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Paper Citation Record · LEDGER

Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.00132.

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

pith.paper-citation-record.v1
2204.00132 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:00:45.085562Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:27:32.073894Z

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 9df4e42a-9adc-4dde-a74d-8481eb950a2f · inbound

Few-Shot Learning in Video and 3D Object Detection: A Survey cites this paper.

Few-Shot Learning in Video and 3D Object Detection: A Survey Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation

Reference 174

Resolution
unresolved
no resolver link, observed 2026-08-06T15:00:45.085562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:00:45.085562Z digest=sha256:46ad2480fbe9e496f560463c8a5a75cfddf0742f70fbadac5b88184a7b9788ec

Observation 45e217c7-ad77-4dce-94b0-19efa0d2eece · inbound

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset cites this paper.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:27:32.122091Z

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.

source=pdf_text observed=2026-08-05T18:27:30.802608Z digest=sha256:a12ffedbd284456ccf1b76ae69425d96fbcf5f9d14933011b8e1370ce20818e9