PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation | Paper Citation Record

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This paper cites BEiT: BERT Pre-Training of Image Transformers.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation BEiT: BERT Pre-Training of Image Transformers

Reference 1

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Observation 9eeca964-b92a-41d1-9d60-c1e68d55d138 · outbound

This paper cites Adabins: Depth estimation using adaptive bins.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Adabins: Depth estimation using adaptive bins

Reference 2

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Observation 488acc16-fd93-43cb-a23e-bbe3050ab1dc · outbound

This paper cites Localbins: Improving depth estimation by learning local dis- tributions.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Localbins: Improving depth estimation by learning local dis- tributions

Reference 3

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Observation 0b520ab4-cd76-4b0d-9e07-beb4872c2d60 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 4

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This paper cites In- structpix2pix: Learning to follow image editing instructions.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation In- structpix2pix: Learning to follow image editing instructions

Reference 5

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Observation a67d3782-8c1e-4f18-b816-7e2a050fdfb1 · outbound

This paper cites On the over-smoothing problem of cnn based disparity estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation On the over-smoothing problem of cnn based disparity estimation

Reference 6

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Observation 1d38ba68-d804-43aa-a85f-8150aef375c6 · outbound

This paper cites Progressive feature alignment for unsupervised do- main adaptation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Progressive feature alignment for unsupervised do- main adaptation

Reference 7

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Observation ee4536cb-f9b0-4068-9700-fbda5f535fb2 · outbound

This paper cites Learning con- tinuous image representation with local implicit image func- tion.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Learning con- tinuous image representation with local implicit image func- tion

Reference 8

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Observation 9fdaa7ed-885f-4930-8955-4e7565105780 · outbound

This paper cites Crdoco: Pixel-level domain transfer with cross- domain consistency.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Crdoco: Pixel-level domain transfer with cross- domain consistency

Reference 9

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Observation 84b9d8b7-e1bb-4c4d-b839-39ee900ff2ff · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation The cityscapes dataset for semantic urban scene understanding

Reference 10

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Observation f918c0ec-5075-4e88-b1dc-b7291fabdab2 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 11

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Observation 677d244e-06b0-4822-a9df-1d17385314b1 · outbound

This paper cites Learn- ing graph regularisation for guided super-resolution.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Learn- ing graph regularisation for guided super-resolution

Reference 12

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Observation 724cd5bc-3a64-4eb5-ac0b-b74d258803d8 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Imagenet: A large-scale hierarchical image database

Reference 13

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Observation 1fe63c12-d943-43cf-8b70-5b3585e2e63f · outbound

This paper cites Soft labels for ordinal regres- sion.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Soft labels for ordinal regres- sion

Reference 14

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Observation 4970c2b4-6c04-4ba4-b803-f757233bbc9c · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

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Observation 89ed9234-91b5-405e-a5a2-d33db95badd6 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Depth map prediction from a single image using a multi-scale deep net- work

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:33.585690Z

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Observation 66095900-0151-404a-9a47-a8af6ac44618 · outbound

This paper cites Con- trastive learning for depth prediction.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Con- trastive learning for depth prediction

Reference 17

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Observation b4631768-213d-42ef-8cc0-d09e3460109e · outbound

This paper cites Deep ordinal regression net- work for monocular depth estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Deep ordinal regression net- work for monocular depth estimation

Reference 18

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Observation 4f775e49-69bd-406f-900d-0a18c300408e · outbound

This paper cites Unsupervised cnn for single view depth estimation: Geom- etry to the rescue.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Unsupervised cnn for single view depth estimation: Geom- etry to the rescue

Reference 19

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Observation cde84626-b60b-4b4d-9da9-d5c42c0c8b81 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 20

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Observation 4df9b9a1-672b-4c5e-b335-7c561e9869af · outbound

This paper cites Vision meets robotics: The kitti dataset.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Vision meets robotics: The kitti dataset

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:33.526106Z

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Observation a46b9c45-ad09-4b26-a038-296276e26cd3 · outbound

