Paper Citation Record · LEDGER

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

As of 11 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 7 inbound Pith citation observations for arXiv:2501.02576.

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

pith.paper-citation-record.v1
2501.02576 v2

Coverage vector

measured 82 of 82 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T06:08:14.988386Z

measured 89 of 89 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:55:16.333093Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-30T06:24:19.433510Z

Reference resolution

82 of 82 outbound references displayed

verified exact22
verified fuzzy60
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 33d5ff37-87c7-42d6-9892-149405b74c8c · outbound

This paper cites Mgnet: Monocular geo- metric scene understanding for autonomous driving.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Mgnet: Monocular geo- metric scene understanding for autonomous driving

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.656024Z

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Observation 4c3c9d4c-8c00-4f0f-a8b5-99012b04cbd8 · outbound

This paper cites Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.680273Z

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Observation 6643c14d-3862-45d0-8bcf-d4cc3bf8bc65 · outbound

This paper cites Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.894254Z

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Observation 6a6a9c2d-f498-4a5a-af2d-6a02756d9976 · outbound

This paper cites Ro- bodepth: Robust out-of-distribution depth estimation under corruptions.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Ro- bodepth: Robust out-of-distribution depth estimation under corruptions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.616512Z

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Observation e391a15f-6297-4403-a87d-b3af119f7676 · outbound

This paper cites Consistent video depth estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Consistent video depth estimation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.623102Z

Source-reported events for the cited work

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Observation db4f505f-a51b-4c62-a010-b997a13f8cb4 · outbound

This paper cites Low power depth estimation of rigid objects for time-of-flight imaging.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Low power depth estimation of rigid objects for time-of-flight imaging

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.608924Z

Source-reported events for the cited work

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Observation d57a87a1-4be5-45ca-a2c2-5d253d63c1c8 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Adding conditional control to text-to-image diffusion models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.613082Z

Source-reported events for the cited work

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Observation a66b4a9c-0bee-4862-8005-8a4888ddb882 · outbound

This paper cites Structure and content-guided video synthesis with diffusion models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Structure and content-guided video synthesis with diffusion models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.724655Z

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Observation d6276a9a-72b4-4e63-bab3-c26bf474e794 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Depth anything: Unleashing the power of large-scale unlabeled data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.635924Z

Source-reported events for the cited work

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Observation 82e3b092-64be-409d-b30c-008b837f09f6 · outbound

This paper cites Vision transformers for dense prediction.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Vision transformers for dense prediction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.619942Z

Source-reported events for the cited work

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Observation 0e6d4939-b1be-4dbe-8611-e1e39b126081 · outbound

This paper cites ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.883697Z

Source-reported events for the cited work

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Observation 9e8254e0-a9fc-46c7-8a73-bc0892966fec · outbound

This paper cites Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.688148Z

Source-reported events for the cited work

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Observation 2810502f-b1a8-45c9-a18e-81fe60612037 · outbound

This paper cites Towards robust monocular depth estimation: Mixing datasets for zero-shot cross- dataset transfer.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Towards robust monocular depth estimation: Mixing datasets for zero-shot cross- dataset transfer

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.794529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23f54bc8-e7fb-48fa-a0b0-c78d5d6320a0 · outbound

This paper cites DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.972365Z

Source-reported events for the cited work

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Observation 337f7b94-f6e1-451d-a9ec-13a774b912f6 · outbound

This paper cites Scaledepth: Decomposing metric depth estimation into scale prediction and relative depth estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Scaledepth: Decomposing metric depth estimation into scale prediction and relative depth estimation

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.942797Z

Source-reported events for the cited work

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Observation e59bdf7f-e2e6-4741-bdc4-f17571929061 · outbound

This paper cites Repurposing diffusion-based image generators for monoc- ular depth estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Repurposing diffusion-based image generators for monoc- ular depth estimation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.814154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 128e152d-0bb0-441f-ad6a-87d55320adbd · outbound

This paper cites Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.591580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 513963d9-45c2-4f43-b386-8ddc4cd43874 · outbound

This paper cites Depthfm: Fast monocular depth estimation with flow matching.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Depthfm: Fast monocular depth estimation with flow matching

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.595173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f965914a-7220-4267-9906-f7e0efeecab8 · outbound

This paper cites What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.903996Z

Source-reported events for the cited work

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Observation b3e44472-b897-4049-af84-874aee2344eb · outbound

This paper cites Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.878807Z

Source-reported events for the cited work

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Observation bc144933-3371-4a25-a149-6542581d9c1a · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation High- resolution image synthesis with latent diffusion models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.630044Z

