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Pose Correction for Highly Accurate Visual Localization in Large-scale Indoor Spaces (ICCV 2021)
Janghun Hyeon<sup>1* </sup>, JooHyung Kim<sup>1* </sup>, Nakju Doh<sup>1, 2 </sup>
<sup>1</sup> Korea University, <sup>2</sup> TeeLabs
* Equally contributed to this work.
<img src="images/paper.png" width="18%"/> <img src="images/poster.png" width=37.0%> <img src="images/slides.png" width=39%>
Abstract
Indoor visual localization is significant for various applications such as autonomous robots, augmented reality, and mixed reality. Recent advances in visual localization have demonstrated their feasibility in large-scale indoor spaces through coarse-to-fine methods that typically employ three steps: image retrieval, pose estimation, and pose selection. However, further research is needed to improve the accuracy of large-scale indoor visual localization. We demonstrate that the limitations in the previous methods can be attributed to the sparsity of image positions in the database, which causes view-differences between a query and a retrieved image from the database. In this paper, to address this problem, we propose a novel module, named pose correction, that enables re-estimation of the pose with local feature matching in a similar view by reorganizing the local features. This module enhances the accuracy of the initially estimated pose and assigns more reliable ranks. Furthermore, the proposed method achieves a new state-of-the-art performance with an accuracy of more than 90 %within 1.0 m in the challenging indoor benchmark dataset InLoc for the first time.
<p align="center"> <img src="images/methods.png" width="60%"/> </p>Dependencies
-
Python 3
-
Pytorch >= 1.1
-
Tensorflow >= 1.13
-
openCV >= 3.4
-
Matplotlib >= 3.1
-
Numpy >= 1.18
-
scipy >= 1.4.1
-
open3d >= 0.7.0.0
-
vlfeat >= 0.9.20
-
vlfeat-ctypes >= 0.1.5
Prerequisite: Model Parameters
PCLoc is based on coarse-to-fine localization, which uses NetVLAD, SuperPoint, and SuperGlue. Thus, the model parameter should be downloaded from the original code.
NetVLAD: Download Model
Download parameter from the above URL, and unzip the file at:
./thirdparty/netvlad_tf/checkpoints/vd16_pitts30k_conv5_3_vlad_preL2_intra_white.data-00000-of-00001
./thirdparty/netvlad_tf/checkpoints/vd16_pitts30k_conv5_3_vlad_preL2_intra_white.index
./thirdparty/netvlad_tf/checkpoints/vd16_pitts30k_conv5_3_vlad_preL2_intra_white.meta
SuperPoint and SuperGlue: Download Model
./thirdparty/SuperGluePretrainedNetwork/models/weights/superglue_outdoor.pth
./thirdparty/SuperGluePretrainedNetwork/models/weights/superpoint_v1.pth
Prerequisite: Dataset
Dataset
To test our model using the InLoc dataset, the dataset should be downloaded. Downloading takes a while (Dataset is about 1.0TB). Click here to download dataset.
Quick Start
- Clone repository
git clone --recurse-submodules https://github.com/JanghunHyeon/PCLoc.git
- Download InLoc dataset
- Install dependencies
- Download model parameters and unzip the files at each of above path
- Modify
database_setup.py
- line 21 (--db_dir) : path to inloc dataset
- line 22 (--save_dir) : path to save directory of database features
- Execute
database_setup.py
in python, which prepares database features. - Modify
main_inference.py
- line 39 (--query_dir) : path to query directory.
- line 40 (--db_dir) : path to database features, which was generated by running
database_setup.py
.
- Execute
main_inference.py
in python, and results are saved at--log_dir
.
Database Description
netvlad_feats.npy
: Global descriptors (NetVLAD) of the database images.local_feats
: Local features and the corresponding 3D coordinates to the keypoints of each database image.pc_feats
: Local feature map used for the pose correction.scans_npy.npy
: RGB-D scan data from the dataset (InLoc), which is used for pose verification.
Contents
The provided sample code (06_main_inference.py
) runs pose correction.
This code provides three options:
--opt_div_matching
: Usage of Divided Matching
Example: --opt_div_matching
- False: Table 5 (b-1) from the paper
- True: Table 5 (b-2) from the paper
Results
After running the code, results are shown in the --log_dir
.
Example: ./log/202103241833/IMG_0738/mpv
00_query_img.jpg
: image used for the query.01_final_pose.jpg
: rendered image at the final pose.02_final_err_30.837045.jpg
: error image between the query and the rendered image.pred_IMG_0738.txt
: estimated final pose.all/*
: top-k candidates from the pose correction.
Error [m, 10<sup>o</sup>] | DUC1 | DUC2 |
---|---|---|
InLoc | 40.9/ 58.1/ 70.2 | 35.9/ 54.2/ 69.5 |
HFNet | 39.9/ 55.6/ 67.2 | 37.4/ 57.3/ 70.2 |
KAPTURE | 41.4/ 60.1/ 73.7 | 47.3/ 67.2/ 73.3 |
D2Net | 43.9/ 61.6/ 73.7 | 42.0/ 60.3/ 74.8 |
Oracle | 43.9/ 66.2/ 78.3 | 43.5/ 63.4/ 76.3 |
Sparse NCNet | 47.0/ 67.2/ 79.8 | 43.5/ 64.9/ 80.2 |
RLOCS | 47.0/ 71.2/ 84.8 | 58.8/ 77.9/ 80.9 |
SuperGlue | 46.5/ 65.7/ 77.8 | 51.9/ 72.5/ 79.4 |
Baseline (3,000) | 53.0/ 76.8/ 85.9 | 61.8/ 80.9/ 87.0 |
Ours (3,000) | 59.6/ 78.3/ 89.4 | 71.0/ 93.1/ 93.9 |
Ours (4,096) | 60.6/ 79.8/ 90.4 | 70.2/ 92.4/ 93.1 |
Every evaluation was conudcted with the online viusal localization benchmark server. visuallocalization.net/benchmark
BibTeX Citation
If you use any ideas from the paper or code from this repo, please consider citing:
@inproceedings{hyeon2021pose,
title={Pose Correction for Highly Accurate Visual Localization in Large-Scale Indoor Spaces},
author={Hyeon, Janghun and Kim, Joohyung and Doh, Nakju},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={15974--15983},
year={2021}
}