3D scene reconstruction and rendering are core tasks in computer vision, with applications spanning industrial monitoring, robotics, and autonomous driving. Recent advances in 3D Gaussian Splatting (GS) and its variants have achieved impressive rendering fidelity while maintaining high computational and memory efficiency. However, conventional vision-based GS pipelines typically rely on a sufficient number of camera views to initialize the Gaussian primitives and train their parameters, typically incurring additional processing cost during initialization while falling short in conditions where visual cues are unreliable, such as adverse weather, low illumination, or partial occlusions. To cope with these challenges, and motivated by the robustness of radio-frequency (RF) signals to weather, lighting, and occlusions, we introduce a multimodal framework that integrates RF sensing, such as automotive radar, with GS-based rendering as a more efficient and robust alternative to vision-only GS rendering. The proposed approach enables efficient depth prediction from only sparse RF-based depth measurements, yielding a high-quality 3D point cloud for initializing Gaussian functions across diverse GS architectures. Numerical tests demonstrate the merits of judiciously incorporating RF sensing into GS pipelines, achieving high-fidelity 3D scene rendering driven by RF-informed structural accuracy.

翻译：三维场景重建与渲染是计算机视觉领域的核心任务，其应用涵盖工业监测、机器人技术与自动驾驶。近年来，三维高斯溅射（GS）及其变体在保持高计算与内存效率的同时，实现了令人印象深刻的渲染保真度。然而，传统的基于视觉的GS流程通常依赖足够数量的相机视角来初始化高斯基元并训练其参数，这通常会在初始化阶段产生额外的处理成本，且在视觉线索不可靠的条件下（如恶劣天气、低光照或部分遮挡）表现不佳。为应对这些挑战，并受射频（RF）信号对天气、光照和遮挡具有鲁棒性的启发，我们提出了一种多模态框架，该框架将射频感知（如汽车雷达）与基于GS的渲染相结合，作为纯视觉GS渲染的一种更高效、更鲁棒的替代方案。所提方法能够仅从稀疏的基于射频的深度测量中高效预测深度，从而生成高质量的三维点云，用于跨不同GS架构的高斯函数初始化。数值测试证明了将射频感知审慎融入GS流程的优势，实现了由射频信息驱动的结构准确性所支撑的高保真三维场景渲染。