RISE：基于单静态雷达的室内场景理解
RISE: Single Static Radar-based Indoor Scene Understanding
AI总结：
提出RISE系统，利用毫米波雷达的多径反射（传统视为噪声）编码几何线索，通过双角度多径增强和模拟到现实的分层扩散框架，实现布局重建和物体检测，在50,000帧数据集上布局重建倒角距离降低60%，首次实现基于毫米波雷达的物体检测。
摘要：
鲁棒且保护隐私的室内场景理解仍然是一个基本开放问题。虽然光学传感器（如RGB和LiDAR）提供高空间保真度，但它们在室内环境中遭受严重遮挡并引入隐私风险。相比之下，毫米波雷达保护隐私并穿透障碍物，但其固有的低空间分辨率使得可靠的几何推理变得困难。我们介绍了RISE，这是首个用于单静态雷达室内场景理解的基准和系统，同时针对布局重建和物体检测。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。为了利用这一点，我们提出了一种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，一个模拟到现实的分层扩散框架将碎片化的雷达响应转化为完整的布局重建和物体检测。我们的基准包含100条真实室内轨迹中收集的50,000帧数据，形成了首个专门用于单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雷达的物体检测，IoU达到58%。这些结果确立了RISE作为使用单静态雷达进行几何感知和隐私保护室内场景理解的新基础。我们的网站和代码可在https://rise-cvpr.github.io获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
