diff --git a/hloc/extract_features.py b/hloc/extract_features.py
index f7fd6990..5594d567 100644
--- a/hloc/extract_features.py
+++ b/hloc/extract_features.py
@@ -125,6 +125,17 @@
             "resize_max": 1024,
         },
     },
+    "xfeat": {
+        "output": "feats-xfeat-n5000-r1600",
+        "model": {
+            "name": "xfeat",
+            "max_keypoints": 5000,
+        },
+        "preprocessing": {
+            "grayscale": False,
+            "resize_max": 1600,
+        },
+    },
     # Global descriptors
     "dir": {
         "output": "global-feats-dir",
diff --git a/hloc/extractors/xfeat.py b/hloc/extractors/xfeat.py
new file mode 100644
index 00000000..5dc230f2
--- /dev/null
+++ b/hloc/extractors/xfeat.py
@@ -0,0 +1,33 @@
+import torch
+
+from hloc import logger
+
+from ..utils.base_model import BaseModel
+
+
+class XFeat(BaseModel):
+    default_conf = {
+        "keypoint_threshold": 0.005,
+        "max_keypoints": -1,
+    }
+    required_inputs = ["image"]
+
+    def _init(self, conf):
+        self.net = torch.hub.load(
+            "verlab/accelerated_features",
+            "XFeat",
+            pretrained=True,
+            top_k=self.conf["max_keypoints"],
+        )
+        logger.info("Load XFeat(sparse) model done.")
+
+    def _forward(self, data):
+        pred = self.net.detectAndCompute(
+            data["image"], top_k=self.conf["max_keypoints"]
+        )[0]
+        pred = {
+            "keypoints": pred["keypoints"][None],
+            "scores": pred["scores"][None],
+            "descriptors": pred["descriptors"].T[None],
+        }
+        return pred
diff --git a/hloc/match_features.py b/hloc/match_features.py
index 679e81e9..ad818a0c 100644
--- a/hloc/match_features.py
+++ b/hloc/match_features.py
@@ -42,6 +42,13 @@
             "features": "aliked",
         },
     },
+    "xfeat+lighterglue": {
+        "output": "matches-xfeat-lighterglue",
+        "model": {
+            "name": "lighterglue",
+            "features": "xfeat",
+        },
+    },
     "superglue": {
         "output": "matches-superglue",
         "model": {
diff --git a/hloc/matchers/lighterglue.py b/hloc/matchers/lighterglue.py
new file mode 100644
index 00000000..c5752d92
--- /dev/null
+++ b/hloc/matchers/lighterglue.py
@@ -0,0 +1,58 @@
+import torch
+from lightglue import LightGlue as LightGlue_
+
+from ..utils.base_model import BaseModel
+
+
+class LighterGlue(BaseModel):
+    default_conf_xfeat = {
+        "name": "lighterglue",  # just for interfacing
+        "input_dim": 64,  # input descriptor dimension (autoselected from weights)
+        "descriptor_dim": 96,
+        "add_scale_ori": False,
+        "add_laf": False,  # for KeyNetAffNetHardNet
+        "scale_coef": 1.0,  # to compensate for the SIFT scale bigger than KeyNet
+        "n_layers": 6,
+        "num_heads": 1,
+        "flash": True,  # enable FlashAttention if available.
