add: liftfeat (#130)

* add: liftfeat

* style: pre-commit fixes

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Vincentqyw

.gitmodules

@@ -76,3 +76,6 @@
 [submodule "imcui/third_party/dad"]
     path = imcui/third_party/dad
     url = https://github.com/Parskatt/dad
+[submodule "imcui/third_party/LiftFeat"]
+    path = imcui/third_party/LiftFeat
+    url = https://github.com/agipro/LiftFeat.git

README.md

@@ -34,6 +34,7 @@ The tool currently supports various popular image matching algorithms, namely:
 | Algorithm        | Supported | Conference/Journal | Year | GitHub Link |
 |------------------|-----------|--------------------|------|-------------|
 | DaD            | ✅ | ARXIV   | 2025 | [Link](https://github.com/Parskatt/dad) |
+| LiftFeat       | ✅ | ICRA    | 2025 | [Link](https://github.com/lyp-deeplearning/LiftFeat) |
 | MINIMA         | ✅ | ARXIV   | 2024 | [Link](https://github.com/LSXI7/MINIMA) |
 | XoFTR          | ✅ | CVPR    | 2024 | [Link](https://github.com/OnderT/XoFTR) |
 | EfficientLoFTR | ✅ | CVPR    | 2024 | [Link](https://github.com/zju3dv/EfficientLoFTR) |

config/config.yaml

@@ -242,6 +242,17 @@ matcher_zoo:
       paper: https://arxiv.org/abs/2404.19174
       project: null
       display: false
+  liftfeat(sparse):
+    matcher: NN-mutual
+    feature: liftfeat
+    dense: false
+    info:
+      name: LiftFeat #dispaly name
+      source: "ICRA 2025"
+      github: https://github.com/lyp-deeplearning/LiftFeat
+      paper: https://arxiv.org/abs/2505.0342
+      project: null
+      display: true
   dedode:
     matcher: Dual-Softmax
     feature: dedode

imcui/hloc/extract_features.py

@@ -214,6 +214,17 @@
             "resize_max": 1600,
         },
     },
+    "liftfeat": {
+        "output": "feats-liftfeat-n5000-r1600",
+        "model": {
+            "name": "liftfeat",
+            "max_keypoints": 5000,
+        },
+        "preprocessing": {
+            "grayscale": False,
+            "resize_max": 1600,
+        },
+    },
     "aliked-n16-rot": {
         "output": "feats-aliked-n16-rot",
         "model": {

imcui/hloc/extractors/liftfeat.py

@@ -0,0 +1,48 @@
+import sys
+from pathlib import Path
+from ..utils.base_model import BaseModel
+from .. import logger
+
+fire_path = Path(__file__).parent / "../../third_party/LiftFeat"
+sys.path.append(str(fire_path))
+
+from models.liftfeat_wrapper import LiftFeat, MODEL_PATH
+
+
+class Liftfeat(BaseModel):
+    default_conf = {
+        "keypoint_threshold": 0.05,
+        "max_keypoints": 5000,
+    }
+
+    required_inputs = ["image"]
+
+    def _init(self, conf):
+        logger.info("Loading LiftFeat model...")
+        self.net = LiftFeat(
+            weight=MODEL_PATH,
+            detect_threshold=self.conf["keypoint_threshold"],
+            top_k=self.conf["max_keypoints"],
+        )
+        logger.info("Loading LiftFeat model done!")
+
+    def _forward(self, data):
+        image = data["image"].cpu().numpy().squeeze() * 255
+        image = image.transpose(1, 2, 0)
+        pred = self.net.extract(image)
+
+        keypoints = pred["keypoints"]
+        descriptors = pred["descriptors"]
+        scores = pred["scores"]
+        if self.conf["max_keypoints"] < len(keypoints):
+            idxs = scores.argsort()[-self.conf["max_keypoints"] or None :]
+            keypoints = keypoints[idxs, :2]
+            descriptors = descriptors[idxs]
+            scores = scores[idxs]
+
+        pred = {
+            "keypoints": keypoints[None],
+            "descriptors": descriptors[None].permute(0, 2, 1),
+            "scores": scores[None],
+        }
+        return pred