LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment | Paper Citation Record

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This paper cites https://3d.bk.tudelft.nl/projects/ 3dbag/.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https://3d.bk.tudelft.nl/projects/ 3dbag/

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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https : / / www

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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https : / / www

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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https : / / www

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This paper cites https://www.bousai.go.jp/ kohou/kouhoubousai/r03/102/news_05.html.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https://www.bousai.go.jp/ kohou/kouhoubousai/r03/102/news_05.html

Reference 5

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This paper cites https://geospatialworld.net/prime/case- study / aec / 3d - evolution - of - the - dutch - city-of-rotterdam-the-netherlands/.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https://geospatialworld.net/prime/case- study / aec / 3d - evolution - of - the - dutch - city-of-rotterdam-the-netherlands/

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This paper cites http://www.openscenegraph.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment http://www.openscenegraph

Reference 7

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This paper cites https://www.smartnation.gov.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https://www.smartnation.gov

Reference 8

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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https : / / www

Reference 9

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This paper cites https : //www.swisstopo.admin.ch/en/vision- and- strategic-fields-of-action-2025.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https : //www.swisstopo.admin.ch/en/vision- and- strategic-fields-of-action-2025

Reference 10

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This paper cites https://www.gov.cn/ xinwen/2022-03/01/content_5676226.htm.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https://www.gov.cn/ xinwen/2022-03/01/content_5676226.htm

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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https : / / aws

Reference 12

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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment https : / / www

Reference 13

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This paper cites All about vlad.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment All about vlad

Reference 14

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This paper cites Netvlad: Cnn architecture for weakly supervised place recognition.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Netvlad: Cnn architecture for weakly supervised place recognition

Reference 15

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This paper cites Magsac: marginalizing sample consensus.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Magsac: marginalizing sample consensus

Reference 16

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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Unresolved cited work

Reference 17

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This paper cites Review of target geo-location al- gorithms for aerial remote sensing cameras without control points.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Review of target geo-location al- gorithms for aerial remote sensing cameras without control points

Reference 18

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This paper cites Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model

Reference 19

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This paper cites Sdpl: Shifting-dense partition learning for uav-view geo- localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Sdpl: Shifting-dense partition learning for uav-view geo- localization

Reference 20

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Observation 71b7f865-7d08-4fe5-af87-01bfabf80372 · outbound

This paper cites Real-time geo-localization using satellite imagery and topography for unmanned aerial vehicles.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Real-time geo-localization using satellite imagery and topography for unmanned aerial vehicles

Reference 21

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This paper cites SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 22

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This paper cites Monte carlo filtering on lie groups.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Monte carlo filtering on lie groups

Reference 23

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This paper cites Robust 3d vi- sual tracking using particle filtering on the special euclidean group: A combined approach of keypoint and edge features.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Robust 3d vi- sual tracking using particle filtering on the special euclidean group: A combined approach of keypoint and edge features

Reference 24

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This paper cites Optimal randomized ransac.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Optimal randomized ransac

Reference 25

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This paper cites Locally opti- mized ransac.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Locally opti- mized ransac

Reference 26

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Observation 1f63d521-a7e6-45be-8551-eb34b4c0b092 · outbound

This paper cites A transformer-based feature segmentation and region align- ment method for uav-view geo-localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment A transformer-based feature segmentation and region align- ment method for uav-view geo-localization

Reference 27

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This paper cites Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 28

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This paper cites Superpoint: Self-supervised interest point detection and description.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Superpoint: Self-supervised interest point detection and description

Reference 29

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This paper cites Roma: Robust dense fea- ture matching.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Roma: Robust dense fea- ture matching

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Observation 288115e8-bdc1-4c55-8d32-ba5ca432d1ff · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Pranet: Parallel reverse attention network for polyp segmentation

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Observation 8ddb3064-3a3d-4f31-81f5-b33823a244a6 · outbound

This paper cites Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981

Reference 32

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This paper cites gdls*: Gener- alized pose-and-scale estimation given scale and gravity pri- ors.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment gdls*: Gener- alized pose-and-scale estimation given scale and gravity pri- ors

Reference 33

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This paper cites Bags of binary words for fast place recognition in image sequences.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Bags of binary words for fast place recognition in image sequences

Reference 34

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This paper cites Self-supervising fine-grained region similarities for large-scale image localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Self-supervising fine-grained region similarities for large-scale image localization

