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Paper Citation Record · LEDGER

Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.08715.

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

pith.paper-citation-record.v1
2312.08715 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:30:08.749471Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:00:07.499083Z

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 38612c4c-9512-461d-b48e-3fab13f59dd2 · inbound

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills cites this paper.

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-09T14:30:08.749471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T14:30:08.749471Z digest=sha256:922275d1e10537af55e1d267abc2ef5d2c31ab2ee37aff984a70cd1770e3aed3

Observation d7d336b6-8a98-4768-b4ac-31d729849ef8 · inbound

Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling cites this paper.

Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-16T05:57:24.534219Z

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=2026-05-16T05:55:19.254383Z digest=sha256:02d6f7e64529715d6198e7abe7a28002b879d816f0fe8148688e7a5172a38fe9

Observation 5619744e-f813-420d-8501-d0d38553f580 · inbound

Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling cites this paper.

Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-03T03:27:34.098579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:27:34.098579Z digest=sha256:1b199d4059eccf492b97c53cb56adbd99044f16cf4811bf0011b991b17895cdb

Observation 275708dd-5486-4a1a-abfd-7c2ab498826f · inbound

GenMatter: Perceiving Physical Objects with Generative Matter Models cites this paper.

GenMatter: Perceiving Physical Objects with Generative Matter Models Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:01:18.140784Z

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=2026-05-08T12:53:18.504365Z digest=sha256:35df5879b5c542b0f846d257f0b3fe76dcbaa49de3229318041e6913282114ad

Observation ba86faca-5f60-4ca6-8dc3-d9bd8d75fafb · inbound

GenMatter: Perceiving Physical Objects with Generative Matter Models cites this paper.

GenMatter: Perceiving Physical Objects with Generative Matter Models Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:00:07.501170Z

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=2026-07-04T19:55:05.116930Z digest=sha256:5f0304156d24eb73519d1b32f8a72f142ec5edec61541bacf95a1206e3da4294