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Paper Citation Record · LEDGER

SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.16279.

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

pith.paper-citation-record.v1
2406.16279 v3

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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:04:16.056285Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:21:01.542272Z

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 deb0120b-816c-4605-92f1-9f210e17cef5 · inbound

4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic Segmentation cites this paper.

4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic Segmentation SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-10T22:04:16.056285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:04:16.056285Z digest=sha256:d87269225806c9efdd7b31e3018ddcc06c9321ec2ea5df8437dcc54aaab24933

Observation 888c6fe1-5470-4434-b05b-57bd02bfdd91 · inbound

HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing cites this paper.

HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:21:01.544507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-10T15:32:26.359465Z digest=sha256:7e90036c3192a7e23fe5041a2817570032fe92aa773192bb1d4027eed08fd611