Added ALIKED+LightGlue

README.md

@@ -33,7 +33,7 @@ The tool currently supports various popular image matching algorithms, namely:
 - [x] [DeDoDe](https://github.com/Parskatt/DeDoDe), 3DV 2024
 - [ ] [Mickey](https://github.com/nianticlabs/mickey), CVPR 2024
 - [x] [GIM](https://github.com/xuelunshen/gim), ICLR 2024
-- [x] [DUSt3R](https://github.com/naver/dust3r), arXiv 2023
+- [x] [ALIKED](https://github.com/Shiaoming/ALIKED), ICCV 2023
 - [x] [LightGlue](https://github.com/cvg/LightGlue), ICCV 2023
 - [x] [DarkFeat](https://github.com/THU-LYJ-Lab/DarkFeat), AAAI 2023
 - [x] [SFD2](https://github.com/feixue94/sfd2), CVPR 2023

hloc/extract_features.py

@@ -214,6 +214,35 @@
             "resize_max": 1600,
         },
     },
+
+    "aliked-n16-rot": {
+        "output": "feats-aliked-n16-rot",
+        "model": {
+            "name": "aliked",
+            "model_name": "aliked-n16rot",
+            "max_num_keypoints": -1,
+            "detection_threshold": 0.2,
+            "nms_radius": 2,
+        },
+        "preprocessing": {
+            "grayscale": False,
+            "resize_max": 1024,
+        },
+    },
+    "aliked-n16": {
+        "output": "feats-aliked-n16",
+        "model": {
+            "name": "aliked",
+            "model_name": "aliked-n16",
+            "max_num_keypoints": -1,
+            "detection_threshold": 0.2,
+            "nms_radius": 2,
+        },
+        "preprocessing": {
+            "grayscale": False,
+            "resize_max": 1024,
+        },
+    },
     "alike": {
         "output": "feats-alike-n5000-r1600",
         "model": {

hloc/extractors/aliked.py

@@ -0,0 +1,27 @@
+from lightglue import ALIKED as ALIKED_
+
+from ..utils.base_model import BaseModel
+
+
+class ALIKED(BaseModel):
+    default_conf = {
+        "model_name": "aliked-n16",
+        "max_num_keypoints": -1,
+        "detection_threshold": 0.2,
+        "nms_radius": 2,
+    }
+    required_inputs = ["image"]
+
+    def _init(self, conf):
+        conf.pop("name")
+        self.model = ALIKED_(**conf)
+
+    def _forward(self, data):
+        image = data["image"]
+        features = self.model(data)
+
+        return {
+            "keypoints": [f for f in features["keypoints"]],
+            "scores": [f for f in features["keypoint_scores"]],
+            "descriptors": [f.t() for f in features["descriptors"]],
+        }

hloc/match_features.py

@@ -80,6 +80,23 @@
             "force_resize": False,
         },
     },
+    "aliked-lightglue": {
+        "output": "matches-aliked-lightglue",
+        "model": {
+            "name": "lightglue",
+            "match_threshold": 0.2,
+            "width_confidence": 0.99,  # for point pruning
+            "depth_confidence": 0.95,  # for early stopping,
+            "features": "aliked",
+            "model_name": "aliked_lightglue.pth",
+        },
+        "preprocessing": {
+            "grayscale": True,
+            "resize_max": 1024,
+            "dfactor": 8,
+            "force_resize": False,
+        },
+    },
     "sift-lightglue": {
         "output": "matches-sift-lightglue",
         "model": {

ui/config.yaml

@@ -267,6 +267,17 @@ matcher_zoo:
       paper: https://arxiv.org/pdf/2306.13643
       project: null
       display: true
+  aliked+lightglue:
+    matcher: aliked-lightglue
+    feature: aliked-n16
+    dense: false
+    info:
+      name: ALIKED
+      source: "ICCV 2023"
+      github: https://github.com/Shiaoming/ALIKED
+      paper: https://arxiv.org/pdf/2304.03608.pdf
+      project: null
+      display: true
   superpoint+mnn:
     matcher: NN-mutual
     feature: superpoint_max