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Paper Citation Record · LEDGER

Towards Training-Free Underwater 3D Object Detection from Sonar Point Clouds: A Comparison of Traditional and Deep Learning Approaches

As of 21 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 0 inbound Pith citation observations for arXiv:2508.18293.

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

pith.paper-citation-record.v1
2508.18293 v1

Coverage vector

measured 2 of 2 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:24:20.790058Z

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

2 of 2 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24134239-0377-46ba-a86e-2791562701c4 · outbound

This paper cites localization,.

Towards Training-Free Underwater 3D Object Detection from Sonar Point Clouds: A Comparison of Traditional and Deep Learning Approaches localization,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T17:24:20.836822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T17:24:20.785993Z digest=sha256:a407bdfdc0ad2a02c8dcf54aab329aee429a11a9fbc4f3a7d8a9a95e55864b72

Observation 8313f3b0-57c0-44cd-aa74-00dfe1dfa322 · outbound

This paper cites Numerical study of high-dimensional covariance estimation and localization for data assimilation.

Towards Training-Free Underwater 3D Object Detection from Sonar Point Clouds: A Comparison of Traditional and Deep Learning Approaches Numerical study of high-dimensional covariance estimation and localization for data assimilation

Reference 2003

Resolution
verified exact
local_arxiv, observed 2026-08-05T17:24:20.825636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T17:24:20.790058Z digest=sha256:3ab0e92a8819feac7ac8fea44be85879ae02b5143ba8fc4c0c620df200fad9e9

Pith citing papers

No inbound Pith citation observations are available.