# Fast Back-Projection for Non-Line of Sight Reconstruction

arXiv: https://arxiv.org/abs/1703.02016 · HTML: https://mlsanity.com/paper/1703.02016/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 24 of 24)

- [Non-line-of-Sight Imaging via Neural Transient Fields](https://mlsanity.com/paper/2101.00373/) (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)
- [Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization](https://mlsanity.com/paper/2211.15367/) (2022)
- [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)
- [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)
- [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)
- [Domain Reduction Strategy for Non Line of Sight Imaging](https://mlsanity.com/paper/2308.10269/) (2023)
- [MARMOT: Masked Autoencoder for Modeling Transient Imaging](https://mlsanity.com/paper/2506.08470/) (2025)
- [Fast Non-line-of-sight Imaging with Two-step Deep Remapping](https://mlsanity.com/paper/2101.10492/) (2021)
- [Non-line-of-sight imaging in the presence of scattering media using phasor fields](https://mlsanity.com/paper/2311.09223/) (2023)
- [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)
- [A Renderer-Enabled Framework for Computing Parameter Estimation Lower Bounds in Plenoptic Imaging Systems](https://mlsanity.com/paper/2602.00215/) (2026)
- [Memory-efficient GPU pipelines for real-time non-line-of-sight reconstruction](https://mlsanity.com/paper/2608.28183/) (2026)
- [Cascaded Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2609.16017/) (2026)

## Abstract (arXiv)

Recent works have demonstrated non-line of sight (NLOS) reconstruction by using the time-resolved signal frommultiply scattered light. These works combine ultrafast imaging systems with computation, which back-projects the recorded space-time signal to build a probabilistic map of the hidden geometry. Unfortunately, this computation is slow, becoming a bottleneck as the imaging technology improves. In this work, we propose a new back-projection technique for NLOS reconstruction, which is up to a thousand times faster than previous work, with almost no quality loss. We base on the observation that the hidden geometry probability map can be built as the intersection of the three-bounce space-time manifolds defined by the light illuminating the hidden geometry and the visible point receiving the scattered light from such hidden geometry. This allows us to pose the reconstruction of the hidden geometry as the voxelization of these space-time manifolds, which has lower theoretic complexity and is easily implementable in the GPU. We demonstrate the efficiency and quality of our technique compared against previous methods in both captured and synthetic data