The Fourth Monocular Depth Estimation Challenge | Paper Citation Record

Observation 1c46480b-4cc5-4330-9f3f-502fc805a285 · outbound

This paper cites The Southampton-York Natural Scenes (SYNS) dataset: Statis- tics of surface attitude.

The Fourth Monocular Depth Estimation Challenge The Southampton-York Natural Scenes (SYNS) dataset: Statis- tics of surface attitude

Reference 1

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Observation deae1f43-33b6-48ba-9848-8e8806308e92 · outbound

This paper cites Attention attention everywhere: Monocular depth prediction with skip atten- tion.

The Fourth Monocular Depth Estimation Challenge Attention attention everywhere: Monocular depth prediction with skip atten- tion

Reference 2

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Observation bad5d69a-8023-4fb9-9a79-106e5584bd68 · outbound

This paper cites Generative adversarial networks for unsupervised monocular depth prediction.

The Fourth Monocular Depth Estimation Challenge Generative adversarial networks for unsupervised monocular depth prediction

Reference 3

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Observation 9311074e-f22a-419c-af16-06333f4a9856 · outbound

This paper cites Real-time single image depth perception in the wild with handheld devices.

The Fourth Monocular Depth Estimation Challenge Real-time single image depth perception in the wild with handheld devices

Reference 4

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Observation feeb7d54-901a-40b6-9938-c12db3679860 · outbound

This paper cites Enhancing self-supervised monocular depth estimation with traditional visual odometry.

The Fourth Monocular Depth Estimation Challenge Enhancing self-supervised monocular depth estimation with traditional visual odometry

Reference 5

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Observation 81c796ce-1e10-4a25-8a8a-27c09ef13f86 · outbound

This paper cites Adabins: Depth estimation using adaptive bins.

The Fourth Monocular Depth Estimation Challenge Adabins: Depth estimation using adaptive bins

Reference 6

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Observation 94efd523-388e-454e-9198-7c17488b47b7 · outbound

This paper cites Localbins: Improving depth estimation by learning local distributions.

The Fourth Monocular Depth Estimation Challenge Localbins: Improving depth estimation by learning local distributions

Reference 7

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Observation 5484d999-f652-4423-8af7-801ccc45dfab · outbound

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

The Fourth Monocular Depth Estimation Challenge ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 8

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Observation 20732930-cb93-40c4-b27a-240b0c6fa3bb · outbound

This paper cites Unsu- pervised Scale-consistent Depth and Ego-motion Learning from Monocular Video.

The Fourth Monocular Depth Estimation Challenge Unsu- pervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

Reference 9

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Observation 9913d1ed-0330-4fec-a016-2fe3060bd1c7 · outbound

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

The Fourth Monocular Depth Estimation Challenge Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 10

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Observation 04261a63-ba0b-4533-911e-1467f6b03a60 · outbound

This paper cites Virtual KITTI 2.

The Fourth Monocular Depth Estimation Challenge Virtual KITTI 2

Reference 11

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Observation ab3548b4-3ccc-4ed6-b8c5-7fbc82deab78 · outbound

This paper cites Depth prediction without the sensors: Lever- aging structure for unsupervised learning from monocular videos.

The Fourth Monocular Depth Estimation Challenge Depth prediction without the sensors: Lever- aging structure for unsupervised learning from monocular videos

Reference 12

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Observation 3c617dd1-171c-4307-90be-8dbb2edd7ed2 · outbound

This paper cites Video Depth Anything: Consistent Depth Estimation for Super-Long Videos.

The Fourth Monocular Depth Estimation Challenge Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.416031Z

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Observation 166487a0-6216-4c62-9033-1d23ebdc986e · outbound

This paper cites Single- image depth perception in the wild.

The Fourth Monocular Depth Estimation Challenge Single- image depth perception in the wild

Reference 14

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Observation 7204b4c3-81e2-4961-a8b7-cd66d095ef76 · outbound

This paper cites OASIS: A large-scale dataset for single image 3d in the wild.

The Fourth Monocular Depth Estimation Challenge OASIS: A large-scale dataset for single image 3d in the wild

Reference 15

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Observation 648ef781-330b-4da8-aece-10405935cb1a · outbound

This paper cites Swin-depth: Us- ing transformers and multi-scale fusion for monocular-based depth estimation.

