Paper Citation Record · LEDGER

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

As of 14 August 2026, this Paper Citation Record lists 100 of 139 outbound references and 4 inbound Pith citation observations for arXiv:2601.22054.

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

pith.paper-citation-record.v1
2601.22054 v2

Coverage vector

measured 100 of 139 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:50:25.905855Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T06:16:26.106940Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:45:40.359072Z

Reference resolution

100 of 139 outbound references displayed

verified exact0
verified fuzzy0
unresolved100
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0b9b87d9-24ce-42ab-b974-e0aabcab792d · outbound

This paper cites Mapillary planet-scale depth dataset.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Mapillary planet-scale depth dataset

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:15.409504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 671b749a-601c-49b6-ad85-6ab6dfe466f6 · outbound

This paper cites Apollo synthetic dataset, 2019.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Apollo synthetic dataset, 2019

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:15.444939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 169a0e5b-1db5-4abb-86c2-3ca6849c0ec8 · outbound

This paper cites Qwen2.5-VL Technical Report.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Qwen2.5-VL Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:15.535843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b69636d6-2c1a-414c-be7c-5fc5e2f5f704 · outbound

This paper cites ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:15.640786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaf88ebd-8255-47f1-89a1-1f9402b7355c · outbound

This paper cites Uasol, a large-scale high-resolution outdoor stereo dataset.Scientific data, 6(1):162, 2019.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Uasol, a large-scale high-resolution outdoor stereo dataset.Scientific data, 6(1):162, 2019

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:15.722833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5ba6f2d-c9f5-414b-8272-658d25f1d616 · outbound

This paper cites Adabins: Depth estimation using adaptive bins.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Adabins: Depth estimation using adaptive bins

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:15.843694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1cd4763-d37c-461b-90b6-95d40993f5aa · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Localbins: Improving depth estimation by learning local distributions

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:15.964752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf9f7ddd-0339-412c-a391-4faea3fb8491 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Zoedepth: Zero-shot transfer by combining relative and metric depth, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.176980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eed6ea3-5efc-4ba7-a8a8-02cba7421a0b · outbound

This paper cites 3d cavla: Leveraging depth and 3d context to generalize vision language action models for unseen tasks.arXiv preprint arXiv:2505.05800, 2025.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources 3d cavla: Leveraging depth and 3d context to generalize vision language action models for unseen tasks.arXiv preprint arXiv:2505.05800, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.373035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f99e1e48-da21-4ea3-b2c9-447de1cd504b · outbound

This paper cites Richter, and Vladlen Koltun.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Richter, and Vladlen Koltun

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.491662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b4fd006-f366-4867-9ff4-e2543f35af94 · outbound

This paper cites A naturalistic open source movie for optical flow evaluation.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources A naturalistic open source movie for optical flow evaluation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.660992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89c1e0ad-4419-4324-b778-bf182ca22058 · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources nuscenes: A multimodal dataset for autonomous driving

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.741854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5658bcff-3a6d-4ea5-993e-f50050af4386 · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources WorldVLA: Towards Autoregressive Action World Model

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.813009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b0b49c5-6f02-470e-94a7-d244d5ac99d4 · outbound

This paper cites Matterport3d: Learning from rgb-d data in indoor environments.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Matterport3d: Learning from rgb-d data in indoor environments

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.899414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68edc4b0-bd62-4e48-815a-1a24dcc27500 · outbound

This paper cites Single-image depth perception in the wild.Advances in neural information processing systems, 29, 2016.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Single-image depth perception in the wild.Advances in neural information processing systems, 29, 2016

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:16.983852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5233b173-7f51-4dcd-a4a1-aebe2fbd61c9 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Oasis: A large- scale dataset for single image 3d in the wild

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.040717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 602b04a3-f2a2-4d00-95f8-1667aa9395d5 · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual- linguistic tasks.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Internvl: Scaling up vision foundation models and aligning for generic visual- linguistic tasks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.156826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c465bd8-f1d1-43a7-8e5b-29ddf5833b8a · outbound

This paper cites Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.316977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e40f5a6f-e565-4cf6-bf14-6d08aef4f74c · outbound

