mihaidusmanu/cross-descriptor-vis-loc-map

Cross-Descriptor Visual Localization and Mapping

Cross-Descriptor Visual Localization and Mapping

This repository contains the implementation of the following paper:

"Cross-Descriptor Visual Localization and Mapping".
M. Dusmanu, O. Miksik, J.L. Schönberger, and M. Pollefeys. ICCV 2021.

[Paper on arXiv]

Requirements

COLMAP

We use COLMAP for DoG keypoint extraction as well as localization and mapping. Please follow the installation instructions available on the official webpage. Before proceeding, we recommend setting an environmental variable to the COLMAP executable folder by running export COLMAP_PATH=path_to_colmap_executable_folder.

Python

The environment can be set up directly using conda:

conda env create -f env.yml
conda activate cross-descriptor-vis-loc-map

Training data

We provide a script for downloading the raw training data:

bash scripts/download_training_data.sh

Evaluation data

We provide a script for downloading the LFE dataset along with the GT used for evaluation as well as the Aachen Day-Night dataset:

bash scripts/download_evaluation_data.sh

Training

Data preprocessing

First step is extracting keypoints and descriptors on the training data downloaded above.

bash scripts/process_training_data.sh

Alternatively, you can directly download the processed training data by running:

bash scripts/download_processed_training_data.sh

Training

To run training with the default architecture and hyper-parameters, execute the following:

python train.py \
    --dataset_path data/train/colmap \
    --features brief sift-kornia hardnet sosnet

Pretrained models

We provide two pretrained models trained on descriptors extracted from COLMAP SIFT and OpenCV SIFT keypoints, respectively. These models can be downloaded by running:

bash scripts/download_checkpoints.sh

Evaluation

Demo Notebook

Click for details...

Local Feature Evaluation Benchmark

Click for details...

First step is extracting descriptors on all datasets:

bash scripts/process_LFE_data.sh

We provide examples below for running reconstruction on Madrid Metrpolis in each different evaluation scenario.

Reconstruction using a single descriptor (standard)

python local-feature-evaluation/reconstruction_pipeline_progressive.py \
    --dataset_path data/eval/LFE-release/Madrid_Metropolis \
    --colmap_path $COLMAP_PATH \
    --features sift-kornia \
    --exp_name sift-kornia-single

Reconstruction using the progressive approach (ours)

python local-feature-evaluation/reconstruction_pipeline_progressive.py \
    --dataset_path data/eval/LFE-release/Madrid_Metropolis \
    --colmap_path $COLMAP_PATH \
    --features brief sift-kornia hardnet sosnet \
    --exp_name progressive

Reconstruction using the joint embedding approach (ours)

python local-feature-evaluation/reconstruction_pipeline_embed.py \
    --dataset_path data/eval/LFE-release/Madrid_Metropolis \
    --colmap_path $COLMAP_PATH \
    --features brief sift-kornia hardnet sosnet \
    --exp_name embed

Reconstruction using a single descriptor on the associated split (real-world)

python local-feature-evaluation/reconstruction_pipeline_subset.py \
    --dataset_path data/eval/LFE-release/Madrid_Metropolis/ \
    --colmap_path $COLMAP_PATH \
    --features brief sift-kornia hardnet sosnet \
    --feature sift-kornia \
    --exp_name sift-kornia-subset

Evaluation of a reconstruction w.r.t. metric pseudo-ground-truth

python local-feature-evaluation/align_and_compare.py \
    --colmap_path $COLMAP_PATH \
    --reference_model_path data/eval/LFE-release/Madrid_Metropolis/sparse-reference/filtered-metric/ \
    --model_path data/eval/LFE-release/Madrid_Metropolis/sparse-sift-kornia-single/0/

Aachen Day-Night

Click for details...

BibTeX

If you use this code in your project, please cite the following paper:

@InProceedings{Dusmanu2021Cross,
    author = {Dusmanu, Mihai and Miksik, Ondrej and Sch\"onberger, Johannes L. and Pollefeys, Marc},
    title = {{Cross Descriptor Visual Localization and Mapping}},
    booktitle = {Proceedings of the International Conference on Computer Vision},
    year = {2021}
}