Our InLoc dataset [2] contains color RGB images taken by iphone7 from different locations, times from original WUSTL RGBD [1], and 6DoF reference poses which are manually verified and able to be used for evaluating visual localization methods. We also provide color RGB perspective images and depth maps which are generated from RGBD scans originally provided by [1] and used as the database for InLoc dataset in our paper.
This repository provides the tools for constructing the whole of InLoc datset in the proper format to be easily used by our InLoc demo and evaluation toolkit.
- 2020.8.6 Update: Now we prepared alternative Dropbox links to our dataset. Please try
extract_db_all_mirror.shorextract_query_mirror.shif the original shell scripts take much time for getting data.
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Download and extract all database files: Run
./extract_db_all.shin the terminal. -
Download and extract query images: Run
extract_query.shin the terminal.
database/scans
Each folder corresponds to the building floor. In each folder, there are 3D scan datas originally provided in [1] converted in matlab binary format.
cse_scan_000.ptx.mat : the 3D points converted in matlab file.
database/alignments
Each file, e.g. cse_trans_000.txt, contains transformation matrix that converts 3D points in original ptx file to global coordinates. Please see more details at [1].
database/cutouts
The subfolder, e.g. database/cutouts/CSE3/000, corresponds to the ptx file database/scans/CSE3/cse_scan_000.ptx.mat.
In each subfolder, there are 36 perspective images and
cse_cutout_000_0_-30.jpg: RGB perspective image
cse_cutout_000_0_-30.jpg.mat: RGB perspective image (RGBcut) and the scan points (XYZcut) seen from this perspective view.
query/iphone7
Query images taken by iphone7.
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Erik Wijmans, Yasutaka Furukawa. Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment. In: CVPR 2017. Project page
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Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, Akihiko Torii. InLoc: Indoor Visual Localization with Dense Matching and View Synthesis. In: CVPR 2018. (To appear) Project page
If you use this toolkit and our dataset, please confirm and agree the LICENSE statements bellow.
Copyright (c) 2017 Hajime Taira
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
If you use our data and Software, please cite our paper:
@inproceedings{taira2018inloc,
title={{InLoc}: Indoor Visual Localization with Dense Matching and View Synthesis},
author={Taira, Hajime and Okutomi, Masatoshi and Sattler, Torsten and Cimpoi, Mircea and Pollefeys, Marc and Sivic, Josef and Pajdla, Tomas and Torii, Akihiko},
booktitle={{CVPR}},
year={2018}
}
and the paper presenting original Wustl Indoor RGBD dataset:
@inproceedings{wijmans17rgbd,
author = {Erik Wijmans and
Yasutaka Furukawa},
title = {Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment},
booktitle = {Computer Vision and Pattern Recognition, {CVPR}},
year = {2017},
url = {http://cvpr17.wijmans.xyz/CVPR2017-0111.pdf}
}