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Paper Citation Record · LEDGER

Region-aware Depth Scale Adaptation with Sparse Measurements

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2507.14879.

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

pith.paper-citation-record.v1
2507.14879 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:54:00.544751Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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  • verified fuzzy30
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74237893-9345-4d85-9a8e-39179250e8ff · outbound

This paper cites Bidirectional attention network for monocular depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Bidirectional attention network for monocular depth estimation

Reference 1

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Source-reported events for the cited work

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

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Observation 3eba431f-8af1-446a-a7d6-32b2626a6b4c · outbound

This paper cites Adabins: Depth estimation using adaptive bins.

Region-aware Depth Scale Adaptation with Sparse Measurements Adabins: Depth estimation using adaptive bins

Reference 2

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Source-reported events for the cited work

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

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Observation a193c079-e55f-4e53-bd65-b5c089134af7 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 3

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Source-reported events for the cited work

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Observation 85464a6c-0a86-4fd3-8095-6d63b1eb12c1 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 4

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no resolver link, observed 2026-08-06T15:53:56.483882Z

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Observation fdd47064-235d-442a-9e39-afd333502b24 · outbound

This paper cites Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation.

Region-aware Depth Scale Adaptation with Sparse Measurements Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation

Reference 5

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Source-reported events for the cited work

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

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Observation 9cf5fe78-0464-4de0-a1ac-13ca71ef02da · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes.

Region-aware Depth Scale Adaptation with Sparse Measurements Sam-adapter: Adapting segment anything in underperformed scenes

Reference 6

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Source-reported events for the cited work

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

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Observation a8fde944-8279-4e55-88bd-e17b3e5a2e00 · outbound

This paper cites Katsaggelos.

Region-aware Depth Scale Adaptation with Sparse Measurements Katsaggelos

Reference 7

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Source-reported events for the cited work

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

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Observation 18b971d7-f336-4f18-8181-0fbcf839eb1e · outbound

This paper cites Cswin transformer: A general vision transformer backbone with cross-shaped windows.

Region-aware Depth Scale Adaptation with Sparse Measurements Cswin transformer: A general vision transformer backbone with cross-shaped windows

Reference 8

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raw_fallback, observed 2026-08-06T15:54:05.640204Z

Source-reported events for the cited work

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

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Observation 9b67b58b-aa37-4e1d-8904-1e6ed37d909b · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Depth map prediction from a single image using a multi-scale deep net- work

Reference 9

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Source-reported events for the cited work

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

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Observation 60bf02d4-0336-4c8b-90fb-1a2282ca2432 · outbound

This paper cites Collaborative three-dimensional completion of color and depth in a specified area with superpixels.

Region-aware Depth Scale Adaptation with Sparse Measurements Collaborative three-dimensional completion of color and depth in a specified area with superpixels

Reference 10

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

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Observation f2ccf994-945f-46e3-886e-be26cd2c98b9 · outbound

This paper cites Con- trastive learning for depth prediction.

Region-aware Depth Scale Adaptation with Sparse Measurements Con- trastive learning for depth prediction

Reference 11

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Source-reported events for the cited work

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

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Observation a46f72ab-7ca3-4cdd-8f48-6b11571bccef · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Deep ordinal regression net- work for monocular depth estimation

Reference 12

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Source-reported events for the cited work

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

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Observation 252a6bee-fea7-447f-9792-2e0f1202df77 · outbound

This paper cites Vision meets robotics: The kitti dataset.

Region-aware Depth Scale Adaptation with Sparse Measurements Vision meets robotics: The kitti dataset

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c0e3244c-e54f-48ac-b1c7-c688e755d27c · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Digging into self-supervised monocular depth estimation

Reference 14

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raw_fallback, observed 2026-08-06T15:54:04.717841Z

Source-reported events for the cited work

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

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Observation 98098571-2293-4ee2-b828-559dda9b4c39 · outbound

This paper cites DepthFM: Fast Monocular Depth Estimation with Flow Matching.

