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Paper Citation Record · LEDGER

PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.06403.

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

pith.paper-citation-record.v1
2403.06403 v5

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:31:45.273734Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T22:54:25.133188Z

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 de5242b0-acdc-4cef-a619-73830c68c6a3 · inbound

Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation cites this paper.

Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T21:31:45.273734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:31:45.273734Z digest=sha256:4c3a217e47b3dfec006f5241ce781a1a5123579c2c943c20c268703c12612166

Observation 4ce8bb7a-cc4e-4b69-9c3f-ee4b15f5b756 · inbound

Foundational Models for 3D Point Clouds: A Survey and Outlook cites this paper.

Foundational Models for 3D Point Clouds: A Survey and Outlook PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models

Reference 161

Resolution
verified exact
local_arxiv, observed 2026-08-09T22:54:25.137260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-09T22:54:24.877034Z digest=sha256:b40bc51e810b93bc674eacf31ac1753aa222d1d3f6fc87c2d2992cc0b12542ae