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Paper Citation Record · LEDGER

Neural Surface and Reflectance Modelling from 3D Radar Data

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2603.25623.

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

pith.paper-citation-record.v1
2603.25623 v3

Coverage vector

measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-02T17:25:27.784468Z

measured 36 of 36 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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36 of 36 outbound references displayed

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Outbound references

Observation 66be2279-3220-4aad-bb15-19a58109ae22 · outbound

This paper cites NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis.

Neural Surface and Reflectance Modelling from 3D Radar Data NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Reference 1

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source=pdf_text observed=2026-08-02T17:25:27.687943Z digest=sha256:f7e8657ccd7a53a0a39d59c0d59ab5953ce0d5bf60e9d14e3be2cfec666fc1a9

Observation 1f017092-ecfe-4c57-b0dc-8852e06c9630 · outbound

This paper cites LONER: LiDAR Only Neural Representations for Real-Time SLAM.

Neural Surface and Reflectance Modelling from 3D Radar Data LONER: LiDAR Only Neural Representations for Real-Time SLAM

Reference 2

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Observation ff916762-4601-40b0-b77f-857126ae4baf · outbound

This paper cites SHINE- Mapping: Large-Scale 3D Mapping Using Sparse Hi- erarchical Implicit Neural Representations.

Neural Surface and Reflectance Modelling from 3D Radar Data SHINE- Mapping: Large-Scale 3D Mapping Using Sparse Hi- erarchical Implicit Neural Representations

Reference 3

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Observation b110c92b-4845-4b5b-afe4-f5c2402d7d2a · outbound

This paper cites 3QFP: Efficient neural implicit surface reconstruction using Tri-Quadtrees and Fourier feature Positional encoding.

Neural Surface and Reflectance Modelling from 3D Radar Data 3QFP: Efficient neural implicit surface reconstruction using Tri-Quadtrees and Fourier feature Positional encoding

Reference 4

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Observation f6069992-fcb7-4ec4-b934-f12e381c5e39 · outbound

This paper cites Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar.

Neural Surface and Reflectance Modelling from 3D Radar Data Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar

Reference 5

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source=pdf_text observed=2026-08-02T17:25:27.700744Z digest=sha256:cb6fdab1f336d4c10ae3ab1a38bcb0d501797e3310e93718020a3899df33c628

Observation c10ba524-3767-4e7e-a054-468a23ff207e · outbound

This paper cites DART: Implicit Doppler Tomography for Radar Novel View Synthesis.

Neural Surface and Reflectance Modelling from 3D Radar Data DART: Implicit Doppler Tomography for Radar Novel View Synthesis

Reference 6

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source=pdf_text observed=2026-08-02T17:25:27.703600Z digest=sha256:2ccdac921767506e4e79fafc175901f406a9fd8a55f1fb83050321bd6761b2db

Observation 8e67ffa1-a3b7-40ff-9663-bec56e028871 · outbound

This paper cites RF4D: Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes.

Neural Surface and Reflectance Modelling from 3D Radar Data RF4D: Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes

Reference 7

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Observation 02ab095a-5560-41c5-a38e-4d207dbea3e7 · outbound

This paper cites GeoRF: Geomet- ric Constrained RaDAR Fields.

Neural Surface and Reflectance Modelling from 3D Radar Data GeoRF: Geomet- ric Constrained RaDAR Fields

Reference 8

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Observation d3cc09fd-2eae-4339-8e2e-bc29dded897c · outbound

This paper cites NeuRadar: Neural Radi- ance Fields for Automotive Radar Point Clouds.

Neural Surface and Reflectance Modelling from 3D Radar Data NeuRadar: Neural Radi- ance Fields for Automotive Radar Point Clouds

Reference 9

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source=pdf_text observed=2026-08-02T17:25:27.712686Z digest=sha256:3fffc6516dfc818bfb1c94d7b08d71dada63da3d6107494b1b305af85568a20a

Observation 964e21d0-3795-434c-a463-fba5253a5b0e · outbound

This paper cites NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction.

Neural Surface and Reflectance Modelling from 3D Radar Data NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

Reference 10

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source=pdf_text observed=2026-08-02T17:25:27.715258Z digest=sha256:378e1df4042fab74b5010854bb4daaec8b9d5badf537428ee27e23eed4779cee

Observation 6bcd5f83-d047-425b-96fe-04e6c0b7d057 · outbound

This paper cites OctoMap: An efficient probabilistic 3D mapping framework based on octrees.

Neural Surface and Reflectance Modelling from 3D Radar Data OctoMap: An efficient probabilistic 3D mapping framework based on octrees

Reference 11

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Observation a3d03c46-766a-41fe-b749-c813d4c4a19a · outbound

This paper cites Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog.

