Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models | Paper Citation Record

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This paper cites A revised underwater image formation model.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models A revised underwater image formation model

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This paper cites Sea-thru: A method for removing water from underwater images.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Sea-thru: A method for removing water from underwater images

Reference 2

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This paper cites Unav-sim: A visually realistic underwater robotics simulator and synthetic data-generation framework.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unav-sim: A visually realistic underwater robotics simulator and synthetic data-generation framework

Reference 3

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This paper cites Foundation models defining a new era in vision: A survey and outlook.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Foundation models defining a new era in vision: A survey and outlook

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This paper cites Underwater single image color restoration using haze-lines and a new quantitative dataset, 2018.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Underwater single image color restoration using haze-lines and a new quantitative dataset, 2018

Reference 5

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This paper cites Zoedepth: Zero-shot transfer by com- bining relative and metric depth, 2023.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Zoedepth: Zero-shot transfer by com- bining relative and metric depth, 2023

Reference 6

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This paper cites Richter, and Vladlen Koltun.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Richter, and Vladlen Koltun

Reference 7

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This paper cites Pyramid stereo matching network.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Pyramid stereo matching network

Reference 8

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This paper cites When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Reference 9

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This paper cites Chiang and Ying-Ching Chen.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Chiang and Ying-Ching Chen

Reference 10

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This paper cites Best practices for fine-tuning vi- sual classifiers to new domains.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Best practices for fine-tuning vi- sual classifiers to new domains

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This paper cites Indoor Semantic Segmentation using depth information.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Indoor Semantic Segmentation using depth information

Reference 12

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This paper cites Diffusion models in vision: A survey.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Diffusion models in vision: A survey

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This paper cites Davis, R.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Davis, R

Reference 14

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This paper cites CRC Press, 2017.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models CRC Press, 2017

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

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 16

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This paper cites Simultaneous local- ization and mapping: part i.IEEE robotics & automation magazine, 13(2):99–110, 2006.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Simultaneous local- ization and mapping: part i.IEEE robotics & automation magazine, 13(2):99–110, 2006

Reference 17

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This paper cites Metri- cally scaled monocular depth estimation through sparse pri- ors for underwater robots, 2023.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Metri- cally scaled monocular depth estimation through sparse pri- ors for underwater robots, 2023

Reference 18

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This paper cites Depth map prediction from a single image using a multi-scale deep net- work, 2014.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Depth map prediction from a single image using a multi-scale deep net- work, 2014

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This paper cites 3-d mapping with an rgb-d camera.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models 3-d mapping with an rgb-d camera

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This paper cites Relocating underwater features au- tonomously using sonar-based slam.IEEE Journal of Oceanic Engineering, 38(3):500–513, 2013.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Relocating underwater features au- tonomously using sonar-based slam.IEEE Journal of Oceanic Engineering, 38(3):500–513, 2013

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This paper cites Underwa- ter object detection: architectures and algorithms–a compre- hensive review.Multimedia Tools and Applications, 81(15): 20871–20916, 2022.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Underwa- ter object detection: architectures and algorithms–a compre- hensive review.Multimedia Tools and Applications, 81(15): 20871–20916, 2022

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This paper cites Vision meets robotics: The kitti dataset.The in- ternational journal of robotics research, 32(11):1231–1237,.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Vision meets robotics: The kitti dataset.The in- ternational journal of robotics research, 32(11):1231–1237,

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

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Digging into self-supervised monocular depth estimation

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This paper cites Gonz ´alez-Sabbagh and Antonio Robles-Kelly.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Gonz ´alez-Sabbagh and Antonio Robles-Kelly

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This paper cites Uw-gan: Single-image depth estimation and image enhancement for underwater images.IEEE Transactions on Instrumentation and Measurement, 70:1–12, 2021.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Uw-gan: Single-image depth estimation and image enhancement for underwater images.IEEE Transactions on Instrumentation and Measurement, 70:1–12, 2021

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This paper cites Single image haze removal using dark channel prior.IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 33(12):2341–2353,.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Single image haze removal using dark channel prior.IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 33(12):2341–2353,

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This paper cites Distill any depth: Distillation creates a stronger monocular depth estimator, 2025.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Distill any depth: Distillation creates a stronger monocular depth estimator, 2025

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Teacher-student architecture for knowl- edge distillation: A survey, 2023

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unresolved cited work

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This paper cites Why warmup the learning rate? underlying mechanisms and improvements,.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Why warmup the learning rate? underlying mechanisms and improvements,

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Underwater optical-sonar image fusion systems.Sensors, 22(21):8445,

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This paper cites Fine-tuning can distort pretrained fea- tures and underperform out-of-distribution, 2022.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Fine-tuning can distort pretrained fea- tures and underperform out-of-distribution, 2022

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This paper cites An underwater image enhancement benchmark dataset and beyond.IEEE transac- tions on image processing, 29:4376–4389, 2019.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models An underwater image enhancement benchmark dataset and beyond.IEEE transac- tions on image processing, 29:4376–4389, 2019

