Pith. sign in

Paper Citation Record · LEDGER

Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.07548.

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

pith.paper-citation-record.v1
2204.07548 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:58:19.440431Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:09:30.254068Z

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 8b6c5831-4f57-4aa1-81a7-539037f40a5d · inbound

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation cites this paper.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-15T23:58:19.440431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:58:19.440431Z digest=sha256:73be061796eae91983dc670e6d9b34b32e36cd754e4d54d88c53a6ede5c86b58

Observation 52324e7b-c961-4779-9506-79c2c53f3e24 · inbound

TimeProVe: Propose, then Verify for Efficient Long Video Temporal Reasoning in Activities of Daily Living cites this paper.

TimeProVe: Propose, then Verify for Efficient Long Video Temporal Reasoning in Activities of Daily Living Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation

Reference 99

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:09:30.256033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-26T18:22:13.147215Z digest=sha256:23c880e6ed1bea4c85a6c95864011b52991fa9b297fe0e124a564c6bae8c3600