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Paper Citation Record · LEDGER

Digging Into Self-Supervised Monocular Depth Estimation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1806.01260.

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

pith.paper-citation-record.v1
1806.01260 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:03:10.129567Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T19:21:09.774047Z

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 885ad6b1-15c6-4b89-8e8c-2f266b4ab5e8 · inbound

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks cites this paper.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Digging Into Self-Supervised Monocular Depth Estimation

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-25T19:21:09.777656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-25T19:18:49.945978Z digest=sha256:378f43a9a5c5993b78f229df600e6bba0e6d87192ab1c4fb87957c9eab8dd3ca

Observation 62ecf9bc-591f-479e-bb2d-0d60479bda0e · inbound

StixelNExT++: Lightweight Monocular Scene Segmentation and Representation for Collective Perception cites this paper.

StixelNExT++: Lightweight Monocular Scene Segmentation and Representation for Collective Perception Digging Into Self-Supervised Monocular Depth Estimation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T19:03:10.129567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:03:10.129567Z digest=sha256:f4c6e769046db0e68a352dc3645ea762835d11e0d8c210d1c4e444b06692463b

Observation 70c2d526-6c33-4aa4-a68b-ffc2383153e9 · inbound

Edit in 2D, Verify in 3D: Reinforcement Learning for Multi-view Consistent Scene Editing cites this paper.

Edit in 2D, Verify in 3D: Reinforcement Learning for Multi-view Consistent Scene Editing Digging Into Self-Supervised Monocular Depth Estimation

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-02T19:14:06.574604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:14:06.574604Z digest=sha256:b4a5377286295b723749b59bf46a7a87988c95143eed794a9f63f269fe66fba6

Observation fb07309c-1250-4829-be05-87d440c56762 · inbound

Zero-shot World Models Are Developmentally Efficient Learners cites this paper.

Zero-shot World Models Are Developmentally Efficient Learners Digging Into Self-Supervised Monocular Depth Estimation

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:21:00.175011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:33:39.342672Z digest=sha256:8b7833ac11547e326b3418f648d14d0a93f08f5480e1a5597089e59a293dd703