# Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data

arXiv: https://arxiv.org/abs/2301.00406 · HTML: https://mlsanity.com/paper/2301.00406/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 1 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [Non-line-of-sight Imaging](https://mlsanity.com/paper/2005.08026/) (2020)
- [Non-line-of-Sight Imaging via Neural Transient Fields](https://mlsanity.com/paper/2101.00373/) (2021)
- [Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar](https://mlsanity.com/paper/1912.06613/) (2019)
- [Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation](https://mlsanity.com/paper/2003.14414/) (2020)
- [Fast Back-Projection for Non-Line of Sight Reconstruction](https://mlsanity.com/paper/1703.02016/) (2017)
- [Deep Non-Line-of-Sight Reconstruction](https://mlsanity.com/paper/2001.09067/) (2020)
- [Real-time Non-line-of-Sight imaging of dynamic scenes](https://mlsanity.com/paper/2010.12737/) (2020)
- [Curvature Regularized Surface Reconstruction from Point Cloud](https://mlsanity.com/paper/2001.07884/) (2020)
- [Efficient Non-Line-of-Sight Imaging from Transient Sinograms](https://mlsanity.com/paper/2008.02787/) (2020)
- [Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes](https://mlsanity.com/paper/2104.13807/) (2021)
- [CURE: Curvature Regularization For Missing Data Recovery](https://mlsanity.com/paper/1901.09548/) (2019)
- [Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking](https://mlsanity.com/paper/2003.03710/) (2020)
- [CNN-based Euler's Elastica Inpainting with Deep Energy and Deep Image Prior](https://mlsanity.com/paper/2207.07921/) (2022)

## Built on by (most cited first, 2 of 2)

- [Geometric Constrained Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2503.17992/) (2025)
- [A Globally Convergent Algorithm for Total Scaled-Gradient Variation via Cone-Constrained Bilinear Decomposition](https://mlsanity.com/paper/2609.00036/) (2026)

## Abstract (arXiv)

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly possibility to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.