Paper Citation Record · LEDGER

Gaussian Belief Propagation Network for Depth Completion

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2601.21291.

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

pith.paper-citation-record.v1
2601.21291 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:04:35.697412Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T13:52:01.152288Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T13:55:53.207763Z

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy0
unresolved23
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 44cdcb82-72b8-4d41-8780-4a7cc498c6fb · outbound

This paper cites Gaussian Belief Propagation: Theory and Aplication.

Gaussian Belief Propagation Network for Depth Completion Gaussian Belief Propagation: Theory and Aplication

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.625895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98ffc202-cd11-4b1b-b3a7-21c100481f74 · outbound

This paper cites Loopy Belief Propagation for Approximate Inference: An Empirical Study.

Gaussian Belief Propagation Network for Depth Completion Loopy Belief Propagation for Approximate Inference: An Empirical Study

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.640441Z

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Observation 4eb3f785-6591-4e1b-a54a-edebbd829cee · outbound

This paper cites For training, we take the data proposed by Ma & Karaman (2018), utilizing 50,000 frames sampled from 249 scenes.

Gaussian Belief Propagation Network for Depth Completion For training, we take the data proposed by Ma & Karaman (2018), utilizing 50,000 frames sampled from 249 scenes

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.650525Z

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Observation b331b863-3d18-41df-8c46-c1efb91aa7a6 · outbound

This paper cites Models are trained from scratch for approximately 300,000 iterations.

Gaussian Belief Propagation Network for Depth Completion Models are trained from scratch for approximately 300,000 iterations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.653371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa64a88c-2b23-445c-8fc5-b64cdada30f1 · outbound

This paper cites an unresolved cited work.

Gaussian Belief Propagation Network for Depth Completion Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.656147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9905b512-1c17-4619-8cc8-8a4699be6b8c · outbound

This paper cites For clearer visualization, sparse depth points are enlarged.

Gaussian Belief Propagation Network for Depth Completion For clearer visualization, sparse depth points are enlarged

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.662251Z

Source-reported events for the cited work

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Observation 9b96877a-a518-4f36-a8ef-65062bab59bc · outbound

This paper cites an unresolved cited work.

Gaussian Belief Propagation Network for Depth Completion Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.668017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8333b42e-076e-45ed-bf7d-cf33e32d2a40 · outbound

This paper cites (2021) 735.81 217.15 2.20 0.98 0.106 0.015 – – NLSPN (Park et al.,.

Gaussian Belief Propagation Network for Depth Completion (2021) 735.81 217.15 2.20 0.98 0.106 0.015 – – NLSPN (Park et al.,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.670796Z

Source-reported events for the cited work

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Observation 86689f5f-245d-4801-8715-87d7aa4aa41e · outbound

This paper cites (2022) 712.66 203.25 2.08 0.90 0.090 0.013 – – DySPN (Lin et al.,.

Gaussian Belief Propagation Network for Depth Completion (2022) 712.66 203.25 2.08 0.90 0.090 0.013 – – DySPN (Lin et al.,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.673437Z

Source-reported events for the cited work

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Observation ac8e4919-3892-42aa-8173-2f22227dcd06 · outbound

This paper cites an unresolved cited work.

Gaussian Belief Propagation Network for Depth Completion Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.676324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14e50d20-82f3-4813-bb12-bb61c931a545 · outbound

This paper cites an unresolved cited work.

Gaussian Belief Propagation Network for Depth Completion Unresolved cited work

Reference 18

Resolution: malformed identifier; no resolver link, observed 2026-08-03T07:04:35.679015Z

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Observation db8845a0-4b5a-40b5-bea6-77d7ef8bb8fb · outbound

This paper cites For a thorough evaluation, given a sparsity level, each test image is sampled 100 times with different random seeds to generate the input sparse depth 24 Preprint map.

Gaussian Belief Propagation Network for Depth Completion For a thorough evaluation, given a sparsity level, each test image is sampled 100 times with different random seeds to generate the input sparse depth 24 Preprint map

Reference 19

Resolution: malformed identifier; no resolver link, observed 2026-08-03T07:04:35.681516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5d7756b-2b5e-47da-a3e0-661209f7c263 · outbound

This paper cites an unresolved cited work.

Gaussian Belief Propagation Network for Depth Completion Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.684175Z

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Unavailable: canonical work link unavailable.

