# Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging

arXiv: https://arxiv.org/abs/2407.18574 · HTML: https://mlsanity.com/paper/2407.18574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [Non-line-of-Sight Imaging via Neural Transient Fields](https://mlsanity.com/paper/2101.00373/) (2021)
- [Spectral Bias in Practice: The Role of Function Frequency in Generalization](https://mlsanity.com/paper/2110.02424/) (2021)
- [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)
- [Attention in Attention Network for Image Super-Resolution](https://mlsanity.com/paper/2104.09497/) (2021)
- [Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging](https://mlsanity.com/paper/2205.01679/) (2022)
- [A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging](https://mlsanity.com/paper/1703.02635/) (2017)
- [Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder](https://mlsanity.com/paper/2006.09241/) (2020)
- [Efficient Non-Line-of-Sight Imaging from Transient Sinograms](https://mlsanity.com/paper/2008.02787/) (2020)
- [Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization](https://mlsanity.com/paper/2211.15367/) (2022)

## Built on by (most cited first, 3 of 3)

- [Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors](https://mlsanity.com/paper/2409.14011/) (2024)
- [Shoot-Bounce-3D: Single-Shot Occlusion-Aware 3D from Lidar by Decomposing Two-Bounce Light](https://mlsanity.com/paper/2512.06080/) (2025)
- [Iterating the Transient Light Transport Matrix for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2412.10300/) (2024)

## Abstract (arXiv)

This paper aims to facilitate more practical NLOS imaging by reducing the number of samplings and scan areas. To this end, we introduce a phasor-based enhancement network that is capable of predicting clean and full measurements from noisy partial observations. We leverage a denoising autoencoder scheme to acquire rich and noise-robust representations in the measurement space. Through this pipeline, our enhancement network is trained to accurately reconstruct complete measurements from their corrupted and partial counterparts. However, we observe that the naive application of denoising often yields degraded and over-smoothed results, caused by unnecessary and spurious frequency signals present in measurements. To address this issue, we introduce a phasor-based pipeline designed to limit the spectrum of our network to the frequency range of interests, where the majority of informative signals are detected. The phasor wavefronts at the aperture, which are band-limited signals, are employed as inputs and outputs of the network, guiding our network to learn from the frequency range of interests and discard unnecessary information. The experimental results in more practical acquisition scenarios demonstrate that we can look around the corners with 16× or 64× fewer samplings and 4× smaller apertures. Our code is available at https://github.com/join16/LEAP.