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Paper Citation Record · LEDGER

InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.05335.

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

pith.paper-citation-record.v1
2401.05335 v1

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 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T00:10:15.141684Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T00:12:17.664552Z

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 20ef2160-1bc9-49db-8b4f-f8fb3eb8ade1 · inbound

Depth Anything V2 cites this paper.

Depth Anything V2 InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-13T14:56:34.162958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-13T14:56:33.945280Z digest=sha256:bebc749bc7b9c052ad79692ff2a98bee69ff38272c2d2fca31cfbf744ce1d824

Observation 331fdd03-d4e6-46c6-b10b-539f81925f0b · inbound

Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers cites this paper.

Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-23T00:12:17.667586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-23T00:10:15.141684Z digest=sha256:3bd9fae95252653d6ac941ecdc3f46a0313a94e3e81ca4162174f2ecfd75406e