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Paper Citation Record · LEDGER

Towards Foundation Models for 3D Vision: How Close Are We?

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.10799.

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

pith.paper-citation-record.v1
2410.10799 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:38:58.191736Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T08:40:33.031888Z

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 4959c0de-f4e5-47d7-9320-bf7ab0845816 · inbound

Multi-SpatialMLLM: Multi-Frame Spatial Understanding with Multi-Modal Large Language Models cites this paper.

Multi-SpatialMLLM: Multi-Frame Spatial Understanding with Multi-Modal Large Language Models Towards Foundation Models for 3D Vision: How Close Are We?

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:40:33.034803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-25T08:38:02.944230Z digest=sha256:c4f7fb17e21bf1602b56b53cc0fecc8e7f8afc50ac7871fb4d9a1a31fe80b45a

Observation cdce873d-4773-4311-9e8b-469dbc993f02 · inbound

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? cites this paper.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Towards Foundation Models for 3D Vision: How Close Are We?

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T15:38:58.191736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:38:58.191736Z digest=sha256:ea882642b2a2c4a10e045111eaf9cd53892fadcfe0d058427269fe2160248685

Observation 8b2e77df-b49b-4458-a739-3b81af41b5f4 · inbound

Zero-shot World Models Are Developmentally Efficient Learners cites this paper.

Zero-shot World Models Are Developmentally Efficient Learners Towards Foundation Models for 3D Vision: How Close Are We?

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:16:08.350704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:33:39.342672Z digest=sha256:a87daeac57f82544b4b5ec928f180dac7565f969bf4c2c5f88f10ce7a20b032e