Paper Citation Record · LEDGER

NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2111.13260.

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

pith.paper-citation-record.v1
2111.13260 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:24:25.171231Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T03:01:28.526787Z

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 c6e47013-1e8f-497e-b376-3f4e4324ca60 · inbound

Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos cites this paper.

Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:25.171231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b966e73c-0ea8-4a00-9d72-34963efa7f92 · inbound

NeurNCD: Novel Class Discovery via Implicit Neural Representation cites this paper.

NeurNCD: Novel Class Discovery via Implicit Neural Representation NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:55.161336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 541a4523-b7c2-48db-b4d0-38908720c85b · inbound

A Neural Representation Framework with LLM-Driven Spatial Reasoning for Open-Vocabulary 3D Visual Grounding cites this paper.

A Neural Representation Framework with LLM-Driven Spatial Reasoning for Open-Vocabulary 3D Visual Grounding NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T19:02:27.694249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3a26131-8457-43f7-92ed-5852464ebb1a · inbound

OpenTrack3D: Towards Accurate and Generalizable Open-Vocabulary 3D Instance Segmentation cites this paper.

OpenTrack3D: Towards Accurate and Generalizable Open-Vocabulary 3D Instance Segmentation NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-17T03:01:28.529531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f6f4c6e1-10d5-43e5-a235-ddd9b18e4ff1 · inbound

3AM: 3egment Anything with Geometric Consistency in Videos cites this paper.

3AM: 3egment Anything with Geometric Consistency in Videos NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T14:21:01.636488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 51d91c42-ac93-40a1-b337-23dbd61f5ac5 · inbound

First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction cites this paper.

First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:30.768614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.