Paper Citation Record · LEDGER

PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2007.10985.

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

pith.paper-citation-record.v1
2007.10985 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:00:14.577556Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T15:27:12.964278Z

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 675af4aa-bab6-454c-8f8d-151138d5df21 · inbound

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

LeAP: Consistent multi-domain 3D labeling using Foundation Models PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.650253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f5db7a6-5565-4cd9-ad57-a3acb79e32fa · inbound

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D cites this paper.

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T12:00:14.577556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04a6fd8d-66ad-43f0-aea2-7fe6976462d8 · inbound

A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning cites this paper.

A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T12:22:12.956126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7b82c8c-2344-4a70-801b-f8d199b8e552 · inbound

Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation cites this paper.

Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:27:12.969737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e45d22f-ecf9-4504-89fa-324e782c5ef7 · inbound

Industrial3D: A Water-Treatment TLS Point Cloud Dataset and Cross-Paradigm Benchmark for MEP Scene Understanding cites this paper.

Industrial3D: A Water-Treatment TLS Point Cloud Dataset and Cross-Paradigm Benchmark for MEP Scene Understanding PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 38

Resolution: malformed identifier; no resolver link, observed 2026-07-13T16:14:28.988636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df79069c-8c4f-47cb-8e79-3c5a53f7042b · inbound

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density cites this paper.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.468121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.