Paper Citation Record · LEDGER

Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.04691.

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

pith.paper-citation-record.v1
2305.04691 v1

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 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:54:24.327291Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:26:38.544916Z

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 cfd23933-5876-48c9-9cb0-de581a0ca20a · inbound

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

Foundational Models for 3D Point Clouds: A Survey and Outlook Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T22:54:24.327291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb8d37b-4d14-4765-96b0-ff64a2583848 · inbound

Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting cites this paper.

Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:06:57.857443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09b7ce61-5385-443e-93c9-a44772952a87 · inbound

Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning cites this paper.

Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T22:26:20.519733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1df6bd2d-f8d4-4bfd-9620-3d819e7ded50 · inbound

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations cites this paper.

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:32:29.977440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b70df00-f2d6-4d7a-a5b1-27cc903c92bd · inbound

Multi-modal Multi-task Pre-training for Improved Point Cloud Understanding cites this paper.

Multi-modal Multi-task Pre-training for Improved Point Cloud Understanding Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:52.710758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eb698ac-7bf4-4e94-85e2-30afbe705f82 · 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 Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:26:38.549759Z

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.