Paper Citation Record · LEDGER

Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2308.09718.

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

pith.paper-citation-record.v1
2308.09718 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:52:14.958546Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:00:07.711948Z

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 1e765161-49db-4cfa-b061-16ce0c0d2cd3 · inbound

Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation cites this paper.

Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-09T11:52:14.958546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 221cb6fc-8f8b-4fe7-ac7c-6252c5a51cbd · inbound

LeAP: Consistent multi-domain 3D labeling using Foundation Models cites this paper.

LeAP: Consistent multi-domain 3D labeling using Foundation Models Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.499494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46257954-cf81-446b-9fa5-6294fbe71a32 · inbound

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems cites this paper.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:27.139764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55fd5b7c-8753-443c-8644-fc2b9b3d5ace · inbound

Heterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation cites this paper.

Heterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:00:07.714554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.