RISE: Single Static Radar-based Indoor Scene Understanding

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 layouts reconstruction and object detection.Our benchmark contains 50,000 frames collected across 100 real indoor trajectories, forming the first large-scale dataset dedicated to 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 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.