The Fourth Monocular Depth Estimation Challenge Swin-depth: Us- ing transformers and multi-scale fusion for monocular-based depth estimation

Reference 16

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Observation f6b67b22-195c-4361-a303-4474e869403e · outbound

This paper cites DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes.

The Fourth Monocular Depth Estimation Challenge DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes

Reference 17

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Observation 89e8ab0e-5699-4054-8d32-8b716f7b12ec · outbound

This paper cites Adaptive confidence thresholding for monocular depth es- timation.

The Fourth Monocular Depth Estimation Challenge Adaptive confidence thresholding for monocular depth es- timation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.433718Z

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Observation af9b6c30-f8f5-450f-9d12-475ebda56ec8 · outbound

This paper cites Energy-quality scalable monocular depth estimation on low- power cpus.

The Fourth Monocular Depth Estimation Challenge Energy-quality scalable monocular depth estimation on low- power cpus

Reference 19

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Observation aa2a4ce1-979e-42b0-96ff-0f818021b03e · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

The Fourth Monocular Depth Estimation Challenge The cityscapes dataset for semantic urban scene understanding

Reference 20

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Observation 1f6040db-f581-4633-8a9b-807c88ba361e · outbound

This paper cites Learn- ing depth estimation for transparent and mirror surfaces.

The Fourth Monocular Depth Estimation Challenge Learn- ing depth estimation for transparent and mirror surfaces

Reference 21

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Observation 4d1d2809-8d1d-4671-834f-75ae770dda83 · outbound

This paper cites Self-supervised object motion and depth estimation from video.

The Fourth Monocular Depth Estimation Challenge Self-supervised object motion and depth estimation from video

Reference 22

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Observation edb8049b-f458-4aca-a438-ff8700b5dfa8 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

The Fourth Monocular Depth Estimation Challenge Imagenet: A large-scale hierarchical image database

Reference 23

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Observation d0431982-3556-40f8-bc18-288c1791afa0 · outbound

This paper cites DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation.

The Fourth Monocular Depth Estimation Challenge DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation

Reference 24

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Observation 662cb665-3108-496d-9084-1759b14d91b7 · outbound

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

The Fourth Monocular Depth Estimation Challenge Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans

Reference 25

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Observation 5775c734-af80-4b93-8b99-20fdc95b9482 · outbound

This paper cites Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture.

The Fourth Monocular Depth Estimation Challenge Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

Reference 26

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Observation a58d2f9f-18af-46b7-8b3d-4533010e2d30 · outbound

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

The Fourth Monocular Depth Estimation Challenge Deep ordinal regression network for monocular depth estimation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.466357Z

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Observation 03b93051-a276-4be9-8f70-7af5cdf9647a · outbound

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

The Fourth Monocular Depth Estimation Challenge Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.470276Z

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Observation 2ff1848a-16ab-46b0-8905-efc57ffa5f13 · outbound

This paper cites Unsupervised CNN for Single View Depth Estimation: Ge- ometry to the Rescue.

The Fourth Monocular Depth Estimation Challenge Unsupervised CNN for Single View Depth Estimation: Ge- ometry to the Rescue

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.473929Z

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Observation 206a3122-5a32-4eab-946c-0a0a2bdeef83 · outbound

This paper cites R4dyn: Exploring radar for self-supervised monocular depth estima- tion of dynamic scenes.

The Fourth Monocular Depth Estimation Challenge R4dyn: Exploring radar for self-supervised monocular depth estima- tion of dynamic scenes

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.477353Z

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Observation 0a70c986-f97a-4bcd-87e1-73353d66153b · outbound

This paper cites Robust monocular depth estimation under challenging conditions.

The Fourth Monocular Depth Estimation Challenge Robust monocular depth estimation under challenging conditions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.480732Z

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Observation 101f24fc-c6d9-4661-9688-00f37adb03ab · outbound

This paper cites Vision meets robotics: The KITTI dataset.

The Fourth Monocular Depth Estimation Challenge Vision meets robotics: The KITTI dataset

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.484250Z

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Observation 4b18e114-07cb-4df7-80d3-21581a71de4e · outbound

This paper cites an unresolved cited work.

The Fourth Monocular Depth Estimation Challenge Unresolved cited work

Reference 33

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Observation 1d2d33ad-78f7-4d5e-ab10-be48cb06806a · outbound

This paper cites Digging Into Self-Supervised Monocular Depth Estimation.

