Image query

Visual Camera Re-Localization using Graph Neural Networks and Relative Pose Supervision

Mehmet Özgür Türkoǧlu, Eric Brachmann, Konrad Schindler, Gabriel J. Brostow, Áron Monszpart - 3DV 2021.

[Paper on ArXiv] [Paper on IEEE Explore] [Presentation (long)] [Presentation (short)] [Poster]

🌌 Overview

Relative pose regression. We combine the efficiency of image retrieval methods and the ability of graph neural networks to selectively and iteratively refine estimates to solve the challenging relative pose regression problem. Given a query image, we first find similar images to it using a differentiable image retrieval method NetVLAD. We preserve the diversity of neighbors by strided subsampling before building a fully connected Graph Neural Network (GNN). Node representations x_i are initialized from ResNet34, and are combined using MLP-s into edge features e_ij. Finally, the relative pose regression layer maps the refined edge representations into relative poses between image pairs. Edge dropout is only applied at training time.

📈 Results

	Trained on 7 training sets
Chess	pred. poses: relpose_gnn__multi_39_chess_0.09_2.9.npz
Fire	pred. poses: relpose_gnn__multi_39_fire_0.23_7.4.npz
Heads	pred. poses: relpose_gnn__multi_39_heads_0.13_8.5.npz
Office	pred. poses: relpose_gnn__multi_39_office_0.15_4.1.npz
Pumpkin	pred. poses: relpose_gnn__multi_39_pumpkin_0.17_3.3.npz
Kitchen	pred. poses: relpose_gnn__multi_39_redkitchen_0.20_3.6.npz
Stairs	pred. poses: relpose_gnn__multi_39_stairs_0.23_6.4.npz

✏️ 📄 Citation

If you find our work useful or interesting, please cite our paper:

@inproceedings{turkoglu2021visual,
  title={{Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision}},
  author={T{\"{u}}rko\u{g}lu, Mehmet {\"{O}}zg{\"{u}}r and 
          Brachmann, Eric and 
          Schindler, Konrad and 
          Brostow, Gabriel and 
          Monszpart, \'{A}ron},
  booktitle={International Conference on 3D Vision ({3DV})},
  year={2021},
  organization={IEEE}
}

Reproducing results: 7-Scenes

Source code

export RELPOSEGNN="${HOME}/relpose_gnn" 
git clone --recurse-submodules --depth 1 https://github.com/nianticlabs/relpose-gnn.git ${RELPOSEGNN}

Setup

We use a Conda environment that makes it easy to install all dependencies. Our code has been tested on Ubuntu 20.04 with PyTorch 1.8.2 and CUDA 11.1.

Install miniconda with Python 3.8.

Create the conda environment:

conda env create -f environment-cu111.yml

Activate and verify the environment:

conda activate relpose_gnn
python -c 'import torch; \
           print(f"torch.version: {torch.__version__}"); \
           print(f"torch.cuda.is_available(): {torch.cuda.is_available()}"); \
           import torch_scatter; \
           print(f"torch_scatter: {torch_scatter.__version__}")'

Set more paths

export SEVENSCENES="/mnt/disks/data-7scenes/7scenes"
export DATADIR="/mnt/disks/data"
export SEVENSCENESRW="${DATADIR}/7scenes-rw"
export PYTHONPATH="${RELPOSEGNN}:${RELPOSEGNN}/python:${PYTHONPATH}"

I. Prepare the 7-Scenes dataset

Download

mkdir -p "${SEVENSCENES}" || (mkdir -p "${SEVENSCENES}" && chmod go+w -R "${SEVENSCENES}")
for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
  test -f "${SEVENSCENES}/${SCENE}.zip" || \
    (wget -c "http://download.microsoft.com/download/2/8/5/28564B23-0828-408F-8631-23B1EFF1DAC8/${SCENE}.zip" -O "$SEVENSCENES/$SCENE.zip" &)
done

Extract

find "${SEVENSCENES}" -maxdepth 1 -name "*.zip" | xargs -P 7 -I fileName sh -c 'unzip -o -d "$(dirname "fileName")" "fileName"'
find "${SEVENSCENES}" -mindepth 2 -name "*.zip" | xargs -P 7 -I fileName sh -c 'unzip -o -d "$(dirname "fileName")" "fileName"'

II. Image retrieval

For graph construction we incorporate the NetVLAD image retrieval CNN model. It is based on this repository: https://github.com/sfu-gruvi-3dv/sanet_relocal_demo. You'll need preprocessed .bin files (train_frames.bin, test_frames.bin) for each scene.

Pre-processed

Coming soon...

