Pith. sign in

Paper Citation Record · LEDGER

A Survey of Robust 3D Object Detection Methods in Point Clouds

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2204.00106.

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

pith.paper-citation-record.v1
2204.00106 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:57:15.869063Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:27:32.186719Z

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 b843be81-545b-4e78-9b33-386eec60ae9a · inbound

A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles cites this paper.

A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles A Survey of Robust 3D Object Detection Methods in Point Clouds

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-12T15:57:15.869063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:57:15.869063Z digest=sha256:1d95accec964f7b5cbed55b3fc3b4c44b1aa2bf47c6e89e3525b136ed4e48563

Observation 60768fdc-163f-465c-bee8-14d6d528e0e1 · inbound

Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors cites this paper.

Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors A Survey of Robust 3D Object Detection Methods in Point Clouds

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-09T19:11:16.861349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T19:11:16.861349Z digest=sha256:14b3f198b477365c8660d6f2f999b98f002ab4c1aa8ef48a9b81697aa476ec42

Observation f74dcc17-2aa6-4d81-967e-d5b08cc1e17d · inbound

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset cites this paper.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset A Survey of Robust 3D Object Detection Methods in Point Clouds

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:27:32.276727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T18:27:30.741254Z digest=sha256:b4041daa358f5ad7d943e8ab53fbe48a370459fc81c7a32b3f8e803ad7088c8c