Region-aware Depth Scale Adaptation with Sparse Measurements DepthFM: Fast Monocular Depth Estimation with Flow Matching

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:57.548037Z digest=sha256:230b66c6a8f916b92a1afb961f14a6f77a0ba70a9104ecc8c88e42bd48c7d59b

Observation 77d1c9a8-c3b2-400f-8b7d-474a01b13429 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:57.619782Z digest=sha256:423e137599aa7367fc99de3a0a4bcff83693467546a09525e21a03db5980e2da

Observation 5b03a3e9-5d9c-4652-ae2e-3cda3bae98d0 · outbound

This paper cites Oneformer: One transformer to rule universal image segmentation.

Region-aware Depth Scale Adaptation with Sparse Measurements Oneformer: One transformer to rule universal image segmentation

Reference 17

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Source-reported events for the cited work

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

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Observation 97e2a04b-222f-464c-ad9b-ebbd1f34e52d · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 18

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Source-reported events for the cited work

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

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Observation f6e1baf8-4598-480b-8aae-a85009ffc140 · outbound

This paper cites Segment any- thing.

Region-aware Depth Scale Adaptation with Sparse Measurements Segment any- thing

Reference 19

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Source-reported events for the cited work

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

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Observation 0bc9fa07-1787-42e8-97dd-a52b7192742a · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 21783758-e2bb-4bae-b35c-eb067f8a380c · outbound

This paper cites Patch-wise attention network for monocular depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Patch-wise attention network for monocular depth estimation

Reference 21

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raw_fallback, observed 2026-08-06T15:54:03.669764Z

Source-reported events for the cited work

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

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Observation 81194e16-d27c-4389-86d7-bf6d937cdb6b · outbound

This paper cites Segment and recognize anything at any granularity.

Region-aware Depth Scale Adaptation with Sparse Measurements Segment and recognize anything at any granularity

Reference 22

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:58.031561Z digest=sha256:72d5b1edf0b31c620705eb3d324e96e3dba4fac1784d533688fb81269276ffea

Observation da89f39f-e539-4bd1-a3a5-9805990d3a92 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

Reference 23

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Unavailable: canonical work link unavailable.

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Observation 1ae75ae6-2772-4761-b172-0f9820048658 · outbound

This paper cites Prompting depth anything for 4k resolution accurate metric depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Prompting depth anything for 4k resolution accurate metric depth estimation

Reference 24

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raw_fallback, observed 2026-08-06T15:54:03.196413Z

Source-reported events for the cited work

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

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Observation 0d195401-4eaa-4882-95bf-5a20e927f715 · outbound

This paper cites VA-DepthNet: A Variational Approach to Single Image Depth Prediction.

Region-aware Depth Scale Adaptation with Sparse Measurements VA-DepthNet: A Variational Approach to Single Image Depth Prediction

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:58.237570Z digest=sha256:1e7a5cb0da4e3714d59a52015696fa969345363f714a424b0b52d35ccb42f64d

Observation 96b91e23-1147-431f-b103-01070731d6f0 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Region-aware Depth Scale Adaptation with Sparse Measurements Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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no resolver link, observed 2026-08-06T15:53:58.312756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:58.312756Z digest=sha256:1df09fb310f277de48a8d76882ea420bc9140d1e8f07e271f552601f1e8b391c

Observation 1938e32d-d01d-44b7-aa96-2152ec048fe5 · outbound

This paper cites Foundation models meet low-cost sensors: Test-time adaptation for rescaling disparity for zero-shot metric depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Foundation models meet low-cost sensors: Test-time adaptation for rescaling disparity for zero-shot metric depth estimation

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:58.370882Z digest=sha256:be6f2605081cfbc5339c4465d020c3de370ce45de9e60fb5e0fc1e6c9d53bc97

Observation aeb40b71-6620-4106-b932-7f2718afcb23 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Depth prompting for sensor-agnostic depth estimation

Reference 28

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raw_fallback, observed 2026-08-06T15:54:03.060926Z

Source-reported events for the cited work

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

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Observation e9df69c1-eb1d-4189-b294-a6056af4318e · outbound

This paper cites Unidepth: Universal monocular metric depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Unidepth: Universal monocular metric depth estimation

Reference 29

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raw_fallback, observed 2026-08-06T15:54:02.868822Z

Source-reported events for the cited work

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

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Observation 0ba91b5a-9e7a-4b4a-b4b0-d9091eddd489 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

Reference 30

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raw_fallback, observed 2026-08-06T15:54:02.714597Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:58.562602Z digest=sha256:22c18662b17b0e0580d8779e563e50801dda8bd5130ca0d8250fd01e292ee755

Observation 220e8882-56bd-4de2-a8be-1a548e44ca35 · outbound

This paper cites Vi- sion transformers for dense prediction.

