Paper Citation Record · LEDGER

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models?

As of 12 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 2 inbound Pith citation observations for arXiv:2507.15321.

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

pith.paper-citation-record.v1
2507.15321 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:39:00.381942Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:54:38.195630Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:54:42.163341Z

Reference resolution

53 of 53 outbound references displayed

verified exact2
verified fuzzy27
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e124307c-0bcc-4c7a-89ca-ed41e151d917 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Adding conditional control to text-to-image diffusion models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:06.623257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation adcab25c-3d16-40a5-a930-5b1809337831 · outbound

This paper cites Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:06.454132Z

Source-reported events for the cited work

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Observation 93b164f9-3292-492c-8a9c-37ad1371bac8 · outbound

This paper cites Nicer-slam: Neural implicit scene encoding for rgb slam,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Nicer-slam: Neural implicit scene encoding for rgb slam,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:56.471746Z

Source-reported events for the cited work

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Observation dfc3e3d5-4399-4ae0-b590-d2c2c63da337 · outbound

This paper cites Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:56.555384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aeaa437-dfa7-4ead-bb4d-18b8aae8d8a0 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Depth map prediction from a single image using a multi-scale deep network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:06.283461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f2a35ac1-5863-4e2f-b2da-55d94603b8be · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:56.731960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bebbf98-376c-4b4b-81bf-f6afaa5ae5fa · outbound

This paper cites Repurposing diffusion-based image generators for monocular depth estimation,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Repurposing diffusion-based image generators for monocular depth estimation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:06.097312Z

Source-reported events for the cited work

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Observation 7a554f73-967f-4f5c-b545-0c82c0edebfc · outbound

This paper cites Depth Anything V2.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Depth Anything V2

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:57.027314Z

Source-reported events for the cited work

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Observation f3655d69-e6c4-4d74-bfd5-8c5b76487266 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:05.892989Z

Source-reported events for the cited work

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Observation 8a15b29b-dc96-4465-b23d-caacc236bef3 · outbound

This paper cites MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:57.185124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5de7b7b2-9072-4558-9ff3-a1b376030544 · outbound

This paper cites Vggt: Visual geometry grounded transformer,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Vggt: Visual geometry grounded transformer,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:05.680425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 42d7aab9-a3c5-42f1-bc46-345e900d21c1 · outbound

This paper cites GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models

Reference 12

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Source-reported events for the cited work

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Observation 19c2c986-2c44-4086-aea0-44b895e9382c · outbound

This paper cites Unidepth: Universal monocular metric depth estimation,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Unidepth: Universal monocular metric depth estimation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:05.504635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eed43b4e-17c4-49cc-b268-576da9317761 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:05.350764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e14575a5-c5c5-4ba1-a698-7c0228fb8a80 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:57.550913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9184d487-9640-4afc-87b3-46c6989abc6f · outbound

This paper cites Depth prompting for sensor-agnostic depth estimation,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Depth prompting for sensor-agnostic depth estimation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:05.202744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 12b1237a-160e-4624-90fd-f9bf50c68e64 · outbound

This paper cites DEFOM-Stereo: Depth Foundation Model Based Stereo Matching.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? DEFOM-Stereo: Depth Foundation Model Based Stereo Matching

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:57.699345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7093496-27fb-4510-b995-f7933a1dfcb5 · outbound

This paper cites Monster: Marry monodepth to stereo unleashes power,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Monster: Marry monodepth to stereo unleashes power,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:57.769703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d21e2ec-5381-420b-87bf-cdf84b5b7774 · outbound

This paper cites GPT-4 Technical Report.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? GPT-4 Technical Report

Reference 19

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Source-reported events for the cited work

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Observation e7e4d6a5-5ae8-4a6f-a054-9cbe5a3f1ef9 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:05.043266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c2f7bb8-647a-4acc-9e59-6069e3b09299 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Momentum contrast for unsupervised visual representation learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:04.862225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9c2bd717-c7dc-4f3c-b7eb-f0824f670b18 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? DINOv2: Learning Robust Visual Features without Supervision

Reference 22

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Source-reported events for the cited work

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Observation 3f1aaad2-0acf-467f-89e4-e2685cf467ad · outbound

This paper cites Iterative geometry encoding volume for stereo matching,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Iterative geometry encoding volume for stereo matching,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:04.620530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cdce873d-4773-4311-9e8b-469dbc993f02 · outbound

This paper cites Towards Foundation Models for 3D Vision: How Close Are We?.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Towards Foundation Models for 3D Vision: How Close Are We?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:58.191736Z

Source-reported events for the cited work

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Observation 3f5ee3a3-266f-46c6-90c3-7e3391dbf9f3 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:04.455152Z

Source-reported events for the cited work

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Observation d85d2515-3ad8-42b6-9cd7-50a2e7e746ef · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:39:01.173560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 591b2cd5-74c6-4d1e-9bfe-ddf2b0dd4675 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? High-resolution image synthesis with latent diffusion models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:04.285408Z

Source-reported events for the cited work

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Observation c33a0e96-fced-4633-9794-6bb2e50d38b1 · outbound

