基于雷达条件的跨视角去噪过程。通过考虑人体与地面接触的先验知识，REXO通过从该先验确定扩散参数，减少了参数数量。在两个公开的室内雷达数据集上评估，我们的方法在HIBER数据集上以+4.22 AP的优势、在MMVR数据集上以+11.02 AP的优势超越了现有最先进方法。REXO的实现可在https://github.com/merlresearch/radar-bbox-diffusion获取。