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Paper Citation Record · LEDGER

Diffusion Model for Dense Matching

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.19094.

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

pith.paper-citation-record.v1
2305.19094 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:39:27.364440Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:29:29.004918Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0a3cc0d3-66f8-4460-b93e-cca54871611e · inbound

Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching cites this paper.

Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching Diffusion Model for Dense Matching

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T17:39:27.364440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:39:27.364440Z digest=sha256:57475b329cbd51600224262bea2ab281acfe95bce3e7a3f1d0e954dca5ae9b08

Observation 5115abae-1d43-48d8-afef-f4a653258789 · inbound

TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking cites this paper.

TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking Diffusion Model for Dense Matching

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:29:29.007884Z

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.

source=pdf_text observed=2026-05-14T21:28:12.151547Z digest=sha256:ac577627123ee6de285ee0dc34496f5c63f46047785a2fccc21386de11c21708