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Paper Citation Record · LEDGER

Multimodal 3D Object Detection on Unseen Domains

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.11764.

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

pith.paper-citation-record.v1
2404.11764 v1

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:35:29.153803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:16:26.501397Z

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 94e9cbcc-c0d3-44c4-8533-d3ab4ad731c2 · inbound

CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion cites this paper.

CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion Multimodal 3D Object Detection on Unseen Domains

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:36:15.491363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T04:56:08.669141Z digest=sha256:b11583cfdd30b0fbf0674880303e8f7f610c5e12f4da0af713e055e8705628d7

Observation 9f1f9d4d-15b6-4c29-a1e7-dc0d0cf596e3 · inbound

MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving cites this paper.

MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving Multimodal 3D Object Detection on Unseen Domains

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:16:26.509539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T03:35:29.153803Z digest=sha256:c7d4bf95d6437be02f9295712dc86fc8ef25e3924a5f49a04c734eeed06eca96