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Paper Citation Record · LEDGER

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling

As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2605.29538.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.29538 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:44:02.202288Z

measured 39 of 39 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

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Reference resolution

39 of 39 outbound references displayed

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  • verified fuzzy0
  • unresolved38
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Outbound references

Observation a5c8489d-48d3-4de8-995e-510d83af5605 · outbound

This paper cites Blockage- resilient integrated sensing and communication in mmwave networks: Multi-view collaboration and efficient task allocation,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Blockage- resilient integrated sensing and communication in mmwave networks: Multi-view collaboration and efficient task allocation,

Reference 1

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Observation 0b78827e-1694-473f-9f56-5b873426f06e · outbound

This paper cites Cram ´er-rao bound analysis and beam- forming design for integrated sensing and communication with extended targets,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Cram ´er-rao bound analysis and beam- forming design for integrated sensing and communication with extended targets,

Reference 2

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Observation 6f86677f-7b35-41a6-9fa6-9b214f59d37c · outbound

This paper cites A spatiotemporal approach for secure crowd- sourced radio environment map construction,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling A spatiotemporal approach for secure crowd- sourced radio environment map construction,

Reference 3

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:91553458629a8088c785f2324b6ee8088bba7ca35053be78d34c7aed2bc69392

Observation f9e8ab61-ea25-4073-ba26-c82bfeba8fab · outbound

This paper cites Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement,

Reference 4

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:31674bffef633b7a781d507ad9580cacbe542f91472ae286ca949bb573e8c7d9

Observation 520b9b59-3b0c-4c10-8401-b8db31a488b6 · outbound

This paper cites Radiounet: Fast radio map estimation with convolutional neural networks,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Radiounet: Fast radio map estimation with convolutional neural networks,

Reference 5

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:72c92c732da492c38e88f8e7f87e8beb8f9adcc62b80db984a11c8df6b3aa34d

Observation dc70efb9-fe15-4c41-82ce-d9c28e893900 · outbound

This paper cites Dataset of pathloss and toa radio maps with localization application,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Dataset of pathloss and toa radio maps with localization application,

Reference 7

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:5796d394e26d281d55eba068f11ee035871267399901277954856264fc82222d

Observation a66cd119-2724-44d9-8437-7b066505deb7 · outbound

This paper cites Joint trajectory and communication design for multi-uav enabled wireless networks,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Joint trajectory and communication design for multi-uav enabled wireless networks,

Reference 8

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:5efea3d80666f90ea87eeb348ef1bc010ea6f3cc468e4752f538e4c2297a19ce

Observation 531d623c-4ec2-48bf-bb16-96b908c376ee · outbound

This paper cites A mobility-resilient spectrum sharing framework for operating wireless uavs in the 6 ghz band,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling A mobility-resilient spectrum sharing framework for operating wireless uavs in the 6 ghz band,

Reference 9

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:f950a575d43a66b2674accb38660446b04f8c6c94c98f505780d6ddf83aaa079

Observation cf3a9574-e89b-4a99-9267-2ce25a23a091 · outbound

This paper cites Sparse bayesian learning-based hierarchical construction for 3d radio environment maps incorporating channel shadowing,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Sparse bayesian learning-based hierarchical construction for 3d radio environment maps incorporating channel shadowing,

Reference 10

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:728458249f4b710e5df1c8e4aa85bfbc666763fc974d53290ea9b30664f632f2

Observation f30dd52f-1684-4ba5-afb4-e7b6b4804efd · outbound

This paper cites Radio map assisted multi-uav target searching,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Radio map assisted multi-uav target searching,

Reference 11

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:3c6a27f5ea4f79c3e72219973540a4b1b6b982c60d515635da424fa085eaf37d

Observation b63e3358-cbf2-45c8-8749-d878bea2342b · outbound

This paper cites Radioformer: A multiple-granularity radio map estimation transformer with 1‱spatial sampling,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Radioformer: A multiple-granularity radio map estimation transformer with 1‱spatial sampling,

Reference 12

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:d84e07bef81bff3278fd28d7da647be2ef8cedd15029e6c58ea6e7bbc44d06ca

