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Paper Citation Record · LEDGER

DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2412.05268.

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

pith.paper-citation-record.v1
2412.05268 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:51:05.402554Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T17:40:01.069980Z

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 31347ab5-89e4-4054-9245-a85a0314e0e4 · inbound

ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models cites this paper.

ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T23:51:05.402554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:05.402554Z digest=sha256:053143cd8614c3a024d93cb12dd4921e3640d039297fa0feca30f55ee72bc051

Observation 3e5669af-b5f3-478b-858e-9fcf8a1d8a7a · inbound

Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations cites this paper.

Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 138

Resolution
verified exact
arxiv_id, observed 2026-05-19T06:37:07.333645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T06:36:13.144868Z digest=sha256:ceefe748affd7216fb4a340106fe1e6acedf85d57c3edcea71281b4e080560e2

Observation 6cd68ccc-db0e-407b-82f7-26af3da40de1 · inbound

Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks cites this paper.

Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-18T00:55:35.231966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T00:53:24.323059Z digest=sha256:63940910b6897d80052d0d720ce8f4172cbd4faaaccb0226594c18e4c554040f

Observation 7cb25555-bcec-4973-b0cf-5b91531cb6c7 · inbound

IGen: Scalable Data Generation for Robot Learning from Open-World Images cites this paper.

IGen: Scalable Data Generation for Robot Learning from Open-World Images DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-05-17T02:58:54.791384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T02:58:36.214948Z digest=sha256:37007b25017307a8500830737f9ec5cb02825730b0e6ee06588c070c05c95041

Observation 2dc9fe79-affb-4e1e-94ae-5529aefebe7a · inbound

AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence cites this paper.

AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:00:59.161695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T16:21:40.381114Z digest=sha256:25c058cd0b8248f1aa37c73f4daa4fb5907566f82d99922dd9b9dff745a00a96

Observation e434bea8-e760-462c-b2aa-6de002330e4a · inbound

AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence cites this paper.

AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-12T22:32:10.188246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T22:32:10.188246Z digest=sha256:74b64ec00fa1269c3aacc10d8c1511897ddece6125370b4983ac2037c027de2e

Observation 402a4108-1848-474f-bacd-e3e64d1c6b4c · inbound

SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals cites this paper.

SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-05-20T11:53:15.145489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T11:48:55.446684Z digest=sha256:9356878311553cc8b46c0f0bba82cbe9ddce24c783954ca24740b2c843f691b1

Observation 09802c85-84ac-4eec-89ad-d934f6feca77 · inbound

GRAFT: Graph-Based Affordance Transfer via Part Correspondence cites this paper.

GRAFT: Graph-Based Affordance Transfer via Part Correspondence DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-07-04T17:40:01.071620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-25T23:27:15.576980Z digest=sha256:a0903b85358f597369592873d9515b1e154d539b311cd872b506f31b32d4d79f

Observation ec17ba3c-0a22-43e8-9e8c-1e1873380023 · inbound

MeshFM: 2D Features Are All You Need for 3D Shape Understanding cites this paper.

MeshFM: 2D Features Are All You Need for 3D Shape Understanding DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-01T05:06:27.122910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T05:06:27.122910Z digest=sha256:939e4cf01236525a5f6c2835ee5ffa78877ce60c63857272d74020901c6d87d1