Observation 206eaeab-e6d5-40f5-af8a-ddc3b4785e3c · outbound

This paper cites Under extremely sparse input, 20 and 50 points, GBPN-1 achieves the lowest RMSE, significantly outperforming other methods.

Gaussian Belief Propagation Network for Depth Completion Under extremely sparse input, 20 and 50 points, GBPN-1 achieves the lowest RMSE, significantly outperforming other methods

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.687243Z

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Observation 8a58ef09-305b-4acd-82bf-e2de7ecd55ac · outbound

This paper cites Similar phenomenon has also been observed by (Zuo & Deng, 2024), and we attribute this to the lack of robustness to changes in input sparsity.

Gaussian Belief Propagation Network for Depth Completion Similar phenomenon has also been observed by (Zuo & Deng, 2024), and we attribute this to the lack of robustness to changes in input sparsity

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.689755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9aaaddbe-0404-4634-900e-9157ddd2d306 · outbound

This paper cites In addition, these methods were trained exclusively with 500 valid points.

Gaussian Belief Propagation Network for Depth Completion In addition, these methods were trained exclusively with 500 valid points

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.692311Z

Source-reported events for the cited work

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Observation afc7a96b-5e7e-4853-a6ce-4d9d249fa3ac · outbound

This paper cites For each sparsity level, the first row is input image, the second row is sparse map.

Gaussian Belief Propagation Network for Depth Completion For each sparsity level, the first row is input image, the second row is sparse map

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.695038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c100ec4-d365-4dae-8847-db947f7b728e · outbound

This paper cites Here, GuideNet demonstrates the fastest inference speed, while CFormer and OGNI-DC are notably slower.

Gaussian Belief Propagation Network for Depth Completion Here, GuideNet demonstrates the fastest inference speed, while CFormer and OGNI-DC are notably slower

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.697412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f67dbe9-75e3-4d2f-a887-d4dbfae9c777 · outbound

This paper cites Unsupervised depth completion from visual inertial odometry.IEEE Robotics and Automation Letters, 5(2):1899–1906,.

Gaussian Belief Propagation Network for Depth Completion Unsupervised depth completion from visual inertial odometry.IEEE Robotics and Automation Letters, 5(2):1899–1906,

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.643783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e16cf6e-1082-4729-8b82-1acf2376e964 · outbound

This paper cites Dilated Neighborhood Attention Transformer.

Gaussian Belief Propagation Network for Depth Completion Dilated Neighborhood Attention Transformer

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.633327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21ab937c-5689-4b48-bf1f-37154fa86d45 · outbound

This paper cites FutureMapping 2: Gaussian Belief Propagation for Spatial AI.

Gaussian Belief Propagation Network for Depth Completion FutureMapping 2: Gaussian Belief Propagation for Spatial AI

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.629921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d693891-b004-4fd7-977e-ea08e7dc5140 · outbound

This paper cites FractalNet: Ultra-Deep Neural Networks without Residuals.

Gaussian Belief Propagation Network for Depth Completion FractalNet: Ultra-Deep Neural Networks without Residuals

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.636926Z

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Unavailable: canonical work link unavailable.

Observation 301731c0-f5d4-4bb0-aadc-a8b70955c23e · outbound

This paper cites 23 Preprint Table 5:Performance on KITTI and NYUv2 datasets.For the KITTI dataset, results are evaluated by the KITTI testing server.

Gaussian Belief Propagation Network for Depth Completion 23 Preprint Table 5:Performance on KITTI and NYUv2 datasets.For the KITTI dataset, results are evaluated by the KITTI testing server

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.665187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97793e29-cba5-471d-a869-434ef80beb8c · outbound

This paper cites an unresolved cited work.

Gaussian Belief Propagation Network for Depth Completion Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.659318Z

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Unavailable: canonical work link unavailable.

Observation dec4a457-7993-4ee4-a426-c8a166505992 · outbound

This paper cites an unresolved cited work.

Gaussian Belief Propagation Network for Depth Completion Unresolved cited work

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:35.647025Z

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Pith citing papers

Observation d8faea71-501d-4290-8ee5-82580c52b2e7 · inbound

Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion cites this paper.

Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion Gaussian Belief Propagation Network for Depth Completion

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-07-01T02:16:56.703294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.