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This paper cites Sgdr: Stochastic gradient descent with warm restarts, 2017.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Sgdr: Stochastic gradient descent with warm restarts, 2017

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This paper cites Decoupled weight decay regularization, 2019.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Decoupled weight decay regularization, 2019

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models A survey on vision-based uav navigation.Geo- spatial information science, 21(1):21–32, 2018

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unresolved cited work

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This paper cites Autonomous inspection using an underwater 3d lidar.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Autonomous inspection using an underwater 3d lidar

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This paper cites Mobilevit: Light- weight, general-purpose, and mobile-friendly vision trans- former, 2022.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Mobilevit: Light- weight, general-purpose, and mobile-friendly vision trans- former, 2022

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Deep learning for monocular depth estimation: A review.Neuro- computing, 438:14–33, 2021

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This paper cites Adjeroh, and Gi- anfranco Doretto.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Adjeroh, and Gi- anfranco Doretto

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This paper cites Springer, 2021.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Springer, 2021

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This paper cites A survey of structure from motion, 2017.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models A survey of structure from motion, 2017

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Autonomous mapping of underwater 3-d structures: From view planning to execution.IEEE Robotics and Au- tomation Letters, 3(3):1965–1971, 2018

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Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Visual domain adaptation: A survey of recent advances.IEEE Signal Processing Magazine, 32(3):53–69,

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Observation ee0c203e-f28b-4591-84a1-5ab2dd0e8a68 · outbound

This paper cites Unidepthv2: Universal monocular metric depth estimation made simpler, 2025.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unidepthv2: Universal monocular metric depth estimation made simpler, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:45.665308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f807f409-3d0e-478f-b72e-2d08c6d081a0 · outbound

This paper cites Z-splat: Z-axis gaussian splatting for camera-sonar fusion.IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 2024.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Z-splat: Z-axis gaussian splatting for camera-sonar fusion.IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:45.433677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9d229284-6f99-4b15-8d0d-609eabc4ff17 · outbound

This paper cites Svin2: An underwater slam system using sonar, visual, inertial, and depth sensor.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Svin2: An underwater slam system using sonar, visual, inertial, and depth sensor

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:45.100515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2f0d2166-0927-4704-82ae-21e00dc14080 · outbound

This paper cites PhD thesis, ProQuest Dissertations Publishing,.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models PhD thesis, ProQuest Dissertations Publishing,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:44.781643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 752e11d7-6f1f-406e-a3a9-55879eea0093 · outbound

This paper cites Vi- sion transformers for dense prediction.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Vi- sion transformers for dense prediction

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:44.189202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eee029d8-00cb-41a9-8ead-0184e1dee1df · outbound

This paper cites Underwater image enhancement: a comprehensive re- view, recent trends, challenges and applications.Artificial Intelligence Review, 54:5413–5467, 2021.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Underwater image enhancement: a comprehensive re- view, recent trends, challenges and applications.Artificial Intelligence Review, 54:5413–5467, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.902092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 296307c8-2259-4631-830f-47f8dae57970 · outbound

This paper cites Susskind.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Susskind

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T20:40:37.925631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f34a1d23-45ff-4623-ac3d-4ede2d760953 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks, 2019.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Mobilenetv2: Inverted residuals and linear bottlenecks, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.827646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2121510e-998e-454f-b68f-c1b450424f68 · outbound

This paper cites an unresolved cited work.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T20:40:43.711169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1882a4b9-ab5d-4f2a-ad4b-7822f889d2b4 · outbound

This paper cites Schechner and N.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Schechner and N

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.598045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ed96227-1f85-41ba-b32c-2e1fb5703b8d · outbound

This paper cites Unsupervised low-light image enhancement by extracting structural similarity and color consistency.IEEE Signal Pro- cessing Letters, 29:997–1001, 2022.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unsupervised low-light image enhancement by extracting structural similarity and color consistency.IEEE Signal Pro- cessing Letters, 29:997–1001, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.497543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 028029a5-ded7-4c80-830d-d31b0fcf2652 · outbound

This paper cites Solonenko and Curtis D.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Solonenko and Curtis D

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.413104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b183a73-cc2c-4838-b1c5-c9976c695e2e · outbound

This paper cites How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T20:40:38.631360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5e8c015-7ed2-485a-ae5c-c804d042a19a · outbound

This paper cites Review of underwa- ter sensing technologies and applications.Sensors, 21(23): 7849, 2021.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Review of underwa- ter sensing technologies and applications.Sensors, 21(23): 7849, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.296567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e023b084-8cff-4c9c-bc26-713bd79d9cb8 · outbound

This paper cites Vi- sual slam algorithms: A survey from 2010 to 2016.IPSJ transactions on computer vision and applications, 9(1):16,.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Vi- sual slam algorithms: A survey from 2010 to 2016.IPSJ transactions on computer vision and applications, 9(1):16,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.209877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ef0029be-5096-4336-9b8f-3b1ca4dc19c5 · outbound

