| Image Matching | |
| ============== | |
| Image matching is a process of finding pixel and region correspondences between two images of the same scene. | |
| Such correspondences are useful for 3D reconstruction of the scene and relative camera pose estimation. | |
| It is also known as "Wide baseline stereo" and you can read more about it at `Wide Baseline Stereo Blog <https://ducha-aiki.github.io/wide-baseline-stereo-blog/2021/01/09/wxbs-in-simple-terms.html>`_ | |
| We provide many modules and functions for the image matching: from building blocks like `local feature detectors <https://kornia.readthedocs.io/en/latest/feature.html#detectors>`_, `descriptors <https://kornia.readthedocs.io/en/latest/feature.html#descriptors>`_, | |
| `descriptor matching <https://kornia.readthedocs.io/en/latest/feature.html#matching>`_, `geometric model estimation <https://kornia.readthedocs.io/en/latest/geometry.epipolar.html#fundamental>`_ | |
| However we recommend to start with high-level API, such as :py:class:`~kornia.feature.LoFTR` you can use to find correspondence between two images. | |
| .. code:: python | |
| from kornia.feature import LoFTR | |
| matcher = LoFTR(pretrained="outdoor") | |
| input = {"image0": img1, "image1": img2} | |
| correspondences_dict = matcher(input) | |
| .. image:: https://raw.githubusercontent.com/kornia/data/main/matching/matching_loftr.jpg | |
| You also can go through or full tutorial using Colab found `here <https://kornia-tutorials.readthedocs.io/en/latest/image_matching.html>`_. | |