Image keypoint extraction is an important step for visual localization. The localization in indoor environment is challenging for that there may be many unreliable features on dynamic or repetitive objects. Such kind of reliability cannot be well learned by existing Convolutional Neural Network (CNN) based feature extractors. We propose a novel network, RaP-Net, which explicitly addresses feature invariability with a region-wise predictor, and combines it with a point-wise predictor to select reliable keypoints in an image . We also build a new dataset,  OpenLORIS-Location, to train this network. The dataset contains 1553 indoor images with location labels. There are various scene changes between images on the same location , which can help a network to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset significantly outperforms existing CNN-based keypoint extraction algorithms for indoor localization. The code and data are available at URL
Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will disturb robust feature matching and thus, challenge indoor localization greatly. To conquer such an issue, 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 proposed network. The dataset contains 1553 indoor images from 93 indoor locations. Various appearance changes between images of the same location are included and they can help to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset achieves an 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 URL
R
Image keypoint extraction is an important step for
Feature extraction plays an important role in
R
The localization in indoor environment is challenging for that there may be many unreliable
Unreliable
R
or repetitive objects. Such kind of reliability cannot be well learned by existing Convolutional Neural Network (CNN) based feature extractors. We
objects or repetitive regions will disturb robust feature matching and thus, challenge indoor localization greatly. To conquer such an issue, we
R
which explicitly addresses feature invariability with a
to simultaneously predict
R
predictor, and combines it with a
invariability and
R
predictor to select reliable keypoints in an image
reliability, and then extract features by considering both of them
R
build
introduce
A
named
R
this
proposed
R
with location labels. There are various scene
from 93 indoor locations. Various appearance
R
on
of
R
, which can help a network
are included and they can help
R
significantly outperforms existing CNN-based keypoint extraction algorithms for
achieves an excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in
R
code and data
RaP-Net code and dataset