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Paper Citation Record · LEDGER

Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2206.09900.

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

pith.paper-citation-record.v1
2206.09900 v7

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 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:05:27.266517Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:05:41.629655Z

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 d3440214-7ec2-433a-9352-7229afe9559a · inbound

VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving cites this paper.

VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-12T15:05:27.266517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:05:27.266517Z digest=sha256:5d2977f711fac38e145d29693d69d13756f3e3574c9615cb0801edf7a003b399

Observation 763ddcc8-90f7-434c-8329-72c51434dae1 · inbound

Point Cloud Understanding via Attention-Driven Contrastive Learning cites this paper.

Point Cloud Understanding via Attention-Driven Contrastive Learning Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-12T15:02:29.414364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:02:29.414364Z digest=sha256:02df941752931965e511ae0fe5f41965ec72d4396bb4edd4725931965213b74c

Observation 360d4095-f021-4ad5-a5cc-9e161d3d37fc · inbound

Mitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning cites this paper.

Mitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-01T10:05:41.631159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-01T05:47:40.376900Z digest=sha256:464d0911d3c55e04b9f51c96f1055be080076818efdb9bc035b91d8710bb91ca