Paper Citation Record · LEDGER

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation

As of 16 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2605.10251.

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

pith.paper-citation-record.v1
2605.10251 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:20:11.827500Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact1
verified fuzzy16
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8279ba47-b849-4f49-893b-cedfeab7eaf8 · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep network.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Depth map prediction from a single image using a multi-scale deep network

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.619593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea13dd6b-e0a6-46b5-bf90-3a1824f15ba0 · outbound

This paper cites Deeper depth prediction with fully convolutional residual networks.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Deeper depth prediction with fully convolutional residual networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.631753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 14ba58ab-cb1c-4770-a586-40c2011ab904 · outbound

This paper cites Deep ordinal regression network for monoc- ular depth estimation.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Deep ordinal regression network for monoc- ular depth estimation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.658353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8bbb57a9-5733-4ff7-8c97-b206e741ae0b · outbound

This paper cites From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:21:18.760084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cbb2645c-5dc2-4c29-9df3-62784dc134f6 · outbound

This paper cites AdaBins: Depth estimation using adaptive bins.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation AdaBins: Depth estimation using adaptive bins

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.653514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 57f136a6-0de2-4ad9-8f8c-1ff4806cbfb0 · outbound

This paper cites Vi- sion transformers for dense prediction.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Vi- sion transformers for dense prediction

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.675504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d6dffa8e-349a-4a8c-a0d2-3f65f15b3288 · outbound

This paper cites DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:21:18.763204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3c088a3f-aa2b-437b-ae6c-0dbe79e3c348 · outbound

This paper cites Graph- based context reasoning for scene understanding.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Graph- based context reasoning for scene understanding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.635976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c4dc5782-2b45-4237-8b5c-8023d3d6abfe · outbound

This paper cites Induc- tive representation learning on large graphs.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Induc- tive representation learning on large graphs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.640455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9da3d90a-c580-4dba-be84-94b35ae0b639 · outbound

This paper cites Indoor segmentation and support inference from RGBD images.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Indoor segmentation and support inference from RGBD images

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.624074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f5d5d50d-15bf-42ce-ae61-b88d0653b96c · outbound

This paper cites WHU: A large- scale dataset for stereo depth estimation in aerial scenarios.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation WHU: A large- scale dataset for stereo depth estimation in aerial scenarios

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.644696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3dc9f400-92dd-4e7e-ae26-021eb8a8732c · outbound

This paper cites A multi-view stereo bench- mark with high-resolution images and multi-camera videos.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation A multi-view stereo bench- mark with high-resolution images and multi-camera videos

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.649164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 377855f9-06f7-45dc-a562-bdfb9e7ba5b0 · outbound

This paper cites Mid-Air: A multi-modal dataset for ex- tremely low altitude drone flights.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Mid-Air: A multi-modal dataset for ex- tremely low altitude drone flights

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.680146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ff82dfde-1bb2-4214-a9db-4e655e0b8bd9 · outbound

This paper cites U-Net: Convolutional networks for biomedical image seg- mentation.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation U-Net: Convolutional networks for biomedical image seg- mentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.666840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8c8ee4ed-78ca-4756-b776-5ede1c9d2c30 · outbound

This paper cites Deep residual learning for image recognition.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Deep residual learning for image recognition

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.611861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 429e7153-82bf-4983-9e1b-fef6225f6b80 · outbound

This paper cites Squeeze-and-excitation networks.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation Squeeze-and-excitation networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.628428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a1ad6006-368a-4687-b850-f6afc59608f5 · outbound

This paper cites CBAM: Convolutional block attention module.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation CBAM: Convolutional block attention module

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.662461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2fea5fd5-de4e-4a25-ad82-790c887e93d0 · outbound

This paper cites What uncertainties do we needinBayesiandeeplearningforcomputervision?.

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation What uncertainties do we needinBayesiandeeplearningforcomputervision?

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:11:48.671011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.