Added MegaLoc and removed EigenPlaces (#458)

Author: gmberton

README.md

@@ -95,7 +95,7 @@ We show in [`pipeline_SfM.ipynb`](https://nbviewer.jupyter.org/github/cvg/Hierar
 
 - Supported local feature extractors: [SuperPoint](https://arxiv.org/abs/1712.07629), [DISK](https://arxiv.org/abs/2006.13566), [D2-Net](https://arxiv.org/abs/1905.03561), [SIFT](https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf), and [R2D2](https://arxiv.org/abs/1906.06195).
 - Supported feature matchers: [SuperGlue](https://arxiv.org/abs/1911.11763), its faster follow-up [LightGlue](https://github.com/cvg/LightGlue), and nearest neighbor search with ratio test, distance test, and/or mutual check. hloc also supports dense matching with [LoFTR](https://github.com/zju3dv/LoFTR).
-- Supported image retrieval: [NetVLAD](https://arxiv.org/abs/1511.07247), [AP-GeM/DIR](https://github.com/naver/deep-image-retrieval), [OpenIBL](https://github.com/yxgeee/OpenIBL), [CosPlace](https://github.com/gmberton/CosPlace) and  [EigenPlaces](https://github.com/gmberton/EigenPlaces).
+- Supported image retrieval: [NetVLAD](https://arxiv.org/abs/1511.07247), [AP-GeM/DIR](https://github.com/naver/deep-image-retrieval), [OpenIBL](https://github.com/yxgeee/OpenIBL), and [MegaLoc](https://github.com/gmberton/MegaLoc).
 
 Using NetVLAD for retrieval, we obtain the following best results:
 

hloc/extract_features.py

@@ -141,9 +141,9 @@
         "model": {"name": "openibl"},
         "preprocessing": {"resize_max": 1024},
     },
-    "eigenplaces": {
-        "output": "global-feats-eigenplaces",
-        "model": {"name": "eigenplaces"},
+    "megaloc": {
+        "output": "global-feats-megaloc",
+        "model": {"name": "megaloc"},
         "preprocessing": {"resize_max": 1024},
     },
 }

hloc/extractors/eigenplaces.py

@@ -0,0 +1,27 @@
+"""
+Code to use MegaLoc as a global features extractor.
+
+MegaLoc paper: https://arxiv.org/abs/2502.17237
+"""
+
+import torch
+import torchvision.transforms as tvf
+
+from ..utils.base_model import BaseModel
+
+
+class MegaPlaces(BaseModel):
+    required_inputs = ["image"]
+
+    def _init(self, conf):
+        self.net = torch.hub.load("gmberton/MegaLoc", "get_trained_model").eval()
+        mean = [0.485, 0.456, 0.406]
+        std = [0.229, 0.224, 0.225]
+        self.norm_rgb = tvf.Normalize(mean=mean, std=std)
+
+    def _forward(self, data):
+        image = self.norm_rgb(data["image"])
+        desc = self.net(image)
+        return {
+            "global_descriptor": desc,
+        }