The Fourth Monocular Depth Estimation Challenge Digging Into Self-Supervised Monocular Depth Estimation

Reference 34

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Observation 57ca657a-0633-482b-9f80-31d55539ac1c · outbound

This paper cites PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single- View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss.

The Fourth Monocular Depth Estimation Challenge PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single- View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.495838Z

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Observation 953443da-519b-43bf-b889-409b99ee96bf · outbound

This paper cites Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras.

The Fourth Monocular Depth Estimation Challenge Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras

Reference 36

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Observation 706dee01-cdc3-457b-ab04-b9cb5cb02038 · outbound

This paper cites 3D packing for self- supervised monocular depth estimation.

The Fourth Monocular Depth Estimation Challenge 3D packing for self- supervised monocular depth estimation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.503549Z

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Observation 958fb6f6-2079-4a5d-92c2-08d77dedcb17 · outbound

This paper cites Towards zero-shot scale-aware monoc- ular depth estimation.

The Fourth Monocular Depth Estimation Challenge Towards zero-shot scale-aware monoc- ular depth estimation

Reference 38

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Observation 546d5089-38df-4a4a-9964-2d8508b8266c · outbound

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

The Fourth Monocular Depth Estimation Challenge Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction

Reference 39

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Observation d1e52c16-53e5-4f19-8d54-a20d3a75767e · outbound

This paper cites Denoising diffu- sion probabilistic models, 2020.

The Fourth Monocular Depth Estimation Challenge Denoising diffu- sion probabilistic models, 2020

Reference 40

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Observation e04337c2-3be8-4f5d-9740-1fbc19b839f3 · outbound

This paper cites Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation.

The Fourth Monocular Depth Estimation Challenge Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.517789Z

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Observation b3c5cfee-2254-4ee3-bff2-3d9a63b4c63a · outbound

This paper cites DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos.

The Fourth Monocular Depth Estimation Challenge DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos

Reference 42

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Observation 6af11b9b-f6a7-49be-90cb-dc5731434a87 · outbound

This paper cites Densely connected convolutional net- works.

The Fourth Monocular Depth Estimation Challenge Densely connected convolutional net- works

Reference 43

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Observation b9da6459-fa9f-401c-8585-b9048039360c · outbound

This paper cites The apolloscape open dataset for autonomous driving and its application.

The Fourth Monocular Depth Estimation Challenge The apolloscape open dataset for autonomous driving and its application

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.528786Z

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Observation 23e2e659-9c04-4d99-ab66-cd61c886d267 · outbound

This paper cites Lightweight monocular depth with a novel neural architecture search method.

The Fourth Monocular Depth Estimation Challenge Lightweight monocular depth with a novel neural architecture search method

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.532262Z

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Observation ec103e15-fd06-43b6-942a-66d1b5e07888 · outbound

This paper cites DDP: Diffusion model for dense visual prediction.

The Fourth Monocular Depth Estimation Challenge DDP: Diffusion model for dense visual prediction

Reference 46

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Observation ae990da1-0d01-4219-8540-8693dfeb7fc9 · outbound

This paper cites Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity V olume.

The Fourth Monocular Depth Estimation Challenge Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity V olume

Reference 47

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Observation 2f080520-833a-42f9-9e07-06c250817a89 · outbound

This paper cites Video depth without video models.

The Fourth Monocular Depth Estimation Challenge Video depth without video models

Reference 48

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Observation 703eaba9-1693-44d4-801c-d808cb285ede · outbound

This paper cites Repurpos- ing diffusion-based image generators for monocular depth estimation.

The Fourth Monocular Depth Estimation Challenge Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.546185Z

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Observation d57f5965-3be7-4d5c-b24d-843174b9f1ee · outbound

This paper cites Supervising the New with the Old: Learning SFM from SFM.

The Fourth Monocular Depth Estimation Challenge Supervising the New with the Old: Learning SFM from SFM

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.550034Z

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Observation fef8987b-2a2b-405c-9f14-1524ecb968b5 · outbound

This paper cites Evaluation of CNN-Based Single-Image Depth Estimation Methods.

The Fourth Monocular Depth Estimation Challenge Evaluation of CNN-Based Single-Image Depth Estimation Methods

Reference 51

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Observation d59ca31f-5fdb-4a7c-9a99-40713e3ae983 · outbound

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

The Fourth Monocular Depth Estimation Challenge Deeper depth prediction with fully convolutional residual networks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.557411Z

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Observation 21cb63ec-daec-48fb-a5a1-3cbd07437e72 · outbound

This paper cites Deep attention-based classification network for robust depth prediction.