Source-reported events for the cited work

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Observation d013d09b-bd4c-48e7-bcb1-a78a7c9f2f6b · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Scaling rectified flow transformers for high-resolution image synthesis

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.692010Z

Source-reported events for the cited work

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Observation f624d0ac-6587-4974-9af4-c58cf4168d7c · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Deep unsupervised learning using nonequilibrium thermodynamics

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.798702Z

Source-reported events for the cited work

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Observation 49a52153-ebf0-4f39-8963-e9ecf5c2c1c6 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.962957Z

Source-reported events for the cited work

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Observation 796b129d-7cc1-4d48-9067-01c7749edae6 · outbound

This paper cites Animate anyone: Consistent and controllable image-to-video synthesis for character animation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Animate anyone: Consistent and controllable image-to-video synthesis for character animation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.806560Z

Source-reported events for the cited work

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Observation 8dc778d6-17b3-49cd-88ed-9535b94b6d07 · outbound

This paper cites Smartbrush: Text and shape guided object inpainting with diffusion model.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Smartbrush: Text and shape guided object inpainting with diffusion model

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.817749Z

Source-reported events for the cited work

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Observation 17874781-283e-4f75-a17f-e496b54ad78e · outbound

This paper cites HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.958553Z

Source-reported events for the cited work

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Observation ec6d1277-dfca-4fa2-9e49-48a368d7b253 · outbound

This paper cites Srdiff: Single image super-resolution with diffusion probabilistic mod- els.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Srdiff: Single image super-resolution with diffusion probabilistic mod- els

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.787184Z

Source-reported events for the cited work

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Observation 0f28c937-1cf9-4cdb-9039-db2e7bec4d80 · outbound

This paper cites Exploiting diffusion prior for real-world image super-resolution.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Exploiting diffusion prior for real-world image super-resolution

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.790736Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9cdc35f0-d515-4a1b-b3bb-cb05d5443e59 · outbound

This paper cites Denoising diffusion probabilistic models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Denoising diffusion probabilistic models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.810146Z

Source-reported events for the cited work

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Observation b6b1f06a-e88c-4c53-ba76-73845ad9574f · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.937884Z

Source-reported events for the cited work

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Observation fda0fc8b-b9ed-4979-9289-d57c132881c4 · outbound

This paper cites On Fast Sampling of Diffusion Probabilistic Models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation On Fast Sampling of Diffusion Probabilistic Models

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.933600Z

Source-reported events for the cited work

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Observation 1c57d2c8-12c1-4bc4-9780-bb5b395601ea · outbound

This paper cites Noise Estimation for Generative Diffusion Models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Noise Estimation for Generative Diffusion Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.928358Z

Source-reported events for the cited work

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Observation 371538fc-82d9-4a57-b1e8-9129c4aa1324 · outbound

This paper cites Denoising Diffusion Implicit Models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Denoising Diffusion Implicit Models

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.922947Z

Source-reported events for the cited work

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Observation a945fbd8-daba-46cb-bf21-7985e5af32a6 · outbound

This paper cites Cascaded diffusion models for high fidelity image generation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Cascaded diffusion models for high fidelity image generation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.768819Z

Source-reported events for the cited work

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Observation 9c5fb490-faf9-4831-977c-ea06c50f02b3 · outbound

This paper cites Score-based generative modeling in latent space.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Score-based generative modeling in latent space

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.772565Z

Source-reported events for the cited work

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Observation decf5386-7498-4ed3-b365-4a9800ecc700 · outbound

This paper cites LAION-5b: An open large- scale dataset for training next generation image-text models.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation LAION-5b: An open large- scale dataset for training next generation image-text models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.756909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3dbc41fb-b3a1-4cee-942a-4a80c9eb4dfe · outbound

This paper cites A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.752945Z

Source-reported events for the cited work

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Observation b583ee54-e561-4508-9c18-8d6b508241f7 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Self-supervised learning from images with a joint-embedding predictive architecture

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.761181Z

Source-reported events for the cited work

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Observation 54b05ad3-536e-4e33-b12e-5ec8ff94e605 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.918311Z

Source-reported events for the cited work

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Observation e36eb734-06cc-4891-b98b-94670395b35a · outbound

This paper cites Vision meets robotics: The kitti dataset.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Vision meets robotics: The kitti dataset

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.743576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97910c5d-4660-458f-a7ed-b4c8d65e8e37 · outbound

