# Training Semantic Descriptors for Image-Based Localization

arXiv: https://arxiv.org/abs/2202.01212 · HTML: https://mlsanity.com/paper/2202.01212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 2 Feb 2022.

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

- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017)
- [Semantic Visual Localization](https://mlsanity.com/paper/1712.05773/) (2017)
- [Long-term Visual Localization using Semantically Segmented Images](https://mlsanity.com/paper/1801.05269/) (2018)

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

- [Semantic Pose Verification for Outdoor Visual Localization with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2203.16945/) (2022)

## Abstract (arXiv)

Vision based solutions for the localization of vehicles have become popular recently. We employ an image retrieval based visual localization approach. The database images are kept with GPS coordinates and the location of the retrieved database image serves as an approximate position of the query image. We show that localization can be performed via descriptors solely extracted from semantically segmented images. It is reliable especially when the environment is subjected to severe illumination and seasonal changes. Our experiments reveal that the localization performance of a semantic descriptor can increase up to the level of state-of-the-art RGB image based methods.