This paper cites Vision meets robotics: The kitti dataset,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Vision meets robotics: The kitti dataset,

Reference 28

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Source-reported events for the cited work

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Observation 85c784cc-ea03-4105-a4e5-8aef0276d83e · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Indoor segmentation and support inference from rgbd images,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:04.133548Z

Source-reported events for the cited work

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Observation c4dd90ab-3975-4be5-9694-cfe619831fdb · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:03.952936Z

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Observation 7b690b27-4108-45bf-92ff-a28efcc1a271 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? The cityscapes dataset for semantic urban scene understanding,

Reference 31

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Source-reported events for the cited work

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Observation 757f17bd-a06f-476e-a41e-e67c0744bc2e · outbound

This paper cites Depthformer: Exploiting long-range correlation and local information for accurate monocular depth estimation,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Depthformer: Exploiting long-range correlation and local information for accurate monocular depth estimation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:03.758253Z

Source-reported events for the cited work

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Observation 54b88136-f9b9-4a72-98dd-bff95671089f · outbound

This paper cites Adabins: Depth estimation using adaptive bins,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Adabins: Depth estimation using adaptive bins,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:03.545218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ac69cdee-f522-4b64-b1e0-d10e4598d939 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:39:00.965831Z

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Observation 50e728e2-4a2b-4b6b-a62f-3c81232f9f63 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Single-image depth perception in the wild,

Reference 35

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Source-reported events for the cited work

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Observation 57e06796-663f-448d-8031-28fcd1c1a00f · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Deep ordinal regression network for monocular depth estimation,

Reference 36

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Source-reported events for the cited work

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Observation 79cf1f24-97b5-41b9-bc42-9ba1530400be · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

Reference 37

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Observation 867874e3-cd3e-4eff-91c1-f72c3d933feb · outbound

This paper cites Scaling Laws for Neural Language Models.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Scaling Laws for Neural Language Models

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 987f3ed6-fe38-47e6-8a36-81d471dafb96 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Reference 39

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Source-reported events for the cited work

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Observation 9396cd64-c70e-44c2-80ad-5fec841c9b52 · outbound

This paper cites Dust3r: Geometric 3d vision made easy,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Dust3r: Geometric 3d vision made easy,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:02.953972Z

Source-reported events for the cited work

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Observation 4a1a5d5d-126b-4a22-bf84-66f8fffa328c · outbound

This paper cites E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models

Reference 41

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BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Unresolved cited work

Reference 42

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Observation a50d503c-ec5e-45d8-b380-11af94307fb8 · outbound

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BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Unresolved cited work

Reference 43

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Observation 51024def-81d0-40a6-b28a-357ef5033b14 · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? 3d gaussian splatting for real-time radiance field rendering.,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:02.101639Z

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Observation dff4708f-a404-46fa-940a-d24a1f858657 · outbound

This paper cites Neural fields in visual computing and beyond,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Neural fields in visual computing and beyond,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:01.969107Z

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Observation 5957f5e9-d878-47cd-9323-840f7158ce10 · outbound

This paper cites SpatialBot: Precise Spatial Understanding with Vision Language Models.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? SpatialBot: Precise Spatial Understanding with Vision Language Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:59.749275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 140f60d3-444d-4955-9868-be753e04eb79 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Fine-tuning image-conditional diffusion models is easier than you think,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T15:38:59.812005Z

Source-reported events for the cited work

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Observation 61631d19-b42d-4889-b400-44709195006d · outbound

This paper cites A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:01.836751Z

Source-reported events for the cited work

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Observation 87b26cbc-8d62-4889-bdc6-f3d6d6a120e6 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? High-resolution stereo datasets with subpixel-accurate ground truth,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:01.696324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 20c6d611-49ff-4085-ac0f-8695b1d41ab1 · outbound

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? A multi-view stereo benchmark with high-resolution images and multi-camera videos,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:01.602098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6ccfc61-7544-4473-9543-f89513578890 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? U-net: Convolutional networks for biomedical image segmentation,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T15:39:00.108139Z

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Observation 471a704a-28a9-4e58-b852-7c7cc8fdece0 · outbound

This paper cites Stereo Magnification: Learning View Synthesis using Multiplane Images.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? Stereo Magnification: Learning View Synthesis using Multiplane Images

Reference 52

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Observation f89b0fbf-50d0-4aaf-a545-abfa029b65a2 · outbound

This paper cites The Replica Dataset: A Digital Replica of Indoor Spaces.

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? The Replica Dataset: A Digital Replica of Indoor Spaces

Reference 53

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Pith citing papers

Observation e0ac6bdd-6f5e-4367-bc95-5c321c141d21 · inbound

Compact and robust optical frequency reference module based on reproducible and redistributable optical design cites this paper.

Compact and robust optical frequency reference module based on reproducible and redistributable optical design BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models?

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:54:42.276824Z

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Observation 53cef9eb-a739-4a71-ab27-dff1b09ced7c · inbound

Boosting Monocular Metric Depth Estimation via Bokeh Rendering cites this paper.

Boosting Monocular Metric Depth Estimation via Bokeh Rendering BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models?

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T16:43:26.435628Z

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