+        "mp": False,  # enable mixed precision
+        "depth_confidence": -1,  # early stopping, disable with -1
+        "width_confidence": 0.95,  # point pruning, disable with -1
+        "filter_threshold": 0.1,  # match threshold
+        "weights": None,
+    }
+    required_inputs = [
+        "image0",
+        "keypoints0",
+        "descriptors0",
+        "image1",
+        "keypoints1",
+        "descriptors1",
+    ]
+
+    def _init(self, conf):
+        LightGlue_.default_conf = self.default_conf_xfeat
+        self.net = LightGlue_(None, **conf)
+        url = "https://github.com/verlab/accelerated_features/raw/main/weights/xfeat-lighterglue.pt"  # noqa: E501
+        state_dict = torch.hub.load_state_dict_from_url(url)
+
+        # rename old state dict entries
+        for i in range(self.net.conf.n_layers):
+            pattern = f"self_attn.{i}", f"transformers.{i}.self_attn"
+            state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}
+            pattern = f"cross_attn.{i}", f"transformers.{i}.cross_attn"
+            state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}
+            state_dict = {k.replace("matcher.", ""): v for k, v in state_dict.items()}
+
+        self.net.load_state_dict(state_dict, strict=False)
+
+    def _forward(self, data):
+        data["descriptors0"] = data["descriptors0"].transpose(-1, -2)
+        data["descriptors1"] = data["descriptors1"].transpose(-1, -2)
+
+        return self.net(
+            {
+                "image0": {k[:-1]: v for k, v in data.items() if k[-1] == "0"},
+                "image1": {k[:-1]: v for k, v in data.items() if k[-1] == "1"},
+            }
+        )
diff --git a/hloc/pairs_from_retrieval.py b/hloc/pairs_from_retrieval.py
index 32336801..4ccfbc10 100644
--- a/hloc/pairs_from_retrieval.py
+++ b/hloc/pairs_from_retrieval.py
@@ -81,6 +81,7 @@ def main(
     db_list=None,
     db_model=None,
     db_descriptors=None,
+    match_mask=None,
 ):
     logger.info("Extracting image pairs from a retrieval database.")
 
@@ -108,8 +109,15 @@ def main(
     query_desc = get_descriptors(query_names, descriptors)
     sim = torch.einsum("id,jd->ij", query_desc.to(device), db_desc.to(device))
 
-    # Avoid self-matching
-    self = np.array(query_names)[:, None] == np.array(db_names)[None]
+    if match_mask is None:
+        # Avoid self-matching
+        self = np.array(query_names)[:, None] == np.array(db_names)[None]
+    else:
+        assert match_mask.shape == (
+            len(query_names),
+            len(db_names),
+        ), "mask shape must match size of query and database images!"
+        self = match_mask
     pairs = pairs_from_score_matrix(sim, self, num_matched, min_score=0)
     pairs = [(query_names[i], db_names[j]) for i, j in pairs]
 
diff --git a/hloc/pairs_from_sequential.py b/hloc/pairs_from_sequential.py
new file mode 100644
index 00000000..2bb9811d
--- /dev/null
+++ b/hloc/pairs_from_sequential.py
@@ -0,0 +1,150 @@
+import argparse
+import collections.abc as collections
+import os
+from pathlib import Path
+from typing import List, Optional, Union
+
+import numpy as np
+
+from hloc import logger, pairs_from_retrieval
+from hloc.utils.io import list_h5_names
+from hloc.utils.parsers import parse_image_lists, parse_retrieval
+
+
+def main(
+    output: Path,
+    image_list: Optional[Union[Path, List[str]]] = None,
+    features: Optional[Path] = None,
+    window_size: Optional[int] = 10,
+    quadratic_overlap: bool = True,
+    use_loop_closure: bool = False,
+    retrieval_path: Optional[Union[Path, str]] = None,
+    retrieval_interval: Optional[int] = 2,
+    num_loc: Optional[int] = 5,
+) -> None:
+    """
+    Generate pairs of images based on sequential matching and optional loop closure.
+
+    Args:
+        output (Path): The output file path where the pairs will be saved.
+        image_list (Optional[Union[Path, List[str]]]):
+            A path to a file containing a list of images or a list of image names.
+        features (Optional[Path]):
+            A path to a feature file containing image features.
+        window_size (Optional[int]):
+            The size of the window for sequential matching. Default is 10.
+        quadratic_overlap (bool):
+            Whether to use quadratic overlap in sequential matching. Default is True.
+        use_loop_closure (bool):
+            Whether to use loop closure for additional matching. Default is False.
