SuperPoint-Pytorch (A Pure Pytorch Implementation)

SuperPoint: Self-Supervised Interest Point Detection and Description

Thanks

This work is based on:

• Tensorflow implementation by Rémi Pautrat and Paul-Edouard Sarlin
• Official SuperPointPretrainedNetwork.
• Kornia

New update (20211016)

• Train your MagicPoint and SuperPoint

New update (20210904)

• You can now reproduce rpautrat/Superpoint with pytorch.
• Main Steps:
  • 1 Define network by superpoint_bn.py (Refer to train.py for more details)
  • 2 Set parameter eps=1e-3 for all BatchNormalization functions
  • 3 Load pretrained weight superpoint_bn.pth and run forward propagation

Usage

• 1 Prepare your data. Make directories data and export. The data directory should look like,

  data
  |-- coco
  |  |-- train2017
  |  |     |-- a.jpg
  |  |     |-- ...
  |  `-- test2017
  |        |-- b.jpg
  |        |-- ...
  |-- hpatches
  |   |-- i_ajuntament
  |   |   |--1.ppm
  |   |   |--...
  |   |   |--H_1_2
  |   |-- ...
  

  You can create soft links if you already have coco, hpatches data sets, the command is,

  cd data
  ln -s dir_to_coco ./coco
  

• 2 The training steps are much similar to rpautrat/Superpoint
  • 2.1 Train MagicPoint: python train.py ./config/magic_point_train.yaml
  • 2.2 Export coco labels: python export detections.py
  • 2.3 Train MagicPoint on coco labels data set (export by step 2.2)
    python train.py ./config/superpoint_train.py
  • 2.4 Train SuperPoint.py: python train.py ./config/superpoint_train.py
  • others. Validate detection repeatability:

  python export detections_repeatability.py   
  python compute_repeatability.py
  

  (NOTE: You have to edit *.yaml files to run corresponding tasks, especially for the following items

  model
      name: superpoint # magicpoint
   ...
  data:
      name: coco #synthetic
      image_train_path: ['./data/mp_coco_v2/images/train2017',] #several data sets can be list here
      label_train_path: ['./data/mp_coco_v2/labels/train2017/',]
      image_test_path: './data/mp_coco_v2/images/test2017/'
      label_test_path: './data/mp_coco_v2/labels/test2017/'