This paper cites A naturalistic open source movie for optical flow evaluation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation A naturalistic open source movie for optical flow evaluation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.748473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e7e59477-db8c-4a4e-99d9-c1ce08b78db1 · outbound

This paper cites Sun rgb-d: A rgb-d scene under- standing benchmark suite.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Sun rgb-d: A rgb-d scene under- standing benchmark suite

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.765043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0786e208-9691-4e41-9dce-5fdc5a84cce8 · outbound

This paper cites Indoor segmen- tation and support inference from rgbd images.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Indoor segmen- tation and support inference from rgbd images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.776040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 55e8bd8c-5c29-4f41-9915-55fcb6661f83 · outbound

This paper cites Cornet: Context-based ordinal regression network for monocular depth estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Cornet: Context-based ordinal regression network for monocular depth estimation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.783142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27d189b7-12ef-47ea-be7b-4996e1baab35 · outbound

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

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Depth map prediction from a single image using a multi-scale deep network

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.728346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e0e0d33-f056-46b2-838f-0d446dd33f76 · outbound

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

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Deeper depth prediction with fully convolutional residual networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.736155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4171306d-ae52-49d6-b50b-bc71a4363970 · outbound

This paper cites Squeeze-and-excitation networks.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Squeeze-and-excitation networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.739935Z

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Observation 4524ce3f-f5f0-42c8-8afe-78dbc7ea5a74 · outbound

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

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Predicting depth, surface normals and se- mantic labels with a common multi-scale convolutional architecture

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.732334Z

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Observation bb3684e7-761c-4cf8-a43f-4ca9f00e7556 · outbound

This paper cites Web stereo video super- vision for depth prediction from dynamic scenes.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Web stereo video super- vision for depth prediction from dynamic scenes

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.779660Z

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Observation a23443a7-63fe-4f84-b8d4-647401d3d5cb · outbound

This paper cites Monocular depth estimation using laplacian pyramid-based depth residuals.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Monocular depth estimation using laplacian pyramid-based depth residuals

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.720770Z

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Observation 83614a87-f96f-491d-911b-78d0c04d0c97 · outbound

This paper cites NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.914041Z

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Observation 98718c4a-7781-4d2c-9cc5-b22e3569e139 · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Unleashing text-to-image diffusion models for visual perception

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.706986Z

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This paper cites Ecodepth: Effective conditioning of diffusion models for monocular depth estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Ecodepth: Effective conditioning of diffusion models for monocular depth estimation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.710665Z

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Observation b7bd3732-07e7-4ea7-ab90-38dbaa2b2e7d · outbound

This paper cites Estimating depth from monocular images as classification using deep fully convolutional residual networks.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Estimating depth from monocular images as classification using deep fully convolutional residual networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.703206Z

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This paper cites Monocular depth estimation with augmented ordinal depth relationships.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Monocular depth estimation with augmented ordinal depth relationships

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.714136Z

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Observation d8914cda-f777-4872-82bb-d8296e1d713f · outbound

This paper cites Adabins: Depth estimation using adaptive bins.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Adabins: Depth estimation using adaptive bins

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.717617Z

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Observation daa0fdef-913b-424c-8785-9b44f68c6644 · outbound

This paper cites BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.909011Z

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Observation d65c2f76-99bd-4f67-a7c1-4c576c6394ba · outbound

This paper cites Ha-bins: Hierarchical adaptive bins for robust monocular depth estimation across multiple datasets.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Ha-bins: Hierarchical adaptive bins for robust monocular depth estimation across multiple datasets

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.802842Z

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Observation 2ddb22f1-32b6-45ec-9798-443fcbb7a3c1 · outbound

This paper cites Enforcing geometric constraints of virtual normal for depth prediction.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Enforcing geometric constraints of virtual normal for depth prediction

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.639568Z

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This paper cites EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.874057Z

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Observation 6a6bee00-7853-4037-bbb6-1df3d1bfb99b · outbound

This paper cites Geonet: Geometric neural network for joint depth and surface normal estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Geonet: Geometric neural network for joint depth and surface normal estimation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.642515Z

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Observation 0e8a53a9-9728-47a8-8b06-1f03edd7cbb4 · outbound

This paper cites Plane2depth: Hierarchical adaptive plane guidance for monocular depth estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Plane2depth: Hierarchical adaptive plane guidance for monocular depth estimation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.645422Z

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Observation 189b5d2d-aa82-4797-814b-a51196369859 · outbound

This paper cites Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.649019Z

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Observation b87e3bfe-ac01-4363-8f3a-562e55f44e77 · outbound

