third_party/DeDoDe/README.md

DeDoDe 🎶
Detect, Don't Describe, Describe, Don't Detect,
for Local Feature Matching

Johan Edstedt · Georg Bökman · Mårten Wadenbäck · Michael Felsberg ·

Paper (TODO) | Project Page (TODO)

The DeDoDe detector learns to detect 3D consistent repeatable keypoints, which the DeDoDe descriptor learns to match. The result is a powerful decoupled local feature matcher.

We experimentally find that DeDoDe significantly closes the performance gap between detector + descriptor models and fully-fledged matchers. The potential of DeDoDe is not limited to local feature matching, in fact we find that we can improve state-of-the-art matchers by incorporating DeDoDe keypoints.

How to Use DeDoDe?

Below we show how DeDoDe can be run, you can also check out the demos

from DeDoDe import dedode_detector_L, dedode_descriptor_B
from DeDoDe.matchers.dual_softmax_matcher import DualSoftMaxMatcher

detector = dedode_detector_L(weights = torch.load("dedode_detector_L.pth"))
descriptor = dedode_descriptor_B(weights = torch.load("dedode_descriptor_B.pth"))
matcher = DualSoftMaxMatcher()

im_A_path = "assets/im_A.jpg"
im_B_path = "assets/im_B.jpg"
im_A = Image.open(im_A_path)
im_B = Image.open(im_B_path)
W_A, H_A = im_A.size
W_B, H_B = im_B.size


detections_A = detector.detect_from_path(im_A_path, num_keypoints = 10_000)
keypoints_A, P_A = detections_A["keypoints"], detections_A["confidence"]

detections_B = detector.detect_from_path(im_B_path, num_keypoints = 10_000)
keypoints_B, P_B = detections_B["keypoints"], detections_B["confidence"]

description_A = descriptor.describe_keypoints_from_path(im_A_path, keypoints_A)["descriptions"]
description_B = descriptor.describe_keypoints_from_path(im_B_path, keypoints_B)["descriptions"]

matches_A, matches_B, batch_ids = matcher.match(keypoints_A, description_A,
    keypoints_B, description_B,
    P_A = P_A, P_B = P_B,
    normalize = True, inv_temp=20, threshold = 0.1)#Increasing threshold -> fewer matches, fewer outliers

matches_A, matches_B = matcher.to_pixel_coords(matches_A, matches_B, H_A, W_A, H_B, W_B)

Pretrained Models

Right now you can find them here: https://github.com/Parskatt/DeDoDe/releases/tag/dedode_pretrained_models Probably we'll add some autoloading in the near future.

BibTeX

Coming Soon ;)