Paper Citation Record · LEDGER

Adversarial View-Consistent Learning for Monocular Depth Estimation

As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:1908.01301.

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

pith.paper-citation-record.v1
1908.01301 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:23:14.132042Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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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

42 of 42 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0cfb8d53-70b5-4b59-b9f1-06a4cec8eeda · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Adversarial View-Consistent Learning for Monocular Depth Estimation Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 1

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Observation bc63d27e-6681-401a-a23c-b8a2f258df04 · outbound

This paper cites Depth from a single image by harmonizing overcomplete local network predictions.

Adversarial View-Consistent Learning for Monocular Depth Estimation Depth from a single image by harmonizing overcomplete local network predictions

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.757918Z

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Observation f871e86a-3f36-44d2-8e1a-c211ad7bb538 · outbound

This paper cites Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture.

Adversarial View-Consistent Learning for Monocular Depth Estimation Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.738791Z

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Observation f5c81753-ba3c-4613-ab9f-4dfa35f53f76 · outbound

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

Adversarial View-Consistent Learning for Monocular Depth Estimation Depth map prediction from a single image using a multi-scale deep network

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.720195Z

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Observation d41271e2-c08d-444e-9771-b99f7b96e293 · outbound

This paper cites Deep ordinal regression network for monocular depth estimation.

Adversarial View-Consistent Learning for Monocular Depth Estimation Deep ordinal regression network for monocular depth estimation

Reference 5

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Observation f749173c-94c9-44ab-8ec5-d67f6ceaee62 · outbound

This paper cites Multi-view stereo: A tutorial.

Adversarial View-Consistent Learning for Monocular Depth Estimation Multi-view stereo: A tutorial

Reference 6

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Observation e22d590f-76a6-47d5-b413-9f077c8e32b4 · outbound

This paper cites Massively parallel multi- view stereopsis by surface normal diffusion.

Adversarial View-Consistent Learning for Monocular Depth Estimation Massively parallel multi- view stereopsis by surface normal diffusion

Reference 7

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Observation afd9d764-a619-423f-ae37-758391bb2935 · outbound

This paper cites Generative adversarial nets.

Adversarial View-Consistent Learning for Monocular Depth Estimation Generative adversarial nets

Reference 8

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Observation 5735d84f-3cfb-42ac-9202-277b35a2e4aa · outbound

This paper cites Deep residual learning for image recognition.

Adversarial View-Consistent Learning for Monocular Depth Estimation Deep residual learning for image recognition

Reference 9

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This paper cites Recovering surface layout from an image.

Adversarial View-Consistent Learning for Monocular Depth Estimation Recovering surface layout from an image

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.649730Z

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Observation 6fd24a5b-d0b5-4acd-9241-382f58f8b417 · outbound

This paper cites Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis.

Adversarial View-Consistent Learning for Monocular Depth Estimation Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.634297Z

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Observation 1bbdbe2f-07a7-431f-89c7-d04ff1393ee7 · outbound

This paper cites Spatial transformer net- works.

Adversarial View-Consistent Learning for Monocular Depth Estimation Spatial transformer net- works

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.621243Z

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Observation 6aeca3db-cff4-4cff-8cbd-36355df27364 · outbound

This paper cites Surfacenet: An end-to-end 3d neural network for multiview stereopsis.

Adversarial View-Consistent Learning for Monocular Depth Estimation Surfacenet: An end-to-end 3d neural network for multiview stereopsis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.606558Z

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Observation a15aa538-e351-4f75-b8a4-9ab92bca59c0 · outbound

This paper cites Karsch, C.

Adversarial View-Consistent Learning for Monocular Depth Estimation Karsch, C

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.592868Z

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Observation a514219e-5df3-496f-b9e4-3b758e620947 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In nips.

Adversarial View-Consistent Learning for Monocular Depth Estimation What uncertainties do we need in bayesian deep learning for computer vision? In nips

Reference 15

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Observation dda81993-1413-472a-9f12-e86ea456380d · outbound

This paper cites A theory of shape by space carving.

Adversarial View-Consistent Learning for Monocular Depth Estimation A theory of shape by space carving

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.561517Z

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Observation a9b902ff-f71b-49c7-ad52-1bd29d9d2e75 · outbound

This paper cites Pulling things out of perspective.

Adversarial View-Consistent Learning for Monocular Depth Estimation Pulling things out of perspective

Reference 17

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Observation a28e66e8-1888-4a8d-9c55-4b54d1c62540 · outbound

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

Adversarial View-Consistent Learning for Monocular Depth Estimation Deeper depth prediction with fully convolutional residual networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.538063Z

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Observation 4b9d7df7-6dba-4cb4-abf0-888a1a0e49f0 · outbound

This paper cites Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs.

Adversarial View-Consistent Learning for Monocular Depth Estimation Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs

Reference 19

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Observation 268856fd-dc11-4cf9-b3a6-553e36e61d51 · outbound

This paper cites A two-streamed network for estimating ﬁne- scaled depth maps from single rgb images.