Neural Surface and Reflectance Modelling from 3D Radar Data Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog

Reference 12

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Observation 314e91d6-dabc-4dd0-bbe7-085875ea699f · outbound

This paper cites Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environ- ments.

Neural Surface and Reflectance Modelling from 3D Radar Data Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environ- ments

Reference 13

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Observation b25b0875-a060-4ece-8bc1-c64bbbdb5bf7 · outbound

This paper cites Real-time Scalable Dense Surfel Mapping.

Neural Surface and Reflectance Modelling from 3D Radar Data Real-time Scalable Dense Surfel Mapping

Reference 14

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Observation 42264289-adb1-415b-9348-fe7803ea60e2 · outbound

This paper cites On the shape of a set of points in the plane.

Neural Surface and Reflectance Modelling from 3D Radar Data On the shape of a set of points in the plane

Reference 15

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Observation b7f9aa90-1bfa-4d99-8aee-7570fedacd5b · outbound

This paper cites The ball-pivoting algorithm for surface reconstruction.

Neural Surface and Reflectance Modelling from 3D Radar Data The ball-pivoting algorithm for surface reconstruction

Reference 16

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source=pdf_text observed=2026-08-02T17:25:27.731766Z digest=sha256:cd5c92430935a5bc63c99a774ea5421299eafa03fb79a5205718e9bdf67351e0

Observation b8e3e8b3-51c7-4348-bab1-4aad437e81ed · outbound

This paper cites Poisson Surface Reconstruction.

Neural Surface and Reflectance Modelling from 3D Radar Data Poisson Surface Reconstruction

Reference 17

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source=pdf_text observed=2026-08-02T17:25:27.734442Z digest=sha256:a4e5db89edc1c8f704352f910a838363340815c8217b563b5b09ae137413731e

Observation 2ffe5ebe-7496-4b41-ac17-580d6584acf0 · outbound

This paper cites Poisson Surface Reconstruction for LiDAR Odometry and Mapping.

Neural Surface and Reflectance Modelling from 3D Radar Data Poisson Surface Reconstruction for LiDAR Odometry and Mapping

Reference 18

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source=pdf_text observed=2026-08-02T17:25:27.737015Z digest=sha256:3983f375c87e0b040004a434a278ea35da2b1c96e96801c200101ff74b7cb04b

Observation 3066cded-51e3-4524-bf3f-1a907c579d88 · outbound

This paper cites Online 3D Reconstruction Based On Lidar Point Cloud.

Neural Surface and Reflectance Modelling from 3D Radar Data Online 3D Reconstruction Based On Lidar Point Cloud

Reference 19

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Observation 428b3002-8bfa-4b14-a328-085f814bdfe9 · outbound

This paper cites V oxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MA V planning.

Neural Surface and Reflectance Modelling from 3D Radar Data V oxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MA V planning

Reference 20

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Observation 6feb9f66-9d14-4550-8d13-991c6918db2a · outbound

This paper cites VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data.

Neural Surface and Reflectance Modelling from 3D Radar Data VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data

Reference 21

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source=pdf_text observed=2026-08-02T17:25:27.745072Z digest=sha256:aa5d05f842dd2098df2f86a3d8cdf3006c4bf48c5cb91db9adcbd7bfade8a85e

Observation fa862462-26bb-40d1-a8a9-c6cb61b331a2 · outbound

This paper cites Occupancy Networks: Learning 3D Reconstruction in Function Space.

Neural Surface and Reflectance Modelling from 3D Radar Data Occupancy Networks: Learning 3D Reconstruction in Function Space

Reference 22

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Observation 36840f30-c755-4161-9e1f-2cb22f9ccdfb · outbound

This paper cites UNISURF: Uni- fying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction.

Neural Surface and Reflectance Modelling from 3D Radar Data UNISURF: Uni- fying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

Reference 23

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source=pdf_text observed=2026-08-02T17:25:27.750198Z digest=sha256:2cfa53f74141227177b04d7de7744e4396d4b863109cee8dae4978e98c6c5f5a

Observation 8d90631b-b967-4dd5-a8b2-0557df34c242 · outbound

This paper cites Efficient Implicit Neural Reconstruction Using LiDAR.

Neural Surface and Reflectance Modelling from 3D Radar Data Efficient Implicit Neural Reconstruction Using LiDAR

Reference 24

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Observation 3ebdf0c9-4ed3-4fcd-95fb-60da353c25cd · outbound

This paper cites V olume Rendering of Neural Implicit Surfaces.

