# Stereo image de-fencing using smartphones

arXiv: https://arxiv.org/abs/1612.01323 · HTML: https://mlsanity.com/paper/1612.01323/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2016.

## Built on by (most cited first, 5 of 5)

- [Wild2Avatar: Rendering Humans Behind Occlusions](https://mlsanity.com/paper/2401.00431/) (2023)
- [Efficient Flow-Guided Multi-frame De-fencing](https://mlsanity.com/paper/2301.10759/) (2023)
- [Security Fence Inspection at Airports Using Object Detection](https://mlsanity.com/paper/2311.12064/) (2023)
- [DeclutterNeRF: Generative-Free 3D Scene Recovery for Occlusion Removal](https://mlsanity.com/paper/2504.04679/) (2025)
- [Freq-DP Net: A Dual-Branch Network for Fence Removal using Dual-Pixel and Fourier Priors](https://mlsanity.com/paper/2602.14226/) (2026)

## Abstract (arXiv)

Conventional approaches to image de-fencing have limited themselves to using only image data in adjacent frames of the captured video of an approximately static scene. In this work, we present a method to harness disparity using a stereo pair of fenced images in order to detect fence pixels. Tourists and amateur photographers commonly carry smartphones/phablets which can be used to capture a short video sequence of the fenced scene. We model the formation of the occluded frames in the captured video. Furthermore, we propose an optimization framework to estimate the de-fenced image using the total variation prior to regularize the ill-posed problem.