Add new local feature extractor

In this part, we introduce how to add a new feature extractor to XRLocalization. The XRLocalization has provided a global feature netvlad and a local feature d2net. If you intend to use other feature like superpoint in your experiment, a few steps should be taken as follow.

0. Clone the code and place it in the 3rdparty folder

cd xrlocalization/3rdparty
git clone https://github.com/magicleap/SuperGluePretrainedNetwork.git

1. Create a python file in xrlocalization/xrloc/features

vim superpoint.py

2. Create a warpper class in superpoint.py

import sys
import torch.nn as nn

from xrloc.utils.miscs import get_parent_dir

sys.path.append(get_parent_dir(__file__) + '/../3rdparty')
from SuperGluePretrainedNetwork.models.superpoint import SuperPoint as SP


class SuperPoint(nn.Module):
   default_config = {
       'descriptor_dim': 256,
       'nms_radius': 4,
       'keypoint_threshold': 0.005,
       'max_keypoints': -1,
       'remove_borders': 4,
   }

   def __init__(self, config=default_config):
       super().__init__()
       self.config = {**self.default_config, **config}
       self.model = SP(self.config)

   def forward(self, image):
       input = {'image': image}
       return self.model(input)

Note that the lowercase form of the class name should be the same as the file name.

3. Register a default config in xrlocalization/xrloc/features/extractor.py

Add a default config in support_extractors like this.

support_extractors = {
    //...
    'superpoint': {
        'image_size': 640,
        'gray_image': True,
        'model': {
            'nms_radius': 4,
            'keypoint_threshold': 0.005,
            'max_keypoints': -1,
            'remove_borders': 4,
        },
    },
}

Now, you can use superpoint in your experiments. Note that superpoint is not permitted for commercial usage. This project does not include superpoint code. If you use superpoint, you need to download it by yourself, and please abide by its license.