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SC-wLS

This is the official repo for the paper SC-wLS: Towards Interpretable Feed-forward Camera Re-localization (ECCV 2022), by Xin Wu*, Hao Zhao*, Shunkai Li, Yingdian Cao, and Hongbin Zha.

we propose a feed-forward camera re-localization method termed SC-wLS that exploits scene coordinate estimates for weighted least squares pose regression. This differentiable formulation exploits a weight network imposed on 2D-3D correspondences, and requires pose supervision only.

For more details, please refer to the paper.

Requirements

The code in this repo has been tested on Ubuntu 20.04, Python 3.8, and PyTorch 1.9.1 with a nvidia 3090. We recommand to create a virtual conda environment to install them. Other dependencies could be easily installed with pip or conda.

Moreover, the code includes a C++ extension (in the dsacstar) of DSAC*. You could compile and install it by executing:

cd dsacstar
python setup.py install

Note that you need to install opencv (3.4.15) in advance, and change the opencv path in the setup.py.

Datasets

Download the 7Scenes dataset and the Cambridge Landmarks dataset.

Inference

Download the pretrained models into the repo directory.

To evaluate on a scene, call:

python test_e2e_DLT.py <data_path> --dataset <dataset_name> --scene <scene_name> --config_file configs/single_frame.ini --model DenseSC_GNN --pretrained-model <model_path> <--useLM>

the --useLM option means whether to use LM-Refine in Section 3.5 of the paper.

In order to test the end-to-end training model, for example on the chess scene from the 7Scenes dataset:

python test_e2e_DLT.py <data_path> --dataset 7Scenes --scene chess -b1 --config_file configs/single_frame.ini --model DenseSC_GNN --pretrained-model ./7scenes_e2e_model/chess/model.pkl

In order to test the model after test-time adapatation, for example on the chess scene from the 7Scenes dataset:

python test_e2e_DLT.py <data_path> --dataset 7Scenes --scene chess -b1 --config_file configs/single_frame.ini --model DenseSC_GNN --pretrained-model ./7scenes_unsup_model/chess/model.pkl

To plot the per-frame errors of the scenes, please refer to the plot_per_frame file.

Training

The training of SC-wLS includes three stages: scene coordinate initialization, weight initialization and end-to-end optimization. These stages use pose supervision only. Meanwhile, you could also use self-supervised adaptation to enhance the test-time performance.

Scene coordinate initialization

To train the scene coordinate network on a scene, call:

python train_init.py <data_path> --dataset <dataset_name> --scene <scene_name> -b1 --epochs 1500 --config_file configs/single_frame.ini --lr 1e-4 <--cal_training_pose>  --model DenseSC

--cal_training_pose decides whether to calculate and record poses when training.

Weight initialization and end-to-end optimization

Use --e2e_training to specify whether to train the weight network only, or do end-to-end training. Weight initialization:

python train_e2e_DLT.py <data_path> --dataset <dataset_name> --scene <scene_name>  -b1 --epochs 1500 --config_file configs/single_frame.ini --glr 1e-4 --model DenseSC_GNN --loss_classif 0.5 --loss_essential 10.0 --obj_geod_th 1.0 --loss_essential_init_epo 0 -Ea 5 -Eb 1e-4 <--cal_training_pose> <--useRANSAC> <--log_val_loss> --pretrained-model <SCNet_model_path>

--useRANSAC decides whether to output the poses calculated from PnP+RANSAC as well. --log_val_loss means whether to record the losses when validation.

End-to-end training:

python train_e2e_DLT.py <data_path> --dataset <dataset_name> --scene <scene_name>  -b1 --epochs 1500 --config_file configs/single_frame.ini --lr 1e-5 --glr 1e-5 --model DenseSC_GNN --loss_classif 0.5 --loss_essential 10.0 --obj_geod_th 1.0 --loss_essential_init_epo 0 -Ea 5 -Eb 1e-4 --e2e_training <--cal_training_pose> <--useRANSAC> <--log_val_loss> --pretrained-model <SCNet_model_path> --w-model <WeightNet_model_path>

--loss_classif and --loss_essential are weights used to balance the classification loss and the regression loss. -Ea and -Eb are the $\alpha$ and $\beta$ in Eq. 11. As illustrated in the supplement, one should select proper hyper-parameters to balance the losses. Please refer to the detailed settings.

Test-time adaptation

To validate the self-supervised test-time adaptation on a scene, call:

python train_e2e_DLT_unsup.py <data_path> --dataset <dataset_name> --scene <scene_name> -b1 --epochs 1500 --config_file configs/<config_name> --glr 1e-5 --model DenseSC_GNN --pretrained-model <E2E_model_path> --auto_mask --ssim  --use_DLT2T --ph 400 <--cal_training_pose> <--useRANSAC>

--config_file is skip1step7.ini for 7Scenes, and skip1step2.ini for Cambridge.

You could also change --model DenseSC to train a model based on OANet w/o attention. Ablations could be seen in the supplementary.

Call the scripts with the -h option to see a listing of all supported command line arguments.

Acknowledgements

In this repository, we have used codes from the following repositories. We thank all the authors for sharing great codes.

Citation

If you find this repository useful, please cite the following paper and consider giving a star: