# Visual Localization Using Semantic Segmentation and Depth Prediction

arXiv: https://arxiv.org/abs/2005.11922 · HTML: https://mlsanity.com/paper/2005.11922/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

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

- [What makes visual place recognition easy or hard?](https://mlsanity.com/paper/2106.12671/) (2021)
- [RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments](https://mlsanity.com/paper/2207.12579/) (2022)
- [Visual Place Representation and Recognition from Depth Images](https://mlsanity.com/paper/2112.13707/) (2021)
- [Soft Expectation and Deep Maximization for Image Feature Detection](https://mlsanity.com/paper/2104.10291/) (2021)
- [Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge](https://mlsanity.com/paper/2408.13085/) (2024)

## Abstract (arXiv)

In this paper, we propose a monocular visual localization pipeline leveraging semantic and depth cues. We apply semantic consistency evaluation to rank the image retrieval results and a practical clustering technique to reject estimation outliers. In addition, we demonstrate a substantial performance boost achieved with a combination of multiple feature extractors. Furthermore, by using depth prediction with a deep neural network, we show that a significant amount of falsely matched keypoints are identified and eliminated. The proposed pipeline outperforms most of the existing approaches at the Long-Term Visual Localization benchmark 2020.