The Fourth Monocular Depth Estimation Challenge Deep attention-based classification network for robust depth prediction

Reference 53

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Observation 80ea7f65-23ba-439d-a1c8-e460d4133280 · outbound

This paper cites Mannequin- challenge: Learning the depths of moving people by watch- 10 ing frozen people.

The Fourth Monocular Depth Estimation Challenge Mannequin- challenge: Learning the depths of moving people by watch- 10 ing frozen people

Reference 54

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Observation 9236048d-23a2-4437-9fbd-3d53f17a835d · outbound

This paper cites Megadepth: Learning single- view depth prediction from internet photos.

The Fourth Monocular Depth Estimation Challenge Megadepth: Learning single- view depth prediction from internet photos

Reference 55

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Observation f4a7f405-4e18-467b-ae64-ad06d1656d0a · outbound

This paper cites Deep con- volutional neural fields for depth estimation from a single image.

The Fourth Monocular Depth Estimation Challenge Deep con- volutional neural fields for depth estimation from a single image

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.571458Z

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Observation 28283c71-e894-42aa-be89-1c5fe55fe3fd · outbound

This paper cites Sm4depth: Seamless monoc- ular metric depth estimation across multiple cameras and scenes by one model.

The Fourth Monocular Depth Estimation Challenge Sm4depth: Seamless monoc- ular metric depth estimation across multiple cameras and scenes by one model

Reference 57

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Observation 976a9247-12ac-4adb-9d08-6f73f404ba4b · outbound

This paper cites Swiftdepth: An efficient hybrid cnn-transformer model for self-supervised monocular depth estimation on mobile devices.

The Fourth Monocular Depth Estimation Challenge Swiftdepth: An efficient hybrid cnn-transformer model for self-supervised monocular depth estimation on mobile devices

Reference 58

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Observation 55f77466-c165-48de-9f0f-5755ef960f72 · outbound

This paper cites NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training.

The Fourth Monocular Depth Estimation Challenge NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:35:24.020822Z

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

Observation 0f654a77-29ce-4245-86d2-1c6a8cd1d704 · outbound

This paper cites Every pixel counts ++: Joint learning of geometry and motion with 3d holistic understanding.

The Fourth Monocular Depth Estimation Challenge Every pixel counts ++: Joint learning of geometry and motion with 3d holistic understanding

Reference 60

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Observation 1306d380-35e4-458f-8881-8a3a316aa359 · outbound

This paper cites HR-Depth: High Resolution Self-Supervised Monocular Depth Estima- tion.

The Fourth Monocular Depth Estimation Challenge HR-Depth: High Resolution Self-Supervised Monocular Depth Estima- tion

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.588568Z

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Observation cafcae42-6085-499b-b423-31410d0ee43c · outbound

This paper cites Un- supervised Learning of Depth and Ego-Motion from Monoc- ular Video Using 3D Geometric Constraints.

The Fourth Monocular Depth Estimation Challenge Un- supervised Learning of Depth and Ego-Motion from Monoc- ular Video Using 3D Geometric Constraints

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.592047Z

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Observation 6fac093f-c398-4910-9c4a-baff12c60c26 · outbound

This paper cites Fine-tuning image-conditional diffusion models is easier than you think.

The Fourth Monocular Depth Estimation Challenge Fine-tuning image-conditional diffusion models is easier than you think

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.595325Z

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Observation 365c8493-6638-4b22-a29a-c64fd0fa1cdf · outbound

This paper cites Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging.

The Fourth Monocular Depth Estimation Challenge Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.598703Z

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Observation 09a990e3-3fd7-4b8b-8f59-4355b25726e5 · outbound

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

The Fourth Monocular Depth Estimation Challenge Indoor segmentation and support inference from rgbd images

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.602474Z

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Observation b6571dea-b183-40db-9bfc-a000274d18ae · outbound

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

The Fourth Monocular Depth Estimation Challenge DINOv2: Learning Robust Visual Features without Supervision

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.605899Z

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Observation 1adb4238-0c37-487c-83bc-d5786bdea3ad · outbound

This paper cites From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction.

The Fourth Monocular Depth Estimation Challenge From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.609775Z

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Observation f30596e7-2302-498b-931c-4317a7e7a082 · outbound

This paper cites Codalab competitions: An open source platform to organize scientific challenges.