+        retrieval_path (Optional[Union[Path, str]]):
+            The path to the retrieval file for loop closure.
+        retrieval_interval (Optional[int]):
+            The interval for selecting query images for loop closure. Default is 2.
+        num_loc (Optional[int]):
+            The number of top retrieval matches to consider for loop closure.
+            Default is 5.
+
+    Raises:
+        ValueError: If neither image_list nor features are provided,
+            or if image_list is of an unknown type.
+
+    Returns:
+        None
+    """
+    if image_list is not None:
+        if isinstance(image_list, (str, Path)):
+            print(image_list)
+            names_q = parse_image_lists(image_list)
+        elif isinstance(image_list, collections.Iterable):
+            names_q = list(image_list)
+        else:
+            raise ValueError(f"Unknown type for image list: {image_list}")
+    elif features is not None:
+        names_q = list_h5_names(features)
+    else:
+        raise ValueError("Provide either a list of images or a feature file.")
+
+    pairs = []
+    N = len(names_q)
+
+    for i in range(N - 1):
+        for j in range(i + 1, min(i + window_size + 1, N)):
+            pairs.append((names_q[i], names_q[j]))
+
+            if quadratic_overlap:
+                q = 2 ** (j - i)
+                if q > window_size and i + q < N:
+                    pairs.append((names_q[i], names_q[i + q]))
+
+    if use_loop_closure:
+        retrieval_pairs_tmp: Path = output.parent / "retrieval-pairs-tmp.txt"
+
+        # match mask describes for each image, which images NOT to include in retrevial
+        # match search I.e., no reason to get retrieval matches for matches
+        # already included from sequential matching
+
+        query_list = names_q[::retrieval_interval]
+        M = len(query_list)
+        match_mask = np.zeros((M, N), dtype=bool)
+
+        for i in range(M):
+            for k in range(window_size + 1):
+                if i * retrieval_interval - k >= 0 and i * retrieval_interval - k < N:
+                    match_mask[i][i * retrieval_interval - k] = 1
+                if i * retrieval_interval + k >= 0 and i * retrieval_interval + k < N:
+                    match_mask[i][i * retrieval_interval + k] = 1
+
+                if quadratic_overlap:
+                    if (
+                        i * retrieval_interval - 2**k >= 0
+                        and i * retrieval_interval - 2**k < N
+                    ):
+                        match_mask[i][i * retrieval_interval - 2**k] = 1
+                    if (
+                        i * retrieval_interval + 2**k >= 0
+                        and i * retrieval_interval + 2**k < N
+                    ):
+                        match_mask[i][i * retrieval_interval + 2**k] = 1
+
+        pairs_from_retrieval.main(
+            retrieval_path,
+            retrieval_pairs_tmp,
+            num_matched=num_loc,
+            match_mask=match_mask,
+            db_list=names_q,
+            query_list=query_list,
+        )
+
+        retrieval = parse_retrieval(retrieval_pairs_tmp)
+
+        for key, val in retrieval.items():
+            for match in val:
+                pairs.append((key, match))
+
+        os.unlink(retrieval_pairs_tmp)
+
+    logger.info(f"Found {len(pairs)} pairs.")
+    with open(output, "w") as f:
+        f.write("\n".join(" ".join([i, j]) for i, j in pairs))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="""
+        Create a list of image pairs basedon the sequence of images on alphabetic order
+        """
+    )
+    parser.add_argument("--output", required=True, type=Path)
+    parser.add_argument("--image_list", type=Path)
+    parser.add_argument("--features", type=Path)
+    parser.add_argument(
+        "--overlap", type=int, default=10, help="Number of overlapping image pairs"
+    )
+    parser.add_argument(
+        "--quadratic_overlap",
+        action="store_true",
+        help="Whether to match images against their quadratic neighbors.",
+    )
+    args = parser.parse_args()
+    main(**args.__dict__)