This paper cites Digging into self-supervised monocular depth estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Digging into self-supervised monocular depth estimation

Reference 22

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Observation b590ff10-2f87-458b-95b4-00ec3524e467 · outbound

This paper cites Mask r-cnn.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Mask r-cnn

Reference 23

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Observation aaa8963c-a045-4e69-8939-b0c8b6ebf416 · outbound

This paper cites Searching for mo- bilenetv3.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Searching for mo- bilenetv3

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:33.481556Z

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Observation 6666c102-8c87-490b-ac06-0d664ed183b5 · outbound

This paper cites Depth map super-resolution by deep multi-scale guidance.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Depth map super-resolution by deep multi-scale guidance

Reference 25

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Observation 0f3d834a-3ea8-4625-a636-2d399cf681b8 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 26

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Observation 38471aa5-7808-4976-a962-0af3dff6dfab · outbound

This paper cites Segment any- thing.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Segment any- thing

Reference 27

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This paper cites Sharingan: Combining synthetic and real data for unsupervised geome- try estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Sharingan: Combining synthetic and real data for unsupervised geome- try estimation

Reference 28

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Observation d4c4da76-5494-4b0b-bdb6-28c5b5530375 · outbound

This paper cites Adadepth: Unsupervised content congruent adaptation for depth estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Adadepth: Unsupervised content congruent adaptation for depth estimation

Reference 29

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This paper cites Multi-loss rebalancing al- gorithm for monocular depth estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Multi-loss rebalancing al- gorithm for monocular depth estimation

Reference 30

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Observation 0133929b-9897-4f02-aa1b-aaf2e0aa37be · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

Reference 31

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Observation 9333e867-b681-4f04-a35a-e55d9996cca7 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Megadepth: Learning single- view depth prediction from internet photos

Reference 32

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Observation 4b15d477-f840-4dc6-90de-1d498d11d456 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

Reference 33

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Observation b9ef3007-da0b-43db-8f27-3df826c14a28 · outbound

This paper cites PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation

Reference 34

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This paper cites Depthformer: Exploiting long-range correlation and local in- formation for accurate monocular depth estimation.Machine Intelligence Research, pages 1–18, 2023.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Depthformer: Exploiting long-range correlation and local in- formation for accurate monocular depth estimation.Machine Intelligence Research, pages 1–18, 2023

Reference 35

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Observation db26bcc6-c05c-4e50-8fe7-54477148c69e · outbound

This paper cites PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation

Reference 36

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Refinenet: Multi-path refinement networks for high- resolution semantic segmentation

Reference 37

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This paper cites Single image depth prediction made better: A multivariate gaussian take.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Single image depth prediction made better: A multivariate gaussian take

Reference 38

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This paper cites Zero-1-to-3: Zero-shot one image to 3d object.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Zero-1-to-3: Zero-shot one image to 3d object

Reference 39

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Observation 031b2973-6f75-496d-994a-5aad023aa4a3 · outbound

This paper cites Desc: Domain adaptation for depth estimation via semantic con- sistency.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Desc: Domain adaptation for depth estimation via semantic con- sistency

Reference 40

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Observation ba1325a9-d13f-4f97-9b02-1956627b6914 · outbound

This paper cites Guided depth super-resolution by deep anisotropic diffusion.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Guided depth super-resolution by deep anisotropic diffusion

Reference 41

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Boosting monocular depth estima- tion models to high-resolution via content-adaptive multi- resolution merging

Reference 42

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Observation 22531496-5d25-4329-a325-1471f8576048 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 43

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation DINOv2: Learning Robust Visual Features without Supervision

Reference 44

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This paper cites pix2gestalt: Amodal segmentation by synthesizing wholes.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation pix2gestalt: Amodal segmentation by synthesizing wholes

Reference 45

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Observation 6942e142-7e7e-49bb-aec5-15b6bd09566b · outbound

This paper cites A closer look at self-training for zero-label semantic segmentation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation A closer look at self-training for zero-label semantic segmentation

Reference 46

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Observation 530395ff-e5e2-43e5-aa25-7dae955f84f1 · outbound

