RF4D：面向户外动态场景新视角合成的神经雷达场
RF4D:Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes
将毫米波雷达的物理功率渲染公式与神经场结合，并引入场景流模块强制时序占用一致性，为恶劣天气下的动态场景新视角合成提供了可复用的雷达-神经场建模范式。
原文依据 we present RF4D, a radar-based neural field framework tailored for novel view synthesis in outdoor dynamic scenes.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论神经场在新视角合成与场景重建中取得了显著成功。然而，现有依赖RGB或激光雷达输入的神经场方法在恶劣天气条件下往往表现不佳，限制了其在自动驾驶等真实户外环境中的鲁棒性。相比之下，毫米波雷达对环境变化具有内在的鲁棒性，但其与神经场的结合仍鲜有探索。此外，户外驾驶场景频繁包含动态物体，使得时空建模对于时序一致的新视角合成至关重要。为应对这些挑战，我们提出RF4D，一个专为户外动态场景新视角合成设计的基于雷达的神经场框架。RF4D在其表示中显式融入时序信息，从而能够更准确地建模物体运动。一个专用的场景流模块进一步预测相邻帧之间的时序偏移，在动态场景重建过程中强制时序占用一致性。此外，我们提出了一种基于雷达感知物理的雷达专用功率渲染公式，提升了合成精度与可解释性。在公开雷达数据集上的大量实验表明，RF4D在雷达测量合成与占用估计精度上显著优于现有方法，在动态户外环境中增益尤为突出。
英文原文摘要 1442 字符
Neural fields (NFs) have achieved remarkable success in scene reconstruction and novel view synthesis. However, existing NF approaches that rely on RGB or LiDAR inputs often struggle under adverse weather conditions, limiting their robustness in real-world outdoor environments such as autonomous driving. In contrast, millimeter-wave radar is inherently resilient to environmental variations, yet its integration with NFs remains largely underexplored. Moreover, outdoor driving scenes frequently involve dynamic objects, making spatiotemporal modeling crucial for temporally consistent novel view synthesis. To address these challenges, we present RF4D, a radar-based neural field framework tailored for novel view synthesis in outdoor dynamic scenes. RF4D explicitly incorporates temporal information into its representation, enabling more accurate modeling of object motion. A dedicated scene flow module further predicts temporal offsets between adjacent frames, enforcing temporal occupancy coherence during dynamic scene reconstruction. Moreover, we propose a radar-specific power rendering formulation grounded in radar sensing physics, improving both synthesis accuracy and interpretability. Extensive experiments on public radar datasets demonstrate that RF4D substantially outperforms existing methods in radar measurement synthesis and occupancy estimation accuracy, with particularly strong gains in dynamic outdoor environments.