Observation bc72149a-8c45-4222-9912-ab477506027d · outbound

This paper cites Bayesian active learning for sample efficient 5g radio map reconstruction,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Bayesian active learning for sample efficient 5g radio map reconstruction,

Reference 13

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:0af449e8285a44056dbe857db30965711f7c6b5c025bb3ff754dbfdba7d663c9

Observation 62f2c0e8-c856-42b9-b61b-86197ef2eda7 · outbound

This paper cites Deep completion autoencoders for radio map estimation,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Deep completion autoencoders for radio map estimation,

Reference 14

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:a4505106413518f05221f31d46caca6e8770cb1666e3cf00d3e689e417b3db45

Observation b9cb23d1-d799-4b16-8dfe-169145db7e37 · outbound

This paper cites A self-supervised learning-based channel estimation for irs-aided communication without ground truth,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling A self-supervised learning-based channel estimation for irs-aided communication without ground truth,

Reference 15

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:4fe712695629f8139066bdfb9e40a666bbed332e5583a7e2f583b270b433a591

Observation 8efab5db-a06a-44d7-b273-2516cdd2cd3e · outbound

This paper cites Wide- band millimeter-wave propagation measurements and channel models for future wireless communication system design,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Wide- band millimeter-wave propagation measurements and channel models for future wireless communication system design,

Reference 16

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:e1e421f70329ca5d925f7bd96e8b9ae7680057d8f45cda55f238f1174aac44de

Observation 557407f9-9dcd-4405-a628-8628cb0f3ab6 · outbound

This paper cites Ray tracing for radio propagation modeling: Principles and applications,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Ray tracing for radio propagation modeling: Principles and applications,

Reference 17

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:9f71297d16263c015219603f73d51e407af1d47c0d97194ed03dd7722ae40636

Observation 1353c92a-2103-4118-a238-10ea407347a3 · outbound

This paper cites A statistical basis for lognormal shadowing effects in multipath fading channels,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling A statistical basis for lognormal shadowing effects in multipath fading channels,

Reference 18

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:22a98e6d7aec40eb296305a746a7fcb15d5baef5be1124c54e3f8457373b2ae1

Observation f24738aa-ddf5-4bfc-ace7-6db836a69c79 · outbound

This paper cites Radio map estimation: A data-driven approach to spectrum cartography,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Radio map estimation: A data-driven approach to spectrum cartography,

Reference 19

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:942180534ad6ed3681c231f9b9af8f3f1d61ad4a2567e933aba35f8cab92fc65

Observation 98d18147-1bfd-4cd1-8cba-c6cbf86746fa · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling U-net: Convolutional networks for biomedical image segmentation,

Reference 20

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:27fed9b35a646cf26918b8b1b6c9fa0b74b915a7d6fc87884b54ad13d079c3e5

Observation 25dc1684-8565-4d24-81db-d6b7d1722b18 · outbound

This paper cites Paying deformable attention to sparse spatial observations for deep radio map estimation,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Paying deformable attention to sparse spatial observations for deep radio map estimation,

Reference 21

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:1c222a2923fb24f3559f0cde666558ec64fada21b202bc584f321cdb7bd8a662

Observation f00ba568-b96f-49d5-8029-6de6dfbe907f · outbound

This paper cites Deformable convolutional networks,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Deformable convolutional networks,

Reference 22

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Observation b37fa2a0-3cdb-43db-a736-f9ff82b2c67e · outbound

This paper cites Deformable convnets v2: More deformable, better results,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Deformable convnets v2: More deformable, better results,

Reference 23

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:5b54d6dae1f182a2e77d233997d3c27740eed5adbaf05de3b2ee51eb4dcb89af

Observation 0c860a32-071c-4882-8f09-494129bd64e9 · outbound

This paper cites Radiodun: A physics-inspired deep unfolding network for radio map estimation,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Radiodun: A physics-inspired deep unfolding network for radio map estimation,

Reference 24

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:5caf53864ae2879f3fb5e518625cef056edaf468a67d1b60ab734877cf92c504