This paper cites Towards scene understanding: Unsupervised monocular depth estimation with semantic- aware representation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Towards scene understanding: Unsupervised monocular depth estimation with semantic- aware representation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.664306Z

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Observation da509556-579c-4bb6-a1d7-df292ecc4451 · outbound

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

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation DINOv2: Learning Robust Visual Features without Supervision

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.899373Z

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This paper cites Diffusionedge: Diffusion probabilistic model for crisp edge detection.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Diffusionedge: Diffusion probabilistic model for crisp edge detection

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.598708Z

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Observation 01f13765-59c6-46b3-988c-07165b1dd961 · outbound

This paper cites Robust estimation of a location parameter.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Robust estimation of a location parameter

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.587851Z

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Observation 381fcea6-4077-4dec-a4fb-8626378b2cf7 · outbound

This paper cites Decoupled Weight Decay Regularization.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Decoupled Weight Decay Regularization

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.869279Z

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Observation aba0eb03-7627-4afd-ae36-ea519e11fc7a · outbound

This paper cites Learning to recover 3d scene shape from a single image.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Learning to recover 3d scene shape from a single image

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.602830Z

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Observation 0bc1f36d-3f3a-42e1-841e-e5f12bfb6b61 · outbound

This paper cites Hierarchical normalization for robust monocular depth estimation.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Hierarchical normalization for robust monocular depth estimation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.626503Z

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Observation feec0756-acff-448c-b32d-7d1fb76f325d · outbound

This paper cites Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.668621Z

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Observation 394cc980-193c-47e0-8573-1d1796b47ca5 · outbound

This paper cites Virtual KITTI 2.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Virtual KITTI 2

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.967375Z

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Observation 0a77b613-e7f7-4484-a7a8-7a96140115c1 · outbound

This paper cites Vision meets robotics: The kitti dataset.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Vision meets robotics: The kitti dataset

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.672569Z

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Observation cac6320e-46be-42d8-b1f7-b997f69d0156 · outbound

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

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Indoor segmentation and support inference from rgbd images

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.676292Z

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Observation 4784d002-0142-48b8-8c31-8adbef70975f · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.695532Z

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Observation b3a747ec-474a-47d7-beec-d42da02c3eda · outbound

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

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation A multi-view stereo benchmark with high- resolution images and multi-camera videos

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.660335Z

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Observation 36a69866-ef9c-4e65-a85c-dcf1dc2118c6 · outbound

This paper cites DIODE: A Dense Indoor and Outdoor DEpth Dataset.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation DIODE: A Dense Indoor and Outdoor DEpth Dataset

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:12:38.889151Z

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Observation ef60d5ba-1a6a-475f-b326-099938244a4c · outbound

This paper cites Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.947991Z

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Observation 87171d23-92cd-48d3-8775-1bb2c5c4311f · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Reproducible scaling laws for contrastive language-image learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.683894Z

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Observation 25f64a5a-fede-4f5e-a65b-f146e652f109 · outbound

This paper cites Multimodal autoregressive pre-training of large vision encoders.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Multimodal autoregressive pre-training of large vision encoders

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:12:39.699190Z

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Observation 0dd8bb21-f7a8-463f-b85f-c383a3444fbe · outbound

This paper cites Segment Anything.

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation Segment Anything

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-05-23T06:12:38.953432Z

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Pith citing papers

Observation 6b97218f-e004-45b6-8129-744c017d3822 · inbound

Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues cites this paper.

Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:55:16.333093Z

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Observation c134ff6c-3d90-4aea-87de-5a5a1702b26c · inbound

DiSA: Diffusion Step Annealing in Autoregressive Image Generation cites this paper.

DiSA: Diffusion Step Annealing in Autoregressive Image Generation DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Reference 32

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Observation 3b4eb97b-2738-4707-9bef-2aa811145078 · inbound

Depth Anything at Any Condition cites this paper.

Depth Anything at Any Condition DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T20:52:02.565213Z

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ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting cites this paper.

ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Reference 38

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Observation ed50065b-6cd1-413d-8bae-964c122b0d7e · inbound

The Midas Touch for Metric Depth cites this paper.

The Midas Touch for Metric Depth DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-13T01:37:03.305515Z

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Observation 779f4154-61ba-44f2-9d15-ce105f9af910 · inbound

MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction cites this paper.

MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T06:24:19.435221Z

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Observation 7de04795-5f14-4260-9f49-b4be02c5784a · inbound

DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV cites this paper.

DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T07:32:49.841013Z

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