Reference 35

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This paper cites Ogc city geography markup language (citygml) encoding standard.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Ogc city geography markup language (citygml) encoding standard

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This paper cites Review and analysis of solutions of the three point perspective pose estimation problem.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Review and analysis of solutions of the three point perspective pose estimation problem

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This paper cites Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition

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Observation 7cbe4927-e359-46b0-becc-729ad40b22a8 · outbound

This paper cites Investigating the role of image re- trieval for visual localization: An exhaustive benchmark.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Investigating the role of image re- trieval for visual localization: An exhaustive benchmark

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Observation 6204a689-140f-435e-be8c-54080bad0180 · outbound

This paper cites Particle filter networks with application to visual localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Particle filter networks with application to visual localization

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Observation eeaa2dd9-9ccd-445c-b0d5-efa766d3e0f8 · outbound

This paper cites Segment anything in high qual- ity.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segment anything in high qual- ity

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This paper cites Posenet: A convolutional network for real-time 6-dof cam- era relocalization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Posenet: A convolutional network for real-time 6-dof cam- era relocalization

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Observation d3d87515-6dbb-45ac-922b-2167bc62c433 · outbound

This paper cites Segment any- thing.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segment any- thing

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This paper cites A novel parametrization of the perspective-three-point prob- lem for a direct computation of absolute camera position and orientation.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment A novel parametrization of the perspective-three-point prob- lem for a direct computation of absolute camera position and orientation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:17:02.830199Z

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Observation 18c734a5-bcb3-4349-88db-6383813f284f · outbound

This paper cites Ogc city geography markup language (citygml) version 3.0 part 2: Gml encoding standard.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Ogc city geography markup language (citygml) version 3.0 part 2: Gml encoding standard

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:17:02.544780Z

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This paper cites Visual tracking via par- ticle filtering on the affine group.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Visual tracking via par- ticle filtering on the affine group

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:17:02.164751Z

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This paper cites Particle filtering on the euclidean group: framework and applications.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Particle filtering on the euclidean group: framework and applications

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:17:01.782894Z

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Observation 4f462ffb-fef3-4cf6-ae04-7a38b807f7a6 · outbound

This paper cites Monocular model-based 3d tracking of rigid objects: A survey.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Monocular model-based 3d tracking of rigid objects: A survey

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This paper cites Parallel inversion of neural radiance fields for robust pose estimation.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Parallel inversion of neural radiance fields for robust pose estimation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:17:01.071743Z

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Observation cedb56fb-edd4-4e1f-b7b0-2d61bdf8fc8a · outbound

This paper cites Receptive field block net for accurate and fast object detection.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Receptive field block net for accurate and fast object detection

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:17:00.728267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1050ed25-d090-4ab7-81c4-3b050f6f8959 · outbound

This paper cites Distinctive image features from scale- invariant keypoints.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Distinctive image features from scale- invariant keypoints

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:17:00.301632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c518d93a-2e89-46da-bc9d-fc59cc1869bf · outbound

This paper cites Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:46.304742Z

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Observation 8d39a2e3-8404-4265-9ee1-4fdc63c19afe · outbound

This paper cites A survey on vision-based uav navigation.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment A survey on vision-based uav navigation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:59.956453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6c2bbf7-7398-4c96-b5b0-000b4af6503b · outbound

This paper cites Large-scale, real-time visual–inertial localization re- visited.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Large-scale, real-time visual–inertial localization re- visited

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:59.660710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e04e14a2-801d-469e-93a6-5828c3b26a56 · outbound

This paper cites Segment anything in medical images.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segment anything in medical images

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:59.422091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 98b90c0a-fb3a-49a3-9f15-4ae36473b1ca · outbound

This paper cites Loc-nerf: Monte carlo local- ization using neural radiance fields.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Loc-nerf: Monte carlo local- ization using neural radiance fields

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:59.126260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e814e74f-8a2d-422f-998d-4cd8d44a9705 · outbound

This paper cites Segment anything model for medical image analysis: an experimental study.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segment anything model for medical image analysis: an experimental study

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:58.874742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b83c6662-0b11-4f45-83ae-3bba03d5b319 · outbound

This paper cites Visual place recognition for aerial imagery: A survey.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Visual place recognition for aerial imagery: A survey

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:50.294614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba7a2607-ff6f-4846-8b41-d787a2128554 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment DINOv2: Learning Robust Visual Features without Supervision

