# High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits

arXiv: https://arxiv.org/abs/1704.02205 · HTML: https://mlsanity.com/paper/1704.02205/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2017.

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

- [Real-time Human-Centric Segmentation for Complex Video Scenes](https://mlsanity.com/paper/2108.07199/) (2021)
- [Learning 3D Texture-Aware Representations for Parsing Diverse Human Clothing and Body Parts](https://mlsanity.com/paper/2508.06032/) (2025)

## Abstract (arXiv)

Estimating correspondence between two images and extracting the foreground object are two challenges in computer vision. With dual-lens smart phones, such as iPhone 7Plus and Huawei P9, coming into the market, two images of slightly different views provide us new information to unify the two topics. We propose a joint method to tackle them simultaneously via a joint fully connected conditional random field (CRF) framework. The regional correspondence is used to handle textureless regions in matching and make our CRF system computationally efficient. Our method is evaluated over 2,000 new image pairs, and produces promising results on challenging portrait images.