# Virtual Wave Optics for Non-Line-of-Sight Imaging

arXiv: https://arxiv.org/abs/1810.07535 · HTML: https://mlsanity.com/paper/1810.07535/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 30 of 54)

- [Non-line-of-Sight Imaging via Neural Transient Fields](https://mlsanity.com/paper/2101.00373/) (2021)
- [Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction](https://mlsanity.com/paper/2307.09555/) (2023)
- [Passive Inter-Photon Imaging](https://mlsanity.com/paper/2104.00059/) (2021)
- [Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar](https://mlsanity.com/paper/2306.09909/) (2023)
- [Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging](https://mlsanity.com/paper/2205.01679/) (2022)
- [NLOS-NeuS: Non-line-of-sight Neural Implicit Surface](https://mlsanity.com/paper/2303.12280/) (2023)
- [Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes](https://mlsanity.com/paper/2104.13807/) (2021)
- [DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields](https://mlsanity.com/paper/2303.11364/) (2023)
- [Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization](https://mlsanity.com/paper/2211.15367/) (2022)
- [Enhancing Autonomous Navigation by Imaging Hidden Objects using Single-Photon LiDAR](https://mlsanity.com/paper/2410.03555/) (2024)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Adaptive Gating for Single-Photon 3D Imaging](https://mlsanity.com/paper/2111.15047/) (2021)
- [Virtual Light Transport Matrices for Non-Line-Of-Sight Imaging](https://mlsanity.com/paper/2103.12622/) (2021)
- [Role of Transients in Two-Bounce Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2304.01308/) (2023)
- [Robust photon-efficient imaging using a pixel-wise residual shrinkage network](https://mlsanity.com/paper/2201.01453/) (2022)
- [Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction](https://mlsanity.com/paper/2304.10780/) (2023)
- [Virtual Mirrors: Non-Line-of-Sight Imaging Beyond the Third Bounce](https://mlsanity.com/paper/2307.14341/) (2023)
- [Self-Calibrating, Fully Differentiable NLOS Inverse Rendering](https://mlsanity.com/paper/2309.12047/) (2023)
- [Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2407.18574/) (2024)
- [Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction](https://mlsanity.com/paper/2203.08657/) (2022)
- [Non-Line-of-Sight Tracking and Mapping with an Active Corner Camera](https://mlsanity.com/paper/2208.01702/) (2022)
- [Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data](https://mlsanity.com/paper/2301.00406/) (2023)
- [Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking](https://mlsanity.com/paper/2303.11791/) (2023)
- [Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors](https://mlsanity.com/paper/2409.14011/) (2024)
- [Towards Non-Line-of-Sight Photography](https://mlsanity.com/paper/2109.07783/) (2021)
- [Synthetic Wavelength Imaging -- Utilizing Spectral Correlations for High-Precision Time-of-Flight Sensing](https://mlsanity.com/paper/2209.04941/) (2022)
- [Domain Reduction Strategy for Non Line of Sight Imaging](https://mlsanity.com/paper/2308.10269/) (2023)
- [Neural Inverse Rendering from Propagating Light](https://mlsanity.com/paper/2506.05347/) (2025)
- [MARMOT: Masked Autoencoder for Modeling Transient Imaging](https://mlsanity.com/paper/2506.08470/) (2025)
- [Noise-adapted Neural Operator for Robust Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2508.09655/) (2025)

## Abstract (arXiv)

Non-Line-of-Sight (NLOS) imaging allows to observe objects partially or fully occluded from direct view, by analyzing indirect diffuse reflections off a secondary, relay surface. Despite its many potential applications, existing methods lack practical usability due to several shared limitations, including the assumption of single scattering only, lack of occlusions, and Lambertian reflectance. We lift these limitations by transforming the NLOS problem into a virtual Line-Of-Sight (LOS) one. Since imaging information cannot be recovered from the irradiance arriving at the relay surface, we introduce the concept of the phasor field, a mathematical construct representing a fast variation in irradiance. We show that NLOS light transport can be modeled as the propagation of a phasor field wave, which can be solved accurately by the Rayleigh-Sommerfeld diffraction integral. We demonstrate for the first time NLOS reconstruction of complex scenes with strong multiply scattered and ambient light, arbitrary materials, large depth range, and occlusions. Our method handles these challenging cases without explicitly developing a light transport model. By leveraging existing fast algorithms, we outperform existing methods in terms of execution speed, computational complexity, and memory use. We believe that our approach will help unlock the potential of NLOS imaging, and the development of novel applications not restricted to lab conditions. For example, we demonstrate both refocusing and transient NLOS videos of real-world, complex scenes with large depth.