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:46.453572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0779298-91fa-4df5-9f7c-d8486704ab05 · outbound

This paper cites Meshloc: Mesh-based visual localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Meshloc: Mesh-based visual localization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:58.534969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 078134cf-b917-4b7e-9e60-08eee58c403a · outbound

This paper cites Visual localization using imperfect 3d models from the internet.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Visual localization using imperfect 3d models from the internet

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:58.150540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 57c42472-879b-4f25-8cc6-2a2d5a20fe7c · outbound

This paper cites Automated 3d reconstruction of lod2 and lod1 models for all 10 million buildings of the nether- lands.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Automated 3d reconstruction of lod2 and lod1 models for all 10 million buildings of the nether- lands

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:57.874745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa22a92e-6734-4e80-b76c-4d3ef3960444 · outbound

This paper cites Segloc: Learning segmentation-based representations for privacy-preserving visual localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segloc: Learning segmentation-based representations for privacy-preserving visual localization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:57.664453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dec274d4-0dc8-4ea0-ad58-9a61af311947 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment SAM 2: Segment Anything in Images and Videos

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:46.556133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 734f663b-e508-416b-b87a-44b14bc5e373 · outbound

This paper cites Segment anything, from space? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8355–8365, 2024.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segment anything, from space? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8355–8365, 2024

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:57.448452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b350baa3-1152-4c35-aa5d-dbd40eaf65f6 · outbound

This paper cites From coarse to fine: Robust hierarchical localization at large scale.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment From coarse to fine: Robust hierarchical localization at large scale

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:57.112945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37da4ae4-5c54-4031-adfa-6826e1576b7e · outbound

This paper cites Superglue: Learning feature matching with graph neural networks.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Superglue: Learning feature matching with graph neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:56.811053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0630e4cf-8d64-414e-9cab-0d27f204386a · outbound

This paper cites Orienternet: Visual localization in 2d public maps with neural matching.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Orienternet: Visual localization in 2d public maps with neural matching

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:56.504752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fdc3306e-126b-402c-a9e6-6699f263d6d7 · outbound

This paper cites Snap: Self-supervised neural maps for visual positioning and semantic understanding.Ad- vances in Neural Information Processing Systems, 36, 2024.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Snap: Self-supervised neural maps for visual positioning and semantic understanding.Ad- vances in Neural Information Processing Systems, 36, 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:56.192946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 702d5862-8c7b-4038-acda-2a64e1bb175a · outbound

This paper cites Structure- from-motion revisited.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Structure- from-motion revisited

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:55.914745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2090b41e-bc9b-457a-b480-fefc59c4c9fc · outbound

This paper cites Role of 3d city model data as open digital commons: a case study of openness in japan’s digital twin” project plateau”.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Role of 3d city model data as open digital commons: a case study of openness in japan’s digital twin” project plateau”

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:55.605526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd726069-f8a4-4c87-ac76-4d8fd00ac236 · outbound

This paper cites The last puzzle of global building footprints—mapping 280 million build- ings in east asia based on vhr images.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment The last puzzle of global building footprints—mapping 280 million build- ings in east asia based on vhr images

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:55.367416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d0a82d6-3156-414e-bffb-06bc9314db2d · outbound

This paper cites Beyond cross-view image retrieval: Highly accurate vehicle localization using satel- lite image.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Beyond cross-view image retrieval: Highly accurate vehicle localization using satel- lite image

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:55.135755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f96dab18-a20d-403f-89cc-e955571c9a49 · outbound

This paper cites Where am i looking at? joint location and orientation es- timation by cross-view matching.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Where am i looking at? joint location and orientation es- timation by cross-view matching

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:54.914845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a434111f-4ed0-4a15-a6e0-7297c5f23209 · outbound

This paper cites Loftr: Detector-free local feature matching with transformers.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Loftr: Detector-free local feature matching with transformers

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:54.527687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2929c142-32bc-4c07-a467-c6f56f9363bb · outbound

This paper cites Gable: A first fine-grained 3d building model of china on a national scale from very high resolu- tion satellite imagery.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Gable: A first fine-grained 3d building model of china on a national scale from very high resolu- tion satellite imagery

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:54.184828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 61745903-eb92-4f82-a915-507c9814e9a7 · outbound

