Paper Citation Record · LEDGER

PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2504.11451.

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

pith.paper-citation-record.v1
2504.11451 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:21:12.388477Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:33:15.444457Z

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 99261906-a1f2-45d5-bbc1-cd580fe67288 · inbound

Efficient Part-level 3D Object Generation via Dual Volume Packing cites this paper.

Efficient Part-level 3D Object Generation via Dual Volume Packing PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:40:15.995959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e62f681-a5e4-499a-a0b1-bc5f15b8a147 · inbound

Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale cites this paper.

Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T01:05:18.303515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcf31e2b-aac3-4f6a-b4b6-f377552149b5 · inbound

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention cites this paper.

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:21:12.388477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aea857e2-268e-49e6-8c85-a411abc09811 · inbound

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation cites this paper.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T18:48:59.042706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf32f2e1-20e3-4cc2-870e-4f9d461bedfe · inbound

PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding cites this paper.

PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T06:36:45.615680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9336b51b-18b4-4b80-a2df-d64d076fee61 · inbound

Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation cites this paper.

Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:26:10.592180Z

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.

Observation d9f25185-f8fa-4923-aec9-f903c2490679 · inbound

Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence cites this paper.

Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:33:15.445999Z

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.