Region-aware Depth Scale Adaptation with Sparse Measurements Vi- sion transformers for dense prediction

Reference 31

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raw_fallback, observed 2026-08-06T15:54:02.538206Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:58.646046Z digest=sha256:6d36bbbf1d0fe87f4a761cece341bbed1d807fbc1237ec2fa287f7ca6cf2a18c

Observation 95f17d12-b417-4efd-b3f5-9fc256cd8885 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Region-aware Depth Scale Adaptation with Sparse Measurements SAM 2: Segment Anything in Images and Videos

Reference 32

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no resolver link, observed 2026-08-06T15:53:58.703295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:58.703295Z digest=sha256:3d7cf76665e405da5e13ef037aa0901939fd531214b3e28356afe73d2593513e

Observation cd785e17-5538-4b64-bd75-04fd820d83a2 · outbound

This paper cites Nddepth: Normal-distance as- sisted monocular depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Nddepth: Normal-distance as- sisted monocular depth estimation

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-06T15:54:02.316175Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:58.781137Z digest=sha256:63b38cb47bdae05cbb554d4bb09d8406522c3cc6be8860285d4c82f55cf1c2f4

Observation 38d44456-c604-43f7-a9db-8df0d9cb8195 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Indoor segmentation and support inference from rgbd images

Reference 34

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raw_fallback, observed 2026-08-06T15:54:02.181871Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:58.853803Z digest=sha256:30022bd046fde576377462ab9bb40aa37f77858af8acee3eda2671e3c01341f7

Observation 48cff162-41ce-4c58-9a9d-74a203a106e9 · outbound

This paper cites Pdc: piecewise depth completion utilizing superpixels.

Region-aware Depth Scale Adaptation with Sparse Measurements Pdc: piecewise depth completion utilizing superpixels

Reference 35

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raw_fallback, observed 2026-08-06T15:54:01.942947Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:58.925429Z digest=sha256:a997d61b531bb71eb9a45fa66532d9b220f53cc6ef9c59c9d538b521b8bc32c0

Observation ba4ec384-1b10-4a51-8909-4af3f1e1d9cd · outbound

This paper cites DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain.

Region-aware Depth Scale Adaptation with Sparse Measurements DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:54:00.909432Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:59.003349Z digest=sha256:1b949314d0d66e1f4b75b6c70b08633189afb1e8934952cd48edfb4cdc27d388

Observation 43a96e50-9a4c-46b6-aa01-daba603e295f · outbound

This paper cites Unsupervised depth completion from visual iner- tial odometry.

Region-aware Depth Scale Adaptation with Sparse Measurements Unsupervised depth completion from visual iner- tial odometry

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:54:01.853758Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:59.086152Z digest=sha256:95bdd8e6c088caff77d91589b0fe1e03b3f50c77c1d96d246d59d4616f5b93b0

Observation 13bec1f2-df4b-4f55-810e-d71d3458ea46 · outbound

This paper cites Toward practical monocular in- door depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Toward practical monocular in- door depth estimation

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:54:01.739609Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:59.141200Z digest=sha256:340f70a464e49d38d55cf4c0f56b0028345ad81cbd895acb990e17ac8277dde7

Observation 5e2cbf5a-6a89-45f6-acf3-00868566ffe7 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Region-aware Depth Scale Adaptation with Sparse Measurements Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T15:53:59.233259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:59.233259Z digest=sha256:984fde893821f6a1d42bf6ef1e5491c783679a242a88f338791e49a3799f7fad

Observation 9e0bdc33-5d06-4bc7-80e7-c03057ede8e9 · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Depth anything: Unleashing the power of large-scale unlabeled data

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:54:01.630447Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:59.352975Z digest=sha256:7e98f124da7b837f95cbc36584e8f78d657e1ac8df46034845b50023220728f0

Observation 64b4548a-5e9c-4a3e-89d6-a89a8046b7b6 · outbound

This paper cites Depth Anything V2.

