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Paper Citation Record · LEDGER

Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.05399.

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

pith.paper-citation-record.v1
2107.05399 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:33:51.413379Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T21:51:17.690031Z

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 5fa2a170-054c-48aa-b31c-336e628e2422 · inbound

Point Cloud Segmentation of Agricultural Vehicles using 3D Gaussian Splatting cites this paper.

Point Cloud Segmentation of Agricultural Vehicles using 3D Gaussian Splatting Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T10:33:51.413379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:33:51.413379Z digest=sha256:1c3bdf227a1a447a60e6f0ee28045a8b7c3102e46056a798c9c46dfd230680a0

Observation 50de758f-7092-4941-887f-4512b4f1a813 · inbound

SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis cites this paper.

SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:51:17.691782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T21:49:35.263131Z digest=sha256:f5361d70549d209e160bb9f1816b91b36fb1fd85cc5b03f490b13935ff7a8a1c