RISE: Single Static Radar-based Indoor Scene Understanding

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

RISE is a novel benchmark and system designed for robust, privacy-preserving indoor scene understanding using a single static millimeter-wave (mmWave) radar. It jointly addresses layout reconstruction and object detection, overcoming limitations of optical sensors like occlusions and privacy risks, and radar's inherently low spatial resolution. RISE leverages the insight that multipath reflections, traditionally considered noise, contain rich geometric cues. The system employs a Bi-Angular Multipath Enhancement (BAME) module to model Angle-of-Arrival and Angle-of-Departure, recovering "ghost" reflections and invisible structures. A subsequent Simulation-to-Reality Hierarchical Diffusion (SRHD) framework transforms fragmented radar data into complete layouts and object detections. The RISE benchmark comprises 50,000 frames from 100 real indoor trajectories. Experiments show RISE reduces Chamfer Distance by 60% to 16 cm for layout reconstruction and achieves 58% IoU for object detection, significantly surpassing the state of the art.

Key takeaway

For robotics engineers or smart home developers evaluating indoor sensing solutions, RISE demonstrates a compelling alternative to optical sensors. If you prioritize privacy and robust performance through occlusions, consider single static mmWave radar. This approach, employing multipath reflections and diffusion models, enables accurate layout reconstruction (16 cm Chamfer Distance) and object detection (58% IoU) without cameras, even with natural human movement.

Key insights

RISE utilizes multipath radar reflections and generative AI for privacy-preserving, geometry-aware indoor scene understanding from a single static sensor.

Principles

• Multipath reflections, traditionally noise, encode rich geometric cues.
• Separating AOA and AOD processing enhances ghost signal detection.
• Human motion provides geometric variation for uncovering occluded regions.

Method

RISE uses Bi-Angular Multipath Enhancement to recover ghost reflections, followed by multipath inversion for reflector estimation. A Sim2Real Hierarchical Diffusion model then reconstructs complete layouts and detects objects.

In practice

• Use mmWave radar for privacy-sensitive indoor monitoring.
• Use multipath for occluded structure detection.
• Apply diffusion models for fragmented radar data completion.

Topics

• Millimeter-wave Radar
• Indoor Scene Understanding
• Multipath Reflections
• Layout Reconstruction
• Object Detection
• Diffusion Models
• Privacy-Preserving Sensing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.