# Seeing Around Corners with Edge-Resolved Transient Imaging

arXiv: https://arxiv.org/abs/2002.07118 · HTML: https://mlsanity.com/paper/2002.07118/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction](https://mlsanity.com/paper/2307.09555/) (2023)
- [What You Can Learn by Staring at a Blank Wall](https://mlsanity.com/paper/2108.13027/) (2021)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar](https://mlsanity.com/paper/2408.12191/) (2024)
- [Role of Transients in Two-Bounce Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2304.01308/) (2023)
- [Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction](https://mlsanity.com/paper/2304.10780/) (2023)
- [Non-Line-of-Sight Tracking and Mapping with an Active Corner Camera](https://mlsanity.com/paper/2208.01702/) (2022)
- [Can Shadows Reveal Biometric Information?](https://mlsanity.com/paper/2209.10077/) (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)
- [Onsite Non-Line-of-Sight Imaging via Online Calibrations](https://mlsanity.com/paper/2112.14555/) (2021)
- [Soft Shadow Diffusion (SSD): Physics-inspired Learning for 3D Computational Periscopy](https://mlsanity.com/paper/2601.12257/) (2026)

## Abstract (arXiv)

Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in search and rescue, reconnaissance, and even medical imaging. The critical challenge of NLOS imaging is that diffuse reflections scatter light in all directions, resulting in weak signals and a loss of directional information. To address this problem, we propose a method for seeing around corners that derives angular resolution from vertical edges and longitudinal resolution from the temporal response to a pulsed light source. We introduce an acquisition strategy, scene response model, and reconstruction algorithm that enable the formation of 2.5-dimensional representations -- a plan view plus heights -- and a 180circ field of view (FOV) for large-scale scenes. Our experiments demonstrate accurate reconstructions of hidden rooms up to 3 meters in each dimension.