Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2306.09347.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T20:04:16.317672Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T13:45:45.326413Z
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 687da854-86fc-41cc-b8e8-edaf09f7e915 · inbound
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning cites this paper.
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:04:16.317672Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5cdccce3-e9bb-4a17-98d4-4d75906cf8cf · inbound
LeAP: Consistent multi-domain 3D labeling using Foundation Models cites this paper.
LeAP: Consistent multi-domain 3D labeling using Foundation Models Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T00:21:15.695219Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f1ae7f73-f0f4-4d43-a0f6-52795c1555eb · inbound
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception cites this paper.
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:46:39.924381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 024ed1f3-7f68-40db-b075-5040ab56725d · inbound
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception cites this paper.
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Reference 78
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T13:45:45.328020Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.