This code implements TopicFM+, which is an extension of TopicFM. For the implementation of previous version TopicFM, please checkout the aaai23_ver branch.
All experiments in this paper are implemented on the Ubuntu environment with a NVIDIA driver of at least 430.64 and CUDA 10.1.
First, create a virtual environment by anaconda as follows,
conda create -n topicfm python=3.8
conda activate topicfm
conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt
# using pip to install any missing packages
The proposed method is trained on the MegaDepth dataset and evaluated on the MegaDepth test, ScanNet, HPatches, Aachen Day and Night (v1.1), and InLoc dataset. All these datasets are large, so we cannot include them in this code. The following descriptions help download these datasets.
This dataset is used for both training and evaluation (Li and Snavely 2018). To use this dataset with our code, please follow the instruction of LoFTR.
We only use 1500 image pairs of ScanNet (Dai et al. 2017) for evaluation. Please download and prepare test data of ScanNet provided by LoFTR.
To train our model, we recommend using GPU cards as much as possible, and each GPU should be at least 12GB.
In our settings, we train on 4 GPUs, each of which is 12GB.
Please setup your hardware environment in scripts/reproduce_train/outdoor.sh.
Then run this command to start training.
bash scripts/reproduce_train/outdoor.sh <path to the training config file>
# for example,
bash scripts/reproduce_train/outdoor.sh configs/megadepth_train_topicfmfast.py
We provided the pretrained models, which were used in the paper (TopicFM-fast, TopicFM+)
bash scripts/reproduce_test/outdoor.sh <path to the config file in the folder configs> <path to pretrained model>
# For example, to evaluate TopicFM-fast
bash scripts/reproduce_test/outdoor.sh configs/megadepth_test_topicfmfast.py pretrained/topicfm_fast.ckpt
bash scripts/reproduce_test/indoor.sh <path to the config file in the folder configs> <path to pretrained model>
To evaluate on these datasets, we integrate our code to the image-matching-toolbox provided by Patch2Pix. The updated code and detailed evaluations are available here.
Our method TopicFM+ achieved a high ranking (silver medal) on the Kaggle IMC2023 here.
The efficiency evaluation reported in the paper was measured by averaging runtime of 1500 image pairs of the ScanNet evaluation dataset.
The image size can be changed in configs/data/scannet_test_topicfmfast.py
We computed computational costs in GFLOPs and runtimes in ms for LoFTR, MatchFormer, QuadTree, and AspanFormer. However, this process required minor modification of the code of each method individually. Please contact us if you need evaluations for those methods.
Here, we provide the runtime measurement for our method, TopicFM-fast
python visualization.py --method topicfmv2 --dataset_name scannet --config_file configs/scannet_test_topicfmfast.py --measure_time --no_viz
Runtime report at the image resolution of (640, 480) (measured on NVIDIA TITAN V 32GB of Mem.)
| Model | 640 x 480 | 1200 x 896 |
|---|---|---|
| TopicFM-fast | 56 ms | 346 ms |
| TopicFM+ | 90 ms | 388 ms |
If you find this code useful, please cite the following works:
@misc{giang2023topicfm,
title={TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching},
author={Khang Truong Giang and Soohwan Song and Sungho Jo},
year={2023},
eprint={2307.00485},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
or
@inproceedings{giang2023topicfm,
title={TopicFM: Robust and interpretable topic-assisted feature matching},
author={Giang, Khang Truong and Song, Soohwan and Jo, Sungho},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={2},
pages={2447--2455},
year={2023}
}
This code is built based on LoFTR. We thank the authors for their useful source code.