Neural Surface and Reflectance Modelling from 3D Radar Data V olume Rendering of Neural Implicit Surfaces

Reference 25

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source=pdf_text observed=2026-08-02T17:25:27.755714Z digest=sha256:b9c5b8c57211ca162f8edf018f53d2f134efd4ff47f28b2f7ee1fdbd47016104

Observation 5ed45f74-b2ab-428a-bc91-27514ba1742e · outbound

This paper cites NeuS: Learning Neural Implicit Surfaces by V olume Rendering for Multi-view Reconstruction.

Neural Surface and Reflectance Modelling from 3D Radar Data NeuS: Learning Neural Implicit Surfaces by V olume Rendering for Multi-view Reconstruction

Reference 26

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source=pdf_text observed=2026-08-02T17:25:27.758248Z digest=sha256:d3262bacbb7fe7874b7a90973bd054c8a78117206d2a07cc830c2f288deb0063

Observation b826b978-386b-4a58-8061-19caaf9c2700 · outbound

This paper cites Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging.

Neural Surface and Reflectance Modelling from 3D Radar Data Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging

Reference 27

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source=pdf_text observed=2026-08-02T17:25:27.760688Z digest=sha256:9d7d7ab780db0dad20b6929e026823903b39e95a8d7cd74bf53213e42eeb6ac2

Observation 952d74e8-561e-4c6e-816d-215b5b0312af · outbound

This paper cites Neural Im- plicit Surface Reconstruction using Imaging Sonar.

Neural Surface and Reflectance Modelling from 3D Radar Data Neural Im- plicit Surface Reconstruction using Imaging Sonar

Reference 28

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source=pdf_text observed=2026-08-02T17:25:27.763242Z digest=sha256:8dce5d6fd094a77c55e0f7cae2581b5b67b7a29a418fa1244bdf4d41480b54a6

Observation d5344f79-7da3-498b-8413-3f9f61d8bf21 · outbound

This paper cites Bathy- metric Surveying With Imaging Sonar Using Neural V olume Rendering.

Neural Surface and Reflectance Modelling from 3D Radar Data Bathy- metric Surveying With Imaging Sonar Using Neural V olume Rendering

Reference 29

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Observation 10679839-52f7-4acd-a765-470602d9357f · outbound

This paper cites SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation.

Neural Surface and Reflectance Modelling from 3D Radar Data SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

Reference 30

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source=pdf_text observed=2026-08-02T17:25:27.768303Z digest=sha256:91d9d9ef88ecb4171ea854e3d84e251445e9518c7edc3379e83817d3b5693c09

Observation 89c75c12-02ea-4f44-a548-98a01c5be15e · outbound

This paper cites Neural Implicit Repre- sentations for 3D Synthetic Aperture Radar Imaging.

Neural Surface and Reflectance Modelling from 3D Radar Data Neural Implicit Repre- sentations for 3D Synthetic Aperture Radar Imaging

Reference 31

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Observation c7ae4be7-3cb7-4424-9101-0ace27384fb3 · outbound

This paper cites NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images.

Neural Surface and Reflectance Modelling from 3D Radar Data NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images

Reference 32

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source=pdf_text observed=2026-08-02T17:25:27.773746Z digest=sha256:ab58d62fe2fb3630c6b036f379ea8f10b06cf62c966373624da3e9edc78c8947

Observation d49121d3-7a01-4947-9fc1-a085fda496c1 · outbound

This paper cites DehazeNeRF: Multi-image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields.

Neural Surface and Reflectance Modelling from 3D Radar Data DehazeNeRF: Multi-image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields

Reference 33

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source=pdf_text observed=2026-08-02T17:25:27.776391Z digest=sha256:f07035607482ab6eb3ff1e921e7b0374645bdc9da13421eaad10e49440e6b349

Observation bb43f7a8-7384-49a5-8a6c-82ea0fa0ed40 · outbound

This paper cites Marching cubes: A high resolution 3D surface construction algorithm.

Neural Surface and Reflectance Modelling from 3D Radar Data Marching cubes: A high resolution 3D surface construction algorithm

Reference 34

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Observation e67c9478-8e13-4d76-9512-f47d60c8aa51 · outbound

This paper cites SNAIL radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM.

Neural Surface and Reflectance Modelling from 3D Radar Data SNAIL radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM

Reference 35

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Observation 4a3f5f2c-aeef-4738-a271-fd6879c2116e · outbound

This paper cites Open3D: A Modern Library for 3D Data Processing.

Neural Surface and Reflectance Modelling from 3D Radar Data Open3D: A Modern Library for 3D Data Processing

Reference 36

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