RISE：基于单一静态雷达的室内场景理解
RISE: Single Static Radar-based Indoor Scene Understanding
利用多径反射作为几何线索，通过双角度建模恢复不可见结构，并用扩散框架将稀疏雷达信号转化为完整布局与目标检测，为隐私保护下的室内三维感知提供了新思路。
原文依据 multipath reflections--traditionally treated as noise--encode rich geometric cues
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论鲁棒且隐私保护的室内场景理解仍是一个基础性开放问题。RGB 和激光雷达等光学传感器虽能提供高空间保真度，但在室内环境中易受严重遮挡并带来隐私风险。相比之下，毫米波雷达保护隐私且能穿透障碍物，但其固有的低空间分辨率使得可靠的几何推理变得困难。我们提出 RISE，首个面向单一静态雷达室内场景理解的基准与系统，同时针对布局重建和目标检测。RISE 基于一个关键洞察：多径反射——传统上被视为噪声——编码了丰富的几何线索。为此，我们提出双角度多径增强方法，显式建模到达角和离开角以恢复二次（鬼影）反射并揭示不可见结构。在这些增强观测的基础上，一个从仿真到现实的层次化扩散框架将碎片化的雷达响应转化为完整的布局重建和目标检测。我们的基准包含从 100 条真实室内轨迹采集的 50,000 帧，构成了首个专用于单一静态雷达室内场景理解的大规模数据集。大量实验表明，与最先进的毫米波布局重建方法相比，RISE 将倒角距离降低了 60%（降至 16 厘米），并实现了首个基于毫米波的目标检测，达到 58% 的 IoU。这些结果确立了 RISE 作为使用单一静态雷达进行几何感知且隐私保护的室内场景理解的新基础。我们的网站和代码见 https://rise-cvpr.github.io。
英文原文摘要 1738 字符
Robust and privacy-preserving indoor scene understanding remains a fundamental open problem. While optical sensors such as RGB and LiDAR offer high spatial fidelity, they suffer from severe occlusions and introduce privacy risks in indoor environments. In contrast, millimeter-wave (mmWave) radar preserves privacy and penetrates obstacles, but its inherently low spatial resolution makes reliable geometric reasoning difficult. We introduce RISE, the first benchmark and system for single static radar indoor scene understanding, jointly targeting layout reconstruction and object detection. RISE is built upon the key insight that multipath reflections--traditionally treated as noise--encode rich geometric cues. To exploit this, we propose a Bi-Angular Multipath Enhancement that explicitly models Angle of Arrival and Angle of Departure to recover secondary (ghost) reflections and reveal invisible structures. On top of these enhanced observations, a simulation-to-reality hierarchical diffusion framework transforms fragmented radar responses into complete layout reconstruction and object detection. Our benchmark contains 50,000 frames collected across 100 real indoor trajectories, forming the first large-scale dataset dedicated to single static radar-based indoor scene understanding. Extensive experiments show that RISE reduces the Chamfer distance by 60% (down to 16 cm) compared to the state of the art in mmWave layout reconstruction, and delivers the first mmWave-based object detection, achieving 58% IoU. These results establish RISE as a new foundation for geometry-aware and privacy-preserving indoor scene understanding using a single static radar. Our website and code are available at https://rise-cvpr.github.io.