# Non-line-of-sight imaging with arbitrary illumination and detection pattern

arXiv: https://arxiv.org/abs/2211.00648 · HTML: https://mlsanity.com/paper/2211.00648/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2022.

## Built on by (most cited first, 10 of 10)

- [Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization](https://mlsanity.com/paper/2211.15367/) (2022)
- [MARMOT: Masked Autoencoder for Modeling Transient Imaging](https://mlsanity.com/paper/2506.08470/) (2025)
- [Passive Non-Line-of-Sight Imaging with Light Transport Modulation](https://mlsanity.com/paper/2312.16014/) (2023)
- [Iterating the Transient Light Transport Matrix for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2412.10300/) (2024)
- [Dual-branch Graph Feature Learning for NLOS Imaging](https://mlsanity.com/paper/2502.19683/) (2025)
- [TransiT: Transient Transformer for Non-line-of-sight Videography](https://mlsanity.com/paper/2503.11328/) (2025)
- [Geometric Constrained Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2503.17992/) (2025)
- [Fast and Memory-efficient Non-line-of-sight Imaging with Quasi-Fresnel Transform](https://mlsanity.com/paper/2508.02003/) (2025)
- [Semantic Temporal Single-photon LiDAR](https://mlsanity.com/paper/2512.06008/) (2025)
- [Non-line-of-sight imaging with arbitrary relay surface geometries via 3D Gaussian Transient Rendering](https://mlsanity.com/paper/2606.21270/) (2026)

## Abstract (arXiv)

Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight. Existing NLOS imaging algorithms require dense measurements at rectangular grid points in a large area of the relay surface, which severely hinders their availability to variable relay scenarios in practical applications such as robotic vision, autonomous driving, rescue operations and remote sensing. In this work, we propose a Bayesian framework for NLOS imaging with no specific requirements on the spatial pattern of illumination and detection points. By introducing virtual confocal signals, we design a confocal complemented signal-object collaborative regularization (CC-SOCR) algorithm for high quality reconstructions. Our approach is capable of reconstructing both albedo and surface normal of the hidden objects with fine details under the most general relay setting. Moreover, with a regular relay surface, coarse rather than dense measurements are enough for our approach such that the acquisition time can be reduced significantly. As demonstrated in multiple experiments, the new framework substantially enhances the applicability of NLOS imaging.