DeViLoc: Learning to Produce Semi-dense Correspondences for Visual Localization

Our paper is accepted for an oral presentation (top 3.3%) at CVPR 2024. PDF is available at arxiv

Requirements

All experiments were implemented under Ubuntu 16.04 and NVIDIA TESLA V100/NVIDIA GeForce RTX 3090 with cuda version of 11.3/11.6. Note that the results might be different when using different GPU architecture.

To setup working environment, you need to create a virtual Python environment using Conda and then install required packages using pip

conda create -n dvl_env python=3.8 -c anaconda
conda activate dvl_env
conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Next, clone the feature matching model TopicFM and put it in the third_party folder

mkdir third_party/feat_matcher && cd third_party/feat_matcher
git clone https://github.com/TruongKhang/TopicFM.git
cd TopicFM && git checkout dev_2

Training

We train our network on the MegaDepth dataset. Note that you just need to download the file MegaDepth v1 Dataset (tar.gz, 199 GB) from the official website.

We recommend to store your training data in data/megadepth folder. The structure of this folder looks like this:

data
├── megadepth
    ├── phoenix # this folder is uncompressed from the file .tar.gz
    ├── preprocessed
        ├── scene_points3d
        └── megadepth_2d3d_q500ov0.2-1.0covis3-15.npy

The datas in the preprocessed folder is uploaded here

After downloading all data, you can change some training parameters in scripts/train_megadepth.sh and then run this script to train models

bash scripts/train_megadepth.sh configs/megadepth.yml

Evaluation

7scenes

Download dataset and save it into the folder data. This script are provided by HLoc.

bash scripts/download_7scenes.sh

Run the evaluation code as follows:

python evaluate.py configs/se7scenes.yml --ckpt_path pretrained/deviloc_weights.ckpt

Cambridge Landmarks

bash scripts/download_cambridge.sh
python evaluate.py configs/cambridge.yml --ckpt_path pretrained/deviloc_weights.ckpt

Long-term Visual Localization Benchmarks

The estimated camera poses of these datasets are evaluated on this benchmark website.

Downloading files contains pairs of query-reference images For the Aachen, Robotcar, and CMU datasets, it is required to select K reference images per a query image for localization. First, you need to download each dataset using the provided script in scripts/download_aachen/robotcar/cmu.sh

Next, please download our preprocessed pair files here and put each of them into the dataset folder like this:

data
├── aachen
    ├── pairs
        ├── pairs-query-netvlad50.txt

├── RobotCarSeasons
    ├── pairs-query-cosplace20.txt
├── Extended-CMU-Seasons
    ├── slice2
        ├── pairs-query-cosplace10.txt
    .
    .
    .
    ├── slice21
        ├── pairs-query-cosplace10.txt

Aachen Day-Night

bash scripts/download_aachen.sh
python evaluate.py configs/aachen.yml --ckpt_path pretrained/deviloc_weights.ckpt --out_file aachen_eval_deviloc.txt --covis_clustering

Robotcar-Seasons

bash scripts/download_robotcar.sh
python evaluate.py configs/robotcar.yml --ckpt_path pretrained/deviloc_weights.ckpt --out_file robotcar_eval_deviloc.txt

Extended CMU-Seasons

bash scripts/download_cmu.sh
python evaluate.py configs/cmu.yml --ckpt_path pretrained/deviloc_weights.ckpt --out_file cmu_eval_deviloc.txt

Citation

@inproceedings{giang2024learning,
  title={Learning to Produce Semi-dense Correspondences for Visual Localization},
  author={Giang, Khang Truong and Song, Soohwan and Jo, Sungho},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  year={2024}
}

Copyright

This work is affiliated with NMAIL-KAIST, and its intellectual property belongs to NMAIL-KAIST.

Copyright NMAIL-KAIST. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.