Generate yourself

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
  python python/external/sanet_relocal_demo/seq_data/seven_scenes/scenes2seq.py \
    "${SEVENSCENES}/${SCENE}" \
    --dst-dir "${SEVENSCENESRW}/${SCENE}"
done

III. Graph generation

Before starting to train the model, train and test graphs should be generated to speed up the dataloaders, and not have to run NN search during training.

III.A. Pre-processed

Download

Test

mkdir -p "${SEVENSCENESRW}" || (mkdir -p "${SEVENSCENESRW}" && chmod go+w -R "${SEVENSCENESRW}")
for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
  wget -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/data/${SCENE}_fc8_sp5_test.tar" \
       -O "${SEVENSCENESRW}/${SCENE}_fc8_sp5_test.tar"
done

Train

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
  wget -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/data/${SCENE}_fc8_sp5_train.tar" \
       -O "${SEVENSCENESRW}/${SCENE}_fc8_sp5_train.tar"
done

Extract

(cd "${SEVENSCENESRW}"; \
 find "${SEVENSCENESRW}" -mindepth 1 -maxdepth 1 -name "*.tar" | xargs -P 7 -I fileName sh -c 'tar -I pigz -xvf "fileName"')

III.B. Generate yourself

For testing a model

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
   python python/niantic/datasets/dataset_7Scenes_multi.py \
     "${SCENE}" \
     "test" \
     --data-path "${SEVENSCENES}" \
     --graph-data-path "${SEVENSCENESRW}" \
     --seq-len 8 \
     --sampling-period 5 \
     --gpu 0
done

For training a multi-scene model (Table 1. in paper)

python python/niantic/datasets/dataset_7Scenes_multi.py \
  multi \
  "train" \
  --data-path "${SEVENSCENES}" \
  --graph-data-path "${SEVENSCENESRW}" \
  --seq-len 8 \
  --sampling-period 5 \
  --gpu 0

For training a single-scene model (Table 1. in supplementary)

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
   python python/niantic/datasets/dataset_7Scenes_multi.py \
     "${SCENE}" \
     train \
     --data-path "${SEVENSCENES}" \
     --graph-data-path "${SEVENSCENESRW}" \
     --seq-len 8 \
     --sampling-period 5 \
     --gpu 0
done

Evaluation

Pre-trained

Download pre-trained model trained with entire 7-Scenes training scenes (Table 1 in the paper)

wget \
 -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/models/relpose_gnn__multi_39.pth.tar" \
 -O "${DATADIR}/relpose_gnn__multi_39.pth.tar"

Evaluate on each 7scenes test scene

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
   python -u ${RELPOSEGNN}/python/niantic/testing/test.py \
     --dataset-dir "${SEVENSCENES}" \
     --test-data-dir "${SEVENSCENESRW}" \
     --weights "${DATADIR}/relpose_gnn__multi_39.pth.tar" \
     --save-dir "${DATADIR}" \
     --gpu 0 \
     --test-scene "${SCENE}"
done

Download pre-trained models trained with 7-Scenes' 6 training scenes (Table 2 in the paper)

wget \
 -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/models/6Scenes_${SCENE}_epoch_039.pth.tar" \
 -O "${DATADIR}/6Scenes_${SCENE}_epoch_039.pth.tar"

Evaluate each model on a corresponding remaining scene

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
   python -u ${RELPOSEGNN}/python/niantic/testing/test.py \
     --dataset-dir "${SEVENSCENES}" \
     --test-data-dir "${SEVENSCENESRW}" \
     --weights "${DATADIR}/6Scenes_${SCENE}_epoch_039.pth.tar" \
     --save-dir "${DATADIR}" \
     --gpu 0 \
     --test-scene "${SCENE}"
done

Download pre-trained models trained with 7-Scenes' single training scene (Table 1. in the supp.)

wget \
 -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/models/1Scenes_${SCENE}_epoch_039.pth.tar" \
 -O "${DATADIR}/1Scenes_${SCENE}_epoch_039.pth.tar"

Evaluate each model on the same scene

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
   python -u ${RELPOSEGNN}/python/niantic/testing/test.py \
     --dataset-dir "${SEVENSCENES}" \
     --test-data-dir "${SEVENSCENESRW}" \
     --weights "${DATADIR}/1Scenes_${SCENE}_epoch_039.pth.tar" \
     --save-dir "${DATADIR}" \
     --gpu 0 \
     --test-scene "${SCENE}"
done

Train yourself

7 scenes training (Table 1. in the paper)

python -u ${RELPOSEGNN}/python/niantic/training/train.py \
  --dataset-dir "${SEVENSCENES}" \
  --train-data-dir "${SEVENSCENESRW}" \
  --test-data-dir "${SEVENSCENESRW}" \
  --save-dir "${DATADIR}" \
  --gpu 0 \
  --experiment 0 \
  --test-scene multi

6 scenes training (Table 2. in the paper)