Adversarial View-Consistent Learning for Monocular Depth Estimation A two-streamed network for estimating ﬁne- scaled depth maps from single rgb images

Reference 21

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Adversarial View-Consistent Learning for Monocular Depth Estimation Unresolved cited work

Reference 22

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Observation c0c33516-c4eb-429d-850e-2ef8872f9e4a · outbound

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Adversarial View-Consistent Learning for Monocular Depth Estimation Unresolved cited work

Reference 23

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Adversarial View-Consistent Learning for Monocular Depth Estimation Unresolved cited work

Reference 24

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Observation ca2fe135-df29-4541-a7f6-5e60ec7a015f · outbound

This paper cites Learning depth from single monocular images using deep convolutional neural ﬁelds.IEEE transactions on pattern analysis and machine intelligence, 38(10):2024–2039, 2016.

Adversarial View-Consistent Learning for Monocular Depth Estimation Learning depth from single monocular images using deep convolutional neural ﬁelds.IEEE transactions on pattern analysis and machine intelligence, 38(10):2024–2039, 2016

Reference 26

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This paper cites Discrete-continuous depth esti- mation from a single image.

Adversarial View-Consistent Learning for Monocular Depth Estimation Discrete-continuous depth esti- mation from a single image

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.444156Z

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Observation 4e5d6a7e-3556-4fc9-94c1-998a9f538bae · outbound

This paper cites Deep metric learn- ing with bier: Boosting independent embeddings robustly.

Adversarial View-Consistent Learning for Monocular Depth Estimation Deep metric learn- ing with bier: Boosting independent embeddings robustly

Reference 28

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Observation 7aca2ddc-7e18-4195-a4ef-3ae165de64e3 · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Adversarial View-Consistent Learning for Monocular Depth Estimation Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 29

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This paper cites Monocular depth estimation using neural regres- sion forest.

Adversarial View-Consistent Learning for Monocular Depth Estimation Monocular depth estimation using neural regres- sion forest

Reference 30

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Observation 0dfe464d-5a28-4b2f-a906-2e8346a1a837 · outbound

This paper cites Saxena, M.

Adversarial View-Consistent Learning for Monocular Depth Estimation Saxena, M

Reference 31

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Observation 800e8fb8-c16c-489a-aa6d-939ddfc8a648 · outbound

This paper cites Learning depth from single monocular images.

Adversarial View-Consistent Learning for Monocular Depth Estimation Learning depth from single monocular images

Reference 32

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Observation 80eaff04-ca7b-4e0e-81c7-b1f76b32d74b · outbound

This paper cites Photorealistic scene reconstruction by voxel col- oring.

Adversarial View-Consistent Learning for Monocular Depth Estimation Photorealistic scene reconstruction by voxel col- oring

Reference 33

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This paper cites Indoor segmenta- tion and support inference from rgbd images.

Adversarial View-Consistent Learning for Monocular Depth Estimation Indoor segmenta- tion and support inference from rgbd images

Reference 34

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Observation 82a861c2-c42e-4e66-86c2-95dd1fe6aace · outbound

This paper cites Efﬁcient large-scale multi-view stereo for ultra high-resolution image sets.

Adversarial View-Consistent Learning for Monocular Depth Estimation Efﬁcient large-scale multi-view stereo for ultra high-resolution image sets

Reference 35

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Observation 0abf1a12-d2dd-4939-9c45-de99d28eb1e3 · outbound

This paper cites Disentangled representation learning gan for pose-invariant face recognition.

Adversarial View-Consistent Learning for Monocular Depth Estimation Disentangled representation learning gan for pose-invariant face recognition

Reference 36

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Adversarial View-Consistent Learning for Monocular Depth Estimation Unresolved cited work

Reference 37

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Observation bd032492-b3b6-40e6-b254-38aa49f01186 · outbound

This paper cites A-fast-rcnn: Hard positive generation via adversary for object detection.

Adversarial View-Consistent Learning for Monocular Depth Estimation A-fast-rcnn: Hard positive generation via adversary for object detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.306011Z

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Observation f4f7b833-6eb5-4ff4-82ae-d1d9abfb018c · outbound

This paper cites Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks.

Adversarial View-Consistent Learning for Monocular Depth Estimation Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.289629Z

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Observation c109977d-60ad-4b55-a82f-2a0047e3a609 · outbound

This paper cites Multi-scale continuous crfs as sequential deep networks for monocular depth estimation.

Adversarial View-Consistent Learning for Monocular Depth Estimation Multi-scale continuous crfs as sequential deep networks for monocular depth estimation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.271166Z

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Observation b0e3a166-9f2d-40ce-a592-315e9111e794 · outbound

This paper cites Mvsnet: Depth inference for unstructured multi-view stereo.

Adversarial View-Consistent Learning for Monocular Depth Estimation Mvsnet: Depth inference for unstructured multi-view stereo

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.256603Z

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Observation 6cd3e240-1812-4f40-9a1c-c8e4d06cdc24 · outbound

This paper cites Unsupervised learning of depth and ego-motion from video.

Adversarial View-Consistent Learning for Monocular Depth Estimation Unsupervised learning of depth and ego-motion from video

Reference 42

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Observation a23633d5-5171-4894-a4bb-f11a74e55c53 · outbound

This paper cites Generative visual manipulation on the natural image manifold.

Adversarial View-Consistent Learning for Monocular Depth Estimation Generative visual manipulation on the natural image manifold

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.230839Z

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Observation 9514ba2e-0935-4a93-ae15-7be41fbf3465 · outbound

This paper cites Indoor scene structure analysis for single image depth estimation.

Adversarial View-Consistent Learning for Monocular Depth Estimation Indoor scene structure analysis for single image depth estimation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:23:14.214082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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