Observation 902b8872-7b95-44ad-9a3c-c71b765e4103 · outbound

This paper cites Pmnet: Robust pathloss map prediction via supervised learning,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Pmnet: Robust pathloss map prediction via supervised learning,

Reference 25

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Observation 849af535-d309-4049-b962-ff7b8c4a69ba · outbound

This paper cites Attention is all you need,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Attention is all you need,

Reference 26

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:d10769d31de75cf2f07f48751ca7e23f55e6585e17052232be0ba1ea13eb91b4

Observation e4a1a06a-e7f7-45c8-a231-3cfc405efed3 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Nerf: Representing scenes as neural radiance fields for view synthesis,

Reference 27

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:202e976030295acccc246998d0a37871e0390fd4d395a64a7fa90471cf41b531

Observation fad7ce60-f616-4360-adde-dbbb028695bd · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling 3d gaussian splatting for real-time radiance field rendering

Reference 28

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:104770c6e580608e215f2266e9fc51a0b18b7393984a388d56d42782dd6724c8

Observation 5bf52ca5-2b98-47df-9e90-ed62d74d0c04 · outbound

This paper cites Nerf2: Neural radio-frequency radiance fields,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Nerf2: Neural radio-frequency radiance fields,

Reference 29

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:aa36c76f04e0f191bd3a00ae8c9de78a46caef0b2067f5b2d2a188976031c1ce

Observation 02e4959c-3cae-4384-b16c-e12a3ab38349 · outbound

This paper cites NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:5bd2d78ccdb4ed4d7c6a14c5b72d787004bc5542c41764b24615610f5225451d

Observation 788070c4-5e88-4f80-8d33-07321d23081b · outbound

This paper cites Neural representation for wireless radiation field reconstruction: A 3d gaussian splatting approach,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Neural representation for wireless radiation field reconstruction: A 3d gaussian splatting approach,

Reference 31

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Observation 26af0987-140f-4736-a6d4-dde63618f0ed · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 32

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Observation 42b2b82a-e5e0-49a7-8a9f-0c3b3f791642 · outbound

This paper cites Bracewell,The Fourier Transform and its Applications, Tokyo.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Bracewell,The Fourier Transform and its Applications, Tokyo

Reference 33

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Observation d4826fe0-f4b1-4197-9fc7-13ab57c0fb84 · outbound

This paper cites Learning represen- tations by back-propagating errors,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Learning represen- tations by back-propagating errors,

Reference 34

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Observation 17f327f7-eb19-464d-a502-7724eb899d35 · outbound

This paper cites an unresolved cited work.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Unresolved cited work

Reference 35

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source=pdf_text observed=2026-06-29T08:44:02.202288Z digest=sha256:d14f0a825eaf64f1ba8c14e9ce2d3f95b9687f4cc1e3bd21dcc237bc5343f376

Observation 32ed60b9-c87b-4d92-9944-9c5df0a02d97 · outbound

This paper cites Radiodiff-3d: A 3d× 3d radio map dataset and generative diffusion based benchmark for 6g environment-aware communication,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Radiodiff-3d: A 3d× 3d radio map dataset and generative diffusion based benchmark for 6g environment-aware communication,

Reference 36

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Observation 6b8741d9-f131-4065-b41b-f846560a227a · outbound

This paper cites Generative ai on spectrumnet: An open benchmark of multiband 3- d radio maps,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Generative ai on spectrumnet: An open benchmark of multiband 3- d radio maps,

Reference 37

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Observation 944ee3a3-5a0f-4570-9397-d97d9f796b1d · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Image quality assessment: from error visibility to structural similarity,

Reference 38

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Observation 5b359262-a93f-40e2-bb12-75b5c48d7ebb · outbound

This paper cites Radiodiff: An effective generative diffusion model for sampling-free dynamic radio map construction,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Radiodiff: An effective generative diffusion model for sampling-free dynamic radio map construction,

Reference 39

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Observation 37970221-5f28-4851-84ba-6c73e0a5bb33 · outbound

This paper cites Physics-informed diffusion model for radio environment map reconstruction from sparse measurements,.

RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling Physics-informed diffusion model for radio environment map reconstruction from sparse measurements,

Reference 40

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