# Steady-state Non-Line-of-Sight Imaging

arXiv: https://arxiv.org/abs/1811.09910 · HTML: https://mlsanity.com/paper/1811.09910/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2018.

## Built on by (most cited first, 16 of 16)

- [Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar](https://mlsanity.com/paper/2306.09909/) (2023)
- [Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes](https://mlsanity.com/paper/2104.13807/) (2021)
- [What You Can Learn by Staring at a Blank Wall](https://mlsanity.com/paper/2108.13027/) (2021)
- [Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization](https://mlsanity.com/paper/2211.15367/) (2022)
- [Role of Transients in Two-Bounce Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2304.01308/) (2023)
- [The Boombox: Visual Reconstruction from Acoustic Vibrations](https://mlsanity.com/paper/2105.08052/) (2021)
- [Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction](https://mlsanity.com/paper/2203.08657/) (2022)
- [Noise-adapted Neural Operator for Robust Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2508.09655/) (2025)
- [A comprehensive study of time-of-flight non-line-of-sight imaging](https://mlsanity.com/paper/2603.09548/) (2026)
- [Fast Non-line-of-sight Imaging with Two-step Deep Remapping](https://mlsanity.com/paper/2101.10492/) (2021)
- [NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data](https://mlsanity.com/paper/2403.19376/) (2024)
- [PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot](https://mlsanity.com/paper/2404.05024/) (2024)
- [Fast and Memory-efficient Non-line-of-sight Imaging with Quasi-Fresnel Transform](https://mlsanity.com/paper/2508.02003/) (2025)
- [Wave-Former: Through-Occlusion 3D Reconstruction via Wireless Shape Completion](https://mlsanity.com/paper/2511.14152/) (2025)
- [A Renderer-Enabled Framework for Computing Parameter Estimation Lower Bounds in Plenoptic Imaging Systems](https://mlsanity.com/paper/2602.00215/) (2026)
- [Imaging Hidden Objects with Consumer LiDAR via Motion Induced Sampling](https://mlsanity.com/paper/2605.17865/) (2026)

## Abstract (arXiv)

Conventional intensity cameras recover objects in the direct line-of-sight of the camera, whereas occluded scene parts are considered lost in this process. Non-line-of-sight imaging (NLOS) aims at recovering these occluded objects by analyzing their indirect reflections on visible scene surfaces. Existing NLOS methods temporally probe the indirect light transport to unmix light paths based on their travel time, which mandates specialized instrumentation that suffers from low photon efficiency, high cost, and mechanical scanning. We depart from temporal probing and demonstrate steady-state NLOS imaging using conventional intensity sensors and continuous illumination. Instead of assuming perfectly isotropic scattering, the proposed method exploits directionality in the hidden surface reflectance, resulting in (small) spatial variation of their indirect reflections for varying illumination. To tackle the shape-dependence of these variations, we propose a trainable architecture which learns to map diffuse indirect reflections to scene reflectance using only synthetic training data. Relying on consumer color image sensors, with high fill factor, high quantum efficiency and low read-out noise, we demonstrate high-fidelity color NLOS imaging for scene configurations tackled before with picosecond time resolution.