# RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Features for Indoor Localization

arXiv: https://arxiv.org/abs/2012.00234 · HTML: https://mlsanity.com/paper/2012.00234/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Learning Reforms Image Matching: A Survey and Outlook](https://mlsanity.com/paper/2506.04619/) (2025)
- [Discriminative and Semantic Feature Selection for Place Recognition towards Dynamic Environments](https://mlsanity.com/paper/2103.10023/) (2021)
- [PreSIST: Vision-Language-Informed Object Persistence Prediction in Open-World Scenes](https://mlsanity.com/paper/2607.04057/) (2026)

## Abstract (arXiv)

Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then extract features by considering both of them. We also introduce a new dataset, named OpenLORIS-Location, to train the proposed network. The dataset contains 1553 images from 93 indoor locations. Various appearance changes between images of the same location are included and can help the model to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization. The RaP-Net code and dataset are available at https://github.com/ivipsourcecode/RaP-Net.