# Survey on Semantic Stereo Matching / Semantic Depth Estimation

arXiv: https://arxiv.org/abs/2109.10123 · HTML: https://mlsanity.com/paper/2109.10123/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [SegStereo: Exploiting Semantic Information for Disparity Estimation](https://mlsanity.com/paper/1807.11699/) (2018)
- [Geometry meets semantics for semi-supervised monocular depth estimation](https://mlsanity.com/paper/1810.04093/) (2018)
- [Real-Time Semantic Stereo Matching](https://mlsanity.com/paper/1910.00541/) (2019)
- [DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery](https://mlsanity.com/paper/1809.04734/) (2018)

## Built on by (most cited first, 1 of 1)

- [USAM-Net: A U-Net-based Network for Improved Stereo Correspondence and Scene Depth Estimation using Features from a Pre-trained Image Segmentation network](https://mlsanity.com/paper/2503.14950/) (2025)

## Abstract (arXiv)

Stereo matching is one of the widely used techniques for inferring depth from stereo images owing to its robustness and speed. It has become one of the major topics of research since it finds its applications in autonomous driving, robotic navigation, 3D reconstruction, and many other fields. Finding pixel correspondences in non-textured, occluded and reflective areas is the major challenge in stereo matching. Recent developments have shown that semantic cues from image segmentation can be used to improve the results of stereo matching. Many deep neural network architectures have been proposed to leverage the advantages of semantic segmentation in stereo matching. This paper aims to give a comparison among the state of art networks both in terms of accuracy and in terms of speed which are of higher importance in real-time applications.