This paper cites Exploiting pseudo labels in a self-supervised learning framework for improved monocular depth estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Exploiting pseudo labels in a self-supervised learning framework for improved monocular depth estimation

Reference 47

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Observation 92cc5270-951a-4ff1-a89d-15f0f0d7d3d0 · outbound

This paper cites idisc: Internal discretization for monocular depth estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation idisc: Internal discretization for monocular depth estimation

Reference 48

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Observation cf237892-7ab4-4739-b93e-b283280e9a24 · outbound

This paper cites Boost- ing instance segmentation with synthetic data: A study to overcome the limits of real world data sets.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Boost- ing instance segmentation with synthetic data: A study to overcome the limits of real world data sets

Reference 49

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Observation b92a0d20-938b-427d-873a-77244445a372 · outbound

This paper cites The simple and efficient semi-supervised learning method for deep neural networks.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation The simple and efficient semi-supervised learning method for deep neural networks

Reference 50

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation MobileNetV4 -- Universal Models for the Mobile Ecosystem

Reference 51

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation High-resolution syn- thetic rgb-d datasets for monocular depth estimation

Reference 52

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Vi- sion transformers for dense prediction

Reference 53

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

Reference 54

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation High-resolution image syn- thesis with latent diffusion models

Reference 56

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation U-net: Convolutional networks for biomedical image segmentation,

Reference 57

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Asymmetric tri-training for unsupervised domain adaptation

Reference 58

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This paper cites High-resolution stereo datasets with subpixel-accurate ground truth.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation High-resolution stereo datasets with subpixel-accurate ground truth

Reference 59

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation A multi-view stereo benchmark with high- resolution images and multi-camera videos

Reference 60

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Mm-tta: multi-modal test-time adaptation for 3d se- mantic segmentation

Reference 61

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Indoor segmentation and support inference from rgbd images

Reference 62

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Sun rgb-d: A rgb-d scene understanding benchmark suite

Reference 63

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 64

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Smd-nets: Stereo mixture density networks

Reference 65

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation A survey on semi-supervised learning

Reference 66

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This paper cites Image quality assessment: from error visibility to structural similarity.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Image quality assessment: from error visibility to structural similarity

Reference 67

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Monocular relative depth per- ception with web stereo data supervision

Reference 68

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Structure-guided ranking loss for single image depth prediction

Reference 69

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Transformer-based attention networks for con- tinuous pixel-wise prediction

Reference 70

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Observation ae883fcb-4fa9-4f58-9b55-c06fbbb0f221 · outbound

This paper cites Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Reference 71

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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Depth Anything V2

Reference 72

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Observation 0bc6e667-f5f1-4291-abc2-c19ffe467ea0 · outbound

This paper cites Scannet++: A high-fidelity dataset of 3d indoor scenes.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Scannet++: A high-fidelity dataset of 3d indoor scenes

Reference 73

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Observation c75dd923-8763-4124-b864-efd3a7ab70a6 · outbound

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

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Adding conditional control to text-to-image diffusion models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:32.903258Z

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Observation 32db8442-4e0c-4f8a-a064-261e522fcb57 · outbound

This paper cites Geometry-aware symmetric domain adaptation for monocular depth estimation.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Geometry-aware symmetric domain adaptation for monocular depth estimation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:32.891607Z

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Observation da5dd5a4-e730-4a4d-bffd-5417bff7ac0c · outbound

This paper cites Discrete cosine transform network for guided depth map super-resolution.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Discrete cosine transform network for guided depth map super-resolution

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:32.878500Z

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Observation a264ee3e-570f-4f08-b711-9e7c3be6fd95 · outbound

This paper cites T2net: Synthetic-to-realistic translation for solving single-image depth estimation tasks.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation T2net: Synthetic-to-realistic translation for solving single-image depth estimation tasks

Reference 77

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Observation ad48d306-256b-489f-b311-4a459bca5ed0 · outbound

This paper cites Guided depth map super-resolution: A survey.

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation Guided depth map super-resolution: A survey

Reference 78

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