This paper cites Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:47.964749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db7a596e-393b-44c2-8a26-34fbd52afb74 · outbound

This paper cites Probabilistic robotics.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Probabilistic robotics

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:53.894747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a22b1584-9939-4c15-b600-cc5bdfd55ce9 · outbound

This paper cites Long-term visual localization revisited.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Long-term visual localization revisited

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:53.494748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58f83a13-81ce-43f3-8371-93388727de77 · outbound

This paper cites The unreasonable effectiveness of pre- trained features for camera pose refinement.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment The unreasonable effectiveness of pre- trained features for camera pose refinement

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:53.168119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10d10f9f-e79b-4d51-859a-07aa0ca44f36 · outbound

This paper cites A survey on load transportation using mul- tirotor uavs.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment A survey on load transportation using mul- tirotor uavs

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:52.766807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b5bcdcc-ea83-4dfc-a0a6-4e1ed28db7b6 · outbound

This paper cites Efficient loftr: Semi-dense local feature matching with sparse-like speed.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Efficient loftr: Semi-dense local feature matching with sparse-like speed

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:52.344750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7f0e0ef7-d6d3-472f-90d3-161cc14d4625 · outbound

This paper cites F3net: fusion, feedback and focus for salient object detection.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment F3net: fusion, feedback and focus for salient object detection

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:52.094748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f900ff2-06ae-47ab-8106-28a1de5350ba · outbound

This paper cites MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:49.734744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5316cdec-2572-4d8f-b73d-9e6de7ce5019 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:48.265669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea5bd323-4b4b-4699-81bf-7a5d3e7a1880 · outbound

This paper cites Uavd4l: A large-scale dataset for uav 6-dof localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Uavd4l: A large-scale dataset for uav 6-dof localization

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:51.725487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d878162-e05b-4618-9a02-b4a3d1e4c526 · outbound

This paper cites Reviewing open data seman- tic 3d city models to develop novel 3d reconstruction meth- ods.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Reviewing open data seman- tic 3d city models to develop novel 3d reconstruction meth- ods

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:51.572220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0122cff9-bc87-4a43-8812-ced8beddcda2 · outbound

This paper cites Visual cross-view metric localization with dense un- certainty estimates.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Visual cross-view metric localization with dense un- certainty estimates

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:51.444944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1a68822-e785-401c-a7aa-35fc15a8f8e8 · outbound

This paper cites Moving Object Segmentation: All You Need Is SAM (and Flow).

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Moving Object Segmentation: All You Need Is SAM (and Flow)

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:49.434749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f37a61e-59d3-45c2-b6e0-93468eee4668 · outbound

This paper cites Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:48.414778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5906d8f1-a01c-443d-89c2-42dbba75cc95 · outbound

This paper cites Efficientsam: Leveraged masked image pretraining for efficient segment anything.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Efficientsam: Leveraged masked image pretraining for efficient segment anything

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:51.343178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aafa20ea-e14f-4136-be34-3da55590fac1 · outbound

This paper cites Render-and-compare: Cross-view 6-dof localization from noisy prior.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Render-and-compare: Cross-view 6-dof localization from noisy prior

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:51.274893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8247bbb9-3e72-4ed4-8ced-0be6aa03b834 · outbound

This paper cites Long-term visual localization with mobile sensors.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Long-term visual localization with mobile sensors

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:51.184568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da6928c4-d86d-4134-b1e3-1069c764cbc3 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:48.564358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8abcf81f-420f-40bf-961c-c9bb28b1f85c · outbound

This paper cites Fast Segment Anything.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Fast Segment Anything

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:48.616280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94814a64-23fb-418c-bb6a-54c7d5a2251d · outbound

This paper cites University- 1652: A multi-view multi-source benchmark for drone- based geo-localization.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment University- 1652: A multi-view multi-source benchmark for drone- based geo-localization

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:48.664753Z

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Observation 2732a053-4522-4366-aa8f-1c4ad136f0d5 · outbound

This paper cites Lod-loc: Aerial visual localization using lod 3d map with neural wireframe alignment.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Lod-loc: Aerial visual localization using lod 3d map with neural wireframe alignment

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:50.985780Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 656bcd6c-ebe1-434e-8425-79478d3a555b · outbound

This paper cites R2former: Unified retrieval and reranking transformer for place recognition.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment R2former: Unified retrieval and reranking transformer for place recognition

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:16:50.865832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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