This paper cites Attention Is All You Need.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Attention Is All You Need

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:40:38.900703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c921e5c7-9de2-4eb3-8643-615dd160a4c1 · outbound

This paper cites Wagner, Nadieh Khalili, Raghav Sharma, Melanie Boxberg, Carsten Marr, Walter de Back, and Tingying Peng.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Wagner, Nadieh Khalili, Raghav Sharma, Melanie Boxberg, Carsten Marr, Walter de Back, and Tingying Peng

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.087656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 833ecaa1-fdc5-40b4-ab0b-0f2e32bf70af · outbound

This paper cites Underwa- ter localization and 3d mapping of submerged structures with a single-beam scanning sonar.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Underwa- ter localization and 3d mapping of submerged structures with a single-beam scanning sonar

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:43.009276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 63d45296-9e08-451f-8851-b6d276bc1cbf · outbound

This paper cites Domain adaptation for underwater im- age enhancement.IEEE Transactions on Image Processing, 32:1442–1457, 2023.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Domain adaptation for underwater im- age enhancement.IEEE Transactions on Image Processing, 32:1442–1457, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:42.884686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6037ca09-44d4-471e-b5bb-aaafbabc6a64 · outbound

This paper cites an unresolved cited work.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:40:42.740924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 213470fa-9bcf-490c-b738-11c7c28b1d91 · outbound

This paper cites Con- vnext v2: Co-designing and scaling convnets with masked autoencoders.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:42.622846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6d4b6045-e9a6-4c28-ad3d-484b4dd39fe2 · outbound

This paper cites Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle

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Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:40:40.905345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da63f1f4-6dcd-4c1b-92ff-cc42fa1da1be · outbound

This paper cites A systematic review and analysis of deep learning-based underwater object detection.Neurocomput- ing, 527:204–232, 2023.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models A systematic review and analysis of deep learning-based underwater object detection.Neurocomput- ing, 527:204–232, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:42.498755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8895f061-9bae-4b66-9281-80ddc0c7eb78 · outbound

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

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Depth anything: Unleashing the power of large-scale unlabeled data

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:42.305190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 904f8fea-3bb0-43ed-a688-4c262fcbb16e · outbound

This paper cites Depth any- thing v2.Advances in Neural Information Processing Sys- tems, 37:21875–21911, 2024.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Depth any- thing v2.Advances in Neural Information Processing Sys- tems, 37:21875–21911, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:40:39.702402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c880b6af-3d15-46b4-bc42-81a2091f21c3 · outbound

This paper cites Udepth: Fast monocular depth estimation for visually-guided underwater robots.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Udepth: Fast monocular depth estimation for visually-guided underwater robots

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:42.075637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b25f2ee8-5288-4194-bcad-2afb07e036f8 · outbound

This paper cites Atlantis: En- abling underwater depth estimation with stable diffusion.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Atlantis: En- abling underwater depth estimation with stable diffusion

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:41.922757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 14f3d60c-7fec-442e-9983-f9c4362ac86b · outbound

This paper cites Survey on monocular metric depth estima- tion, 2025.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Survey on monocular metric depth estima- tion, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:41.751166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ce7bf17-7f7a-424d-811d-870064844d7e · outbound

This paper cites Adding conditional control to text-to-image diffusion models, 2023.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Adding conditional control to text-to-image diffusion models, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:40:40.107543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5322357e-4a27-4491-98d3-4b70fa1b78e9 · outbound

This paper cites An open-source, fiducial-based, un- derwater stereo visual-inertial localization method with re- fraction correction.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models An open-source, fiducial-based, un- derwater stereo visual-inertial localization method with re- fraction correction

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:41.610170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 990bd285-6fde-4141-9696-2e8ce44c883d · outbound

This paper cites Overview of underwater trans- mission characteristics of oceanic lidar.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14:8144–8159, 2021.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Overview of underwater trans- mission characteristics of oceanic lidar.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14:8144–8159, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:41.401948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation abc40dad-0b1b-4245-8229-9dd744926876 · outbound

This paper cites Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:40:40.513899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 837e0b85-4738-41d7-ac2e-6870ef1e8d84 · outbound

This paper cites Surrogate gap minimization improves sharpness-aware training.ICLR, 2022.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Surrogate gap minimization improves sharpness-aware training.ICLR, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:41.247402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation abdbe711-e60c-4c70-adff-a82d191ee010 · outbound

This paper cites Underwater rgb- d camera based on binocular stereo vision.Acta Photonica Sin, 51:0404003, 2022.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models Underwater rgb- d camera based on binocular stereo vision.Acta Photonica Sin, 51:0404003, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:41.100164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da7638e0-8e27-4a30-a3c5-52e1ad6e07a6 · outbound

This paper cites 1, 2, 3, 4, 5, 6, 8.

Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models 1, 2, 3, 4, 5, 6, 8

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:40:44.488798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers