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add: rdd sparse and dense matcher (#132)
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‎.gitmodules‎

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[submodule "imcui/third_party/LiftFeat"]
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path = imcui/third_party/LiftFeat
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url = https://github.com/agipro/LiftFeat.git
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[submodule "imcui/third_party/rdd"]
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path = imcui/third_party/rdd
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url = https://github.com/agipro/rdd.git

‎README.md‎

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| Algorithm | Supported | Conference/Journal | Year | GitHub Link |
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|------------------|-----------|--------------------|------|-------------|
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| DaD | ✅ | ARXIV | 2025 | [Link](https://github.com/Parskatt/dad) |
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| RDD | ✅ | CVPR | 2025 | [Link](https://github.com/xtcpete/rdd) |
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| LiftFeat | ✅ | ICRA | 2025 | [Link](https://github.com/lyp-deeplearning/LiftFeat) |
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| DaD | ✅ | ARXIV | 2025 | [Link](https://github.com/Parskatt/dad) |
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| MINIMA | ✅ | ARXIV | 2024 | [Link](https://github.com/LSXI7/MINIMA) |
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| XoFTR | ✅ | CVPR | 2024 | [Link](https://github.com/OnderT/XoFTR) |
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| EfficientLoFTR | ✅ | CVPR | 2024 | [Link](https://github.com/zju3dv/EfficientLoFTR) |

‎config/config.yaml‎

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paper: https://arxiv.org/abs/2505.0342
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project: null
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display: true
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rdd(sparse):
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matcher: NN-mutual
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feature: rdd
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dense: false
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info:
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name: RDD(sparse) #dispaly name
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source: "CVPR 2025"
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github: hhttps://github.com/xtcpete/rdd
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paper: https://arxiv.org/abs/2505.08013
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project: https://xtcpete.github.io/rdd
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display: true
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rdd(dense):
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matcher: rdd_dense
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dense: true
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info:
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name: RDD(dense) #dispaly name
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source: "CVPR 2025"
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github: hhttps://github.com/xtcpete/rdd
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paper: https://arxiv.org/abs/2505.08013
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project: https://xtcpete.github.io/rdd
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display: true
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dedode:
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matcher: Dual-Softmax
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feature: dedode

‎imcui/hloc/extract_features.py‎

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"resize_max": 1600,
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},
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},
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"rdd": {
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"output": "feats-rdd-n5000-r1600",
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"model": {
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"name": "rdd",
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"max_keypoints": 5000,
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},
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"preprocessing": {
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"grayscale": False,
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"resize_max": 1600,
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},
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},
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"aliked-n16-rot": {
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"output": "feats-aliked-n16-rot",
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"model": {

‎imcui/hloc/extractors/liftfeat.py‎

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from ..utils.base_model import BaseModel
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from .. import logger, MODEL_REPO_ID
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fire_path = Path(__file__).parent / "../../third_party/LiftFeat"
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sys.path.append(str(fire_path))
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liftfeat_path = Path(__file__).parent / "../../third_party/LiftFeat"
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sys.path.append(str(liftfeat_path))
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from models.liftfeat_wrapper import LiftFeat
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‎imcui/hloc/extractors/rdd.py‎

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import sys
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import yaml
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from pathlib import Path
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from ..utils.base_model import BaseModel
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from .. import logger, MODEL_REPO_ID, DEVICE
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rdd_path = Path(__file__).parent / "../../third_party/rdd"
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sys.path.append(str(rdd_path))
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from RDD.RDD import build as build_rdd
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class Rdd(BaseModel):
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default_conf = {
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"keypoint_threshold": 0.1,
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"max_keypoints": 4096,
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"model_name": "RDD-v2.pth",
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}
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required_inputs = ["image"]
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def _init(self, conf):
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logger.info("Loading RDD model...")
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model_path = self._download_model(
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repo_id=MODEL_REPO_ID,
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filename="{}/{}".format(Path(__file__).stem, self.conf["model_name"]),
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)
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config_path = rdd_path / "configs/default.yaml"
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with open(config_path, "r") as file:
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config = yaml.safe_load(file)
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config["top_k"] = conf["max_keypoints"]
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config["detection_threshold"] = conf["keypoint_threshold"]
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config["device"] = DEVICE
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self.net = build_rdd(config=config, weights=model_path)
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self.net.eval()
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logger.info("Loading RDD model done!")
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def _forward(self, data):
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image = data["image"]
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pred = self.net.extract(image)[0]
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keypoints = pred["keypoints"]
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descriptors = pred["descriptors"]
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scores = pred["scores"]
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if self.conf["max_keypoints"] < len(keypoints):
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idxs = scores.argsort()[-self.conf["max_keypoints"] or None :]
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keypoints = keypoints[idxs, :2]
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descriptors = descriptors[idxs]
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scores = scores[idxs]
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pred = {
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"keypoints": keypoints[None],
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"descriptors": descriptors[None].permute(0, 2, 1),
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"scores": scores[None],
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}
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return pred

‎imcui/hloc/match_dense.py‎

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"dfactor": 8,
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},
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},
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"rdd_dense": {
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"output": "matches-rdd_dense",
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"model": {
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"name": "rdd_dense",
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"model_name": "RDD-v2.pth",
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"max_keypoints": 2000,
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"match_threshold": 0.2,
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},
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"preprocessing": {
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"grayscale": False,
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"force_resize": True,
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"resize_max": 1024,
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"width": 320,
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"height": 240,
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"dfactor": 8,
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},
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},
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"minima_roma": {
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"output": "matches-minima_roma",
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"model": {

‎imcui/hloc/matchers/rdd_dense.py‎

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import sys
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import yaml
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import torch
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from pathlib import Path
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from ..utils.base_model import BaseModel
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from .. import logger, MODEL_REPO_ID, DEVICE
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rdd_path = Path(__file__).parent / "../../third_party/rdd"
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sys.path.append(str(rdd_path))
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from RDD.RDD import build as build_rdd
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from RDD.RDD_helper import RDD_helper
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class RddDense(BaseModel):
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default_conf = {
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"keypoint_threshold": 0.1,
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"max_keypoints": 4096,
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"model_name": "RDD-v2.pth",
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"match_threshold": 0.1,
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}
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required_inputs = ["image0", "image1"]
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def _init(self, conf):
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logger.info("Loading RDD model...")
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model_path = self._download_model(
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repo_id=MODEL_REPO_ID,
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filename="{}/{}".format("rdd", self.conf["model_name"]),
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)
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config_path = rdd_path / "configs/default.yaml"
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with open(config_path, "r") as file:
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config = yaml.safe_load(file)
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config["top_k"] = conf["max_keypoints"]
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config["detection_threshold"] = conf["keypoint_threshold"]
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config["device"] = DEVICE
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rdd_net = build_rdd(config=config, weights=model_path)
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rdd_net.eval()
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self.net = RDD_helper(rdd_net)
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logger.info("Loading RDD model done!")
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def _forward(self, data):
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img0 = data["image0"]
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img1 = data["image1"]
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mkpts_0, mkpts_1, conf = self.net.match_dense(
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img0, img1, thr=self.conf["match_threshold"]
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)
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pred = {
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"keypoints0": torch.from_numpy(mkpts_0),
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"keypoints1": torch.from_numpy(mkpts_1),
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"mconf": torch.from_numpy(conf),
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}
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return pred

‎imcui/third_party/rdd‎

Submodule rdd added at b9307a7

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