Paper Citation Record · LEDGER

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model

As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2502.00315.

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

pith.paper-citation-record.v1
2502.00315 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:31:31.102375Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

31 of 31 outbound references displayed

verified exact0
verified fuzzy12
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bed1f976-57f5-4e54-b0ac-5fa3b0dc6524 · outbound

This paper cites End-to-end object detection with transformers,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model End-to-end object detection with transformers,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:30.976969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b732942-871c-4476-9b64-9e301774c6f3 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model DINOv2: Learning Robust Visual Features without Supervision

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:30.981969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dea1a48-eefa-4d19-b17e-e1c2589b192b · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:30.986976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10917a14-f113-4d39-8933-0fb5fc917e3e · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:30.991683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c65bb62f-ce24-4738-9e2c-9eea9cbc2ceb · outbound

This paper cites M3d-rpn: Monocular 3d region proposal network for object detection,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model M3d-rpn: Monocular 3d region proposal network for object detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.388988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 82eda0e6-428a-4d70-80f4-b1df7c381aa2 · outbound

This paper cites Monoground: Detecting monocular 3d objects from the ground,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Monoground: Detecting monocular 3d objects from the ground,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.377598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aca53789-8481-48d0-a066-c83314f7b3f8 · outbound

This paper cites Learning depth-guided convolutions for monocular 3d object detection,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Learning depth-guided convolutions for monocular 3d object detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.365700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3437aa88-650c-4116-a8a7-5bfcaaa352bc · outbound

This paper cites Depth-conditioned dynamic message propagation for monocular 3d object detection,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Depth-conditioned dynamic message propagation for monocular 3d object detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.353816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 272c1ee1-e289-4de9-9871-462c06f09fc2 · outbound

This paper cites Monodtr: Monocular 3d object detection with depth-aware transformer,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Monodtr: Monocular 3d object detection with depth-aware transformer,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.341878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cb1e1a59-bfe5-44f2-8dd1-584760d1066f · outbound

This paper cites Attention is all you need,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Attention is all you need,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.017762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77a3cd74-ae80-4166-803f-cc0f54496e6b · outbound

This paper cites Monodetr: Depth-guided transformer for monocular 3d object detection,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Monodetr: Depth-guided transformer for monocular 3d object detection,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.022022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cccc173d-03da-4794-b5da-b3dbae5b03ad · outbound

This paper cites Categorical depth distribution network for monocular 3d object detection,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Categorical depth distribution network for monocular 3d object detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.317711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c343dde-a08f-4c9e-bd91-026d1e33701b · outbound

This paper cites DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.030457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 485d70ca-785e-49a2-a547-5c337b3d542d · outbound

This paper cites Imagenet large scale visual recognition challenge,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Imagenet large scale visual recognition challenge,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.034721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0f668ea-58f2-4097-884b-10dea2846927 · outbound

This paper cites Deep residual learning for image recognition,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Deep residual learning for image recognition,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.038576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e494b68f-819b-4af7-ad74-37ea4500769f · outbound

This paper cites Densely connected convolutional networks,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Densely connected convolutional networks,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.042213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7373efe4-d0b2-49f4-b0b6-6fc2da84a9f7 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Learning transferable visual models from natural language supervision,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.286749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 00e4e725-5c41-4f57-acd6-6dfb80815a91 · outbound

This paper cites Emerging properties in self-supervised vision trans- formers,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Emerging properties in self-supervised vision trans- formers,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.049816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8ea01d6-1c1b-427e-9743-6cf90377bc62 · outbound

This paper cites Segment anything,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Segment anything,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.053705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ec96f7f-899f-4b2d-8c78-e528335d04b6 · outbound

This paper cites Vision transformers for dense prediction,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Vision transformers for dense prediction,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.057770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ce98e72-ffb9-4259-af55-a93dabc4c0cb · outbound

This paper cites Depth Anything V2.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Depth Anything V2

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.061883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46d61109-cab3-4bd7-b7a5-45cd8980bac3 · outbound

This paper cites Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.256730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 76b3c28e-44fa-441e-b38c-9d8a9f0033f4 · outbound

This paper cites Monoc- ular relative depth perception with web stereo data supervision,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Monoc- ular relative depth perception with web stereo data supervision,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.244627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d80ccc5-d6ff-4bfa-b84d-75203e023cb6 · outbound

This paper cites Focal Loss for Dense Object Detection.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Focal Loss for Dense Object Detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.073657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86c4e57b-422d-46fa-bc87-2c4159f47c8a · outbound

This paper cites The hungarian method for the assignment problem,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model The hungarian method for the assignment problem,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.077756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 122b9143-22c5-4bd7-a714-162ce1bef421 · outbound

This paper cites 3d object proposals for accurate object class detection,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model 3d object proposals for accurate object class detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.227176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6ef8a58f-db10-4a32-bf9f-7b1f90652640 · outbound

This paper cites Monocular 3d object detection for autonomous driving,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Monocular 3d object detection for autonomous driving,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.215461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 968360c5-676b-4953-9585-43426a7e3494 · outbound

This paper cites Pointpillars: Fast encoders for object detection from point clouds,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Pointpillars: Fast encoders for object detection from point clouds,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.089008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e345235-89ab-4ed0-b448-2aa6445d5a8a · outbound

This paper cites Decoupled Weight Decay Regularization.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Decoupled Weight Decay Regularization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.093261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32d08526-4a72-4a6e-bd85-ae4fcbe81627 · outbound

This paper cites Monocd: Monocular 3d object detection with complementary depths,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Monocd: Monocular 3d object detection with complementary depths,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:31.197896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de0cbbef-af7e-4975-8d14-1a566af763f6 · outbound

This paper cites Exploring plain vision transformer backbones for object detection,.

MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model Exploring plain vision transformer backbones for object detection,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:31.102375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.