The Fourth Monocular Depth Estimation Challenge Codalab competitions: An open source platform to organize scientific challenges

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.613705Z

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Observation da9149d7-2f1d-4633-a45f-5a4a2ef987c7 · outbound

This paper cites Monocular depth perception on microcontrollers for edge applications.

The Fourth Monocular Depth Estimation Challenge Monocular depth perception on microcontrollers for edge applications

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.617875Z

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Observation 996beac3-2e87-4d31-8691-7b586608fdff · outbound

This paper cites Enabling energy-efficient unsupervised monocular depth estimation on armv7-based platforms.

The Fourth Monocular Depth Estimation Challenge Enabling energy-efficient unsupervised monocular depth estimation on armv7-based platforms

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.621590Z

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Observation c9d451b8-5465-4001-9e18-4ee69f32333d · outbound

This paper cites Excavating the potential capacity of self- supervised monocular depth estimation.

The Fourth Monocular Depth Estimation Challenge Excavating the potential capacity of self- supervised monocular depth estimation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:25.004095Z

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Observation a9e49475-1fc7-4e19-8011-ccbe0d29d5b0 · outbound

This paper cites UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler.

The Fourth Monocular Depth Estimation Challenge UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.629006Z

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Observation 7479ce21-6d92-4e0c-b836-05abef5e74d1 · outbound

This paper cites Unidepth: Universal monocular metric depth estimation.

The Fourth Monocular Depth Estimation Challenge Unidepth: Universal monocular metric depth estimation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.989018Z

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Observation c2ddc3c7-3d08-4473-b3cc-ef09f4bde678 · outbound

This paper cites Su- perDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation.

The Fourth Monocular Depth Estimation Challenge Su- perDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.973094Z

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Observation a7dcebc7-4778-4e3b-a118-3a4f6cfbd5d9 · outbound

This paper cites Towards real-time unsupervised monocular depth es- timation on cpu.

The Fourth Monocular Depth Estimation Challenge Towards real-time unsupervised monocular depth es- timation on cpu

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.957357Z

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Observation 9f17d099-9cf1-45f4-909b-6293bb5c463d · outbound

This paper cites On the Uncertainty of Self-Supervised Monocular Depth Estimation.

The Fourth Monocular Depth Estimation Challenge On the Uncertainty of Self-Supervised Monocular Depth Estimation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.942189Z

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Observation 16068b08-3cdd-438f-9980-bae4f50d1bdf · outbound

This paper cites On the synergies between machine learning and binocular stereo for depth estimation from images: a survey.

The Fourth Monocular Depth Estimation Challenge On the synergies between machine learning and binocular stereo for depth estimation from images: a survey

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.928167Z

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Observation 2e64b0af-fd8b-4c0c-9b01-8a3dec494d72 · outbound

This paper cites Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions.

The Fourth Monocular Depth Estimation Challenge Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.914096Z

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Observation a561d9e0-610a-486a-b90b-6d3da78d82fa · outbound

This paper cites Booster: a benchmark for depth from images of specular and transparent surfaces.

The Fourth Monocular Depth Estimation Challenge Booster: a benchmark for depth from images of specular and transparent surfaces

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.899275Z

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Observation 6fb14381-5a1b-409d-934c-ae464ef774e7 · outbound

This paper cites Open chal- lenges in deep stereo: the booster dataset.

The Fourth Monocular Depth Estimation Challenge Open chal- lenges in deep stereo: the booster dataset

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.880937Z

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Observation 36d94958-9877-47c6-92a7-d2ecf6013ba3 · outbound

This paper cites Vi- sion transformers for dense prediction.

The Fourth Monocular Depth Estimation Challenge Vi- sion transformers for dense prediction

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.865687Z

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Observation d49d1fa0-aeec-4867-9f4a-6cbf5b3551ce · outbound

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

The Fourth Monocular Depth Estimation Challenge Towards robust monocu- lar depth estimation: Mixing datasets for zero-shot cross- dataset transfer

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.849200Z

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Observation 4a245c41-9e94-4d2d-ba88-10449f491999 · outbound

This paper cites Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation.

The Fourth Monocular Depth Estimation Challenge Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.821905Z

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Observation 51719ae8-59ad-472b-b21b-118d7c582d1b · outbound

This paper cites Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding.

