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Paper Citation Record · LEDGER

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.10726.

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

pith.paper-citation-record.v1
2312.10726 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:10:16.218975Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T12:47:49.425992Z

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 5bbc99fa-d16f-4670-8c07-739437aa0a88 · inbound

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning cites this paper.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:16.218975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:16.218975Z digest=sha256:a43124adce73b98b68dd8463bf081b6467da2bd835cf3155ac6b7e22b499dce2

Observation 2595c51c-a99b-4e3d-bf4b-73029dff76cc · inbound

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views cites this paper.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Reference 77

Resolution
verified exact
local_arxiv, observed 2026-08-05T12:47:49.431681Z

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.

source=pdf_text observed=2026-08-05T12:47:49.343900Z digest=sha256:67075f1e43e7c601011b6e3b6df8902ead0f9f5210568940812cc0441efd918e