Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2311.01989.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T15:27:12.775842Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T22:54:01.262672Z
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 6b8895c2-eadd-4562-a4e7-be441b55833d · inbound
On Efficient Variants of Segment Anything Model: A Survey cites this paper.
On Efficient Variants of Segment Anything Model: A Survey Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T19:43:23.259155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation eddf898f-f172-45a2-bf00-488478fa4af3 · inbound
Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation cites this paper.
Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:27:12.775842Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c583f3fc-eba9-41dd-9861-9653408c5e33 · inbound
Pantheon360: Taming Digital Twin Generation via 3D-Aware 360{\deg} Video Diffusion cites this paper.
Pantheon360: Taming Digital Twin Generation via 3D-Aware 360{\deg} Video Diffusion Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:54:01.264304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.