The Fourth Monocular Depth Estimation Challenge Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.804257Z

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Observation ec6cb7e7-3943-479f-b70b-402b07d2570f · outbound

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

The Fourth Monocular Depth Estimation Challenge High-resolution image synthesis with latent diffusion models

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.678188Z

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Observation a524a705-cc5f-4902-be36-391e3e8c11a5 · outbound

This paper cites Monocular depth esti- mation using neural regression forest.

The Fourth Monocular Depth Estimation Challenge Monocular depth esti- mation using neural regression forest

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.778602Z

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Observation 90d27e17-aaf1-4931-8e4d-d9d333502137 · outbound

This paper cites Deep Virtual Stereo Odometry: Leveraging Deep Depth Pre- diction for Monocular Direct Sparse Odometry.

The Fourth Monocular Depth Estimation Challenge Deep Virtual Stereo Odometry: Leveraging Deep Depth Pre- diction for Monocular Direct Sparse Odometry

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.764781Z

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Observation 46a6cb29-5dfa-4f39-a5ce-bd8d2f869526 · outbound

This paper cites The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation.

The Fourth Monocular Depth Estimation Challenge The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.688685Z

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Observation 557c2d8d-dc93-495d-962f-f911bb896539 · outbound

This paper cites Monocular Depth Estimation using Diffusion Models.

The Fourth Monocular Depth Estimation Challenge Monocular Depth Estimation using Diffusion Models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.692379Z

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Observation 95f594d0-4914-40c8-a9c6-831b297bdc13 · outbound

This paper cites Swiftformer: Efficient additive attention for transformer- based real-time mobile vision applications.

The Fourth Monocular Depth Estimation Challenge Swiftformer: Efficient additive attention for transformer- based real-time mobile vision applications

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.748630Z

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Observation 3e1be777-134e-4778-aa7c-f28c3ab04bb8 · outbound

This paper cites Learning temporally consistent video depth from video diffusion priors.

The Fourth Monocular Depth Estimation Challenge Learning temporally consistent video depth from video diffusion priors

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.733194Z

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Observation 256e6756-84af-4895-9967-2e6fa7d947b9 · outbound

This paper cites Denoising Diffusion Implicit Models.

The Fourth Monocular Depth Estimation Challenge Denoising Diffusion Implicit Models

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.703686Z

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Observation 2b0a770c-3fa3-4d2b-a7c3-a109c0249169 · outbound

This paper cites DeFeat-Net: General monocular depth via simultaneous unsupervised representation learning.

The Fourth Monocular Depth Estimation Challenge DeFeat-Net: General monocular depth via simultaneous unsupervised representation learning

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.715205Z

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Observation 0bc43264-4f92-4a73-aeee-99a8bc1fe446 · outbound

This paper cites The monoc- ular depth estimation challenge.

The Fourth Monocular Depth Estimation Challenge The monoc- ular depth estimation challenge

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.697331Z

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

Observation de930263-bdc5-4885-9515-43cf3f355746 · outbound

This paper cites The second monocular depth estimation challenge.

The Fourth Monocular Depth Estimation Challenge The second monocular depth estimation challenge

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.676291Z

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Observation 0acf9325-fe5d-497c-b459-ddf967426344 · outbound

This paper cites Deconstructing self-supervised monocular recon- struction: The design decisions that matter.

The Fourth Monocular Depth Estimation Challenge Deconstructing self-supervised monocular recon- struction: The design decisions that matter

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.659840Z

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Observation 75ae5ac5-f663-4aec-9adc-bb42c5c2b8b4 · outbound

This paper cites Kick back & relax: Learning to reconstruct the world by watching slowtv.

The Fourth Monocular Depth Estimation Challenge Kick back & relax: Learning to reconstruct the world by watching slowtv

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.646951Z

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Observation f82bd0c7-c1d1-4e3f-b7e8-fe0c6cf4fcbb · outbound

This paper cites Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV.

The Fourth Monocular Depth Estimation Challenge Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:23.726288Z

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Observation ed9cdcb4-696d-4dfc-bb22-82ef7a4419cc · outbound

This paper cites an unresolved cited work.

The Fourth Monocular Depth Estimation Challenge Unresolved cited work

Reference 99

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:35:24.632555Z

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Observation e055eaa4-742f-40b8-9a72-5bd5773d8c53 · outbound

This paper cites Sturm, N.

The Fourth Monocular Depth Estimation Challenge Sturm, N

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:24.616073Z

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