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Paper Citation Record · LEDGER

Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2208.12573.

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

pith.paper-citation-record.v1
2208.12573 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:58:54.122481Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:25:42.002822Z

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 40746e11-c09a-4657-ab75-0406fb4f1f87 · inbound

Point Cloud Compression and Objective Quality Assessment: A Survey cites this paper.

Point Cloud Compression and Objective Quality Assessment: A Survey Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T21:58:54.122481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:58:54.122481Z digest=sha256:565153ed8c6dc6aa4c3d27de4d57285378791d99e512a4864682c8ea4b586b36

Observation 2954a62f-efaf-4f57-8be5-07ddc455d261 · inbound

Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation cites this paper.

Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-01T10:25:42.004623Z

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=2026-07-01T05:30:04.016376Z digest=sha256:f8375a8e1294336e7e8362f3a596386b5f4ee855c2d7352f39bd9daefab4e2f0