Region-aware Depth Scale Adaptation with Sparse Measurements Depth Anything V2

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T15:53:59.446167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:59.446167Z digest=sha256:98b6ebcc5852d5a79758247fc43d329fde674281af414949ef6c4143e7db6977

Observation 277f2f41-59a7-4ea7-87c7-dff1a0ac6ae5 · outbound

This paper cites En- forcing geometric constraints of virtual normal for depth pre- diction.

Region-aware Depth Scale Adaptation with Sparse Measurements En- forcing geometric constraints of virtual normal for depth pre- diction

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:54:01.518256Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:59.533773Z digest=sha256:3fd41d929ce080ff1ff5ee845aff7c23bad8185cc4dd6d4b6eb8d9d9618d875a

Observation 68b9ae10-39dd-4009-880a-cea3826e79fb · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Learning to recover 3d scene shape from a single image

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T15:53:59.654960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:59.654960Z digest=sha256:89e670728902a42ad4881bedd20e4b5fd18f73043b2c4bc89e10e2b7eaab24c8

Observation 7bcea4f5-2bfb-49e7-97a3-128e24a836e3 · outbound

This paper cites Neural window fully-connected crfs for monocu- lar depth estimation.

Region-aware Depth Scale Adaptation with Sparse Measurements Neural window fully-connected crfs for monocu- lar depth estimation

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:54:01.368505Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:53:59.760918Z digest=sha256:85a5bd985165a3eb8365d7930aa0ccfbd2453c965c5c2fc217c58519fb436650

Observation 890df97b-4fc9-44f2-a7e2-b59168ffe25f · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T15:53:59.913954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:59.913954Z digest=sha256:a00ff535dc728f15749e83666f387170f3d779d381b8337f597496567084a17b

Observation cfe4b4b9-9854-4488-b73d-f945fca5730d · outbound

This paper cites RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions.

Region-aware Depth Scale Adaptation with Sparse Measurements RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T15:54:00.041442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:54:00.041442Z digest=sha256:15c6f39a0a404dacdbe8a8061ace34b5584ff82408d9862f93d8ca33f487e9b5

Observation 1bdd8161-c1a7-4d95-b796-2b984493797e · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Region-aware Depth Scale Adaptation with Sparse Measurements Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T15:54:00.193812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:54:00.193812Z digest=sha256:4831bbf672acd77916888ff648fa6ace885b43cbd29e2ed6b6901db4e427718a

Observation b4fd3d60-3027-4b9e-8a3f-0a71c9e7ba7b · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Region-aware Depth Scale Adaptation with Sparse Measurements Personalize Segment Anything Model with One Shot

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T15:54:00.331859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:54:00.331859Z digest=sha256:e905c1200ba11e9a4f3d975d0ca0d786bf71ce0cebcb81db18d22ee97158d51e

Observation 24f13e7c-9e36-4ba7-9b46-e18bd78555a1 · outbound

This paper cites Metric from human: Zero-shot monoc- ular metric depth estimation via test-time adaptation.

Region-aware Depth Scale Adaptation with Sparse Measurements Metric from human: Zero-shot monoc- ular metric depth estimation via test-time adaptation

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:54:01.177162Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T15:54:00.419964Z digest=sha256:9d377c5ed66129f68afbf143a071d8132670e6fd3ee9792c9b9d8f858cf8fb24

Observation 9646c24a-67c8-4e3b-a975-fa09da48e88b · outbound

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

Region-aware Depth Scale Adaptation with Sparse Measurements Scaledepth: Decomposing metric depth estimation into scale prediction and relative depth estimation

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T15:54:00.544751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:54:00.544751Z digest=sha256:916530159eeace9d8947b28075a1d3951c87bdf018ba01489ed23f8f4a637c91

Pith citing papers

No inbound Pith citation observations are available.