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
   python -u ${RELPOSEGNN}/python/niantic/training/train.py \
     --dataset-dir "${SEVENSCENES}" \
     --train-data-dir "${SEVENSCENESRW}" \
     --test-data-dir "${SEVENSCENESRW}" \
     --save-dir "${DATADIR}" \
     --gpu 0 \
     --experiment 1 \
     --test-scene "${SCENE}"
done

Single scene training (Table 1. in the supp.)

for SCENE in "chess" "fire" "heads" "office" "pumpkin" "redkitchen" "stairs"; do
   python -u ${RELPOSEGNN}/python/niantic/training/train.py \
     --dataset-dir "${SEVENSCENES}" \
     --train-data-dir "${SEVENSCENESRW}" \
     --test-data-dir "${SEVENSCENESRW}" \
     --save-dir "${DATADIR}" \
     --gpu 0 \
     --experiment 2 \
     --train-scene "${SCENE}" \
     --test-scene "${SCENE}" \
     --max-epoch 100
done

Reproducing results: Cambridge Landmarks

Graph generation

Before starting to train the model, train and test graphs should be generated to speed up the dataloaders, and not have to run NN search during training.

A. Pre-processed

Download

Test

mkdir -p "${CAMBRIDGERW}" || (mkdir -p "${CAMBRIDGERW}" && chmod go+w -R "${CAMBRIDGERW}")
for SCENE in "KingsCollege", "OldHospital", "StMarysChurch", "ShopFacade", "GreatCourt"; do
  wget -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/data/${SCENE}_fc8_sp3_test.tar" \
       -O "${CAMBRIDGERW}/${SCENE}_fc8_sp3_test.tar"
done

Train

for SCENE in "KingsCollege", "OldHospital", "StMarysChurch", "ShopFacade", "GreatCourt"; do
  wget -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/data/${SCENE}_fc8_sp3_train.tar" \
       -O "${CAMBRIDGERW}/${SCENE}_fc8_sp3_train.tar"
done

Extract

(cd "${CAMBRIDGERW}"; \
 find "${CAMBRIDGERW}" -mindepth 1 -maxdepth 1 -name "*.tar" | xargs -P 7 -I fileName sh -c 'tar -I pigz -xvf "fileName"')

Evaluation

Pre-trained

Download pre-trained model trained with entire Cambridge training scenes (Table 3 in the paper)

wget \
 -c "https://storage.googleapis.com/niantic-lon-static/research/relpose-gnn/models/relpose_gnn_cambridge_epoch_149.pth.tar" \
 -O "${DATADIR}/relpose_gnn_cambridge_epoch_149.pth.tar"

Evaluate on each Cambridge test scene

for SCENE in "KingsCollege", "OldHospital", "StMarysChurch", "ShopFacade", "GreatCourt"; do
   python -u ${RELPOSEGNN}/python/niantic/testing/test.py \
     --dataset-dir "${CAMBRIDGE}" \
     --test-data-dir "${CAMBRIDGERW}" \
     --weights "${DATADIR}/relpose_gnn_cambridge_epoch_149.pth.tar" \
     --save-dir "${DATADIR}" \
     --gpu 0 \
     --test-scene "${SCENE}"
done

Train yourself

Cambridge training (Table 3 in the paper)

python -u ${RELPOSEGNN}/python/niantic/training/train.py \
  --dataset-dir "${CAMBRIDGE}" \
  --train-data-dir "${CAMBRIDGERW}" \
  --test-data-dir "${CAMBRIDGERW}" \
  --save-dir "${DATADIR}" \
  --gpu 0 \
  --experiment 0 \
  --test-scene multi

🤝 Acknowledgements

We would like to thank Galen Han for his extensive help with this project.
We also thank Qunjie Zhou, Luwei Yang, Dominik Winkelbauer, Torsten Sattler, and Soham Saha for their help and advice with baselines.

	edge_feat = torch.cat(
	(e[torch.min(edge_index, 0)[0], ...],
	e[torch.max(edge_index, 0)[0], ...]),
	dim=1)

nianticlabs / relpose-gnn Goto Github PK

relpose-gnn's Introduction

Visual Camera Re-Localization using Graph Neural Networks and Relative Pose Supervision

🌌 Overview

📈 Results

✏️ 📄 Citation

Reproducing results: 7-Scenes

Source code

Setup

Set more paths

I. Prepare the 7-Scenes dataset

II. Image retrieval

Pre-processed

Generate yourself

III. Graph generation

III.A. Pre-processed

III.B. Generate yourself

Evaluation

Pre-trained

Train yourself

Reproducing results: Cambridge Landmarks

Graph generation

A. Pre-processed

Evaluation

Pre-trained

Train yourself

🤝 Acknowledgements

👩‍⚖️ License

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