X-band Radar Non-Line-of-Sight Imaging

Dongyu Du, Mingkun Zhao, Yutong Yang, Dominik Scheuble, Xiaolong Huang, Zijian Shao, Mario Bijelic, Kaushik Sengupta, Felix Heide; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 5647-5658

Conventional imaging systems capture objects visible in the direct line-of-sight (LOS). A decade of research on non-line-of-sight (NLOS) imaging approaches has made it possible to reconstruct hidden geometry outside the line of sight by analyzing indirect light transport. However, most existing methods operate in the optical visible or IR range. Relying on diffuse inter-reflections, every bounce incurs a quadratic intensity falloff. As such, with illumination power limited by eye-safety limitations, existing methods are fundamentally restricted to short ranges on the order of a few meters. We propose an X-band radar-based NLOS imaging method that leverages the long wavelength to convert diffuse reflections into predominantly specular ones, allowing for large-scale hidden-scene perception. We develop a neural reconstruction method that combines a learned dense prediction module and a geometry-aware NLOS reconstruction module, tackling the inherently low spatial resolution of long-wavelength radar. We assess our method using a prototype system and in simulation. Synthetic validation shows that, under the same transmit power, X-band radar achieves 10xlonger NLOS reconstruction range than optical systems, while experimental results further demonstrate accurate hidden-object reconstructions up to 40 m, establishing a practical pathway toward real-world long-range NLOS sensing.