This paper cites Learning depth with convolutional spatial propagation network.TPAMI, 2019.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Learning depth with convolutional spatial propagation network.TPAMI, 2019

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.426107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b285be4a-2d20-47d0-a35c-81da53b87c0d · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources The cityscapes dataset for semantic urban scene understanding

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.471294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33e936d5-f433-433f-a955-6110e1098352 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.604744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e105298c-93e0-4d97-893d-5aa5fc8c8f00 · outbound

This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher Ré.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Fu, Stefano Ermon, Atri Rudra, and Christopher Ré

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.703430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8caf3cd9-d25f-4386-8a66-90d3a9b2d72d · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.ICLR, 2021.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources An image is worth 16x16 words: Transformers for image recognition at scale.ICLR, 2021

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.777262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83382cde-fe08-4f52-9e79-42655cd0be00 · outbound

This paper cites Predicting depth, surface normals and semantic labels with a common multi- scale convolutional architecture.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Predicting depth, surface normals and semantic labels with a common multi- scale convolutional architecture

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.867311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 992f3d03-bad0-4714-9c40-ac0aef6a5133 · outbound

This paper cites Depth map prediction from a single image using a multi- scale deep network.Advances in neural information processing systems, 27, 2014.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Depth map prediction from a single image using a multi- scale deep network.Advances in neural information processing systems, 27, 2014

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:17.967602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82148395-ffb5-4145-83e9-270d9c80c1fc · outbound

This paper cites Mid-air: A multi-modal dataset for extremely low altitude drone flights.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Mid-air: A multi-modal dataset for extremely low altitude drone flights

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.081203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cff9005-9c53-4b15-963e-9590a5643ef8 · outbound

This paper cites Deep ordinal regres- sion network for monocular depth estimation.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Deep ordinal regres- sion network for monocular depth estimation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.169044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac30d20f-ca73-4df8-a293-613df0a337ae · outbound

This paper cites Virtual worlds as proxy for multi-object tracking analysis.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Virtual worlds as proxy for multi-object tracking analysis

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.342488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f829107-7c24-4a5e-b03c-6c3b73270b23 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources R4dyn: Exploring radar for self-supervised monocular depth estimation of dynamic scenes

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.390705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4996d46c-4247-4b74-8dbf-c66a8a54abd5 · outbound

This paper cites Dsec: A stereo event camera dataset for driving scenarios.IEEE Robotics and Automation Letters, 6(3):4947–4954, 2021.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Dsec: A stereo event camera dataset for driving scenarios.IEEE Robotics and Automation Letters, 6(3):4947–4954, 2021

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.487589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f247cbf-8d83-4d14-92ac-5094b93ad3a3 · outbound

This paper cites Are we ready for autonomous driving.The KITTI vision benchmark suite.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Are we ready for autonomous driving.The KITTI vision benchmark suite

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.576276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 803ac0dc-f13d-4bf6-b034-f07135bc3d79 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Digging into self-supervised monocular depth estimation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.732340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fd4e4af-cdbc-454a-adc2-fac2b333ceec · outbound

This paper cites All for one, and one for all: Urbansyn dataset, the third musketeer of synthetic driving scenes.Neurocomputing, 637:130038, 2025.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources All for one, and one for all: Urbansyn dataset, the third musketeer of synthetic driving scenes.Neurocomputing, 637:130038, 2025

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.888403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ce68659-f048-4f12-a9ef-842115a951a2 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources 3d packing for self- supervised monocular depth estimation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:18.991321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d763bcd-7604-4290-85e8-edfc9995381e · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Towards zero-shot scale- aware monocular depth estimation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:19.275100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33f9032f-4305-4a8c-991e-093475876969 · outbound

This paper cites One Thousand and One Hours: Self-driving Motion Prediction Dataset.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources One Thousand and One Hours: Self-driving Motion Prediction Dataset

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:19.379655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3df8fbd-dc06-42a7-ad98-afcad0ab1e19 · outbound

This paper cites an unresolved cited work.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:19.534895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af79a83f-36a9-4fd3-8793-92bff4195851 · outbound

This paper cites ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:19.646504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afa3bcb0-d33a-4c0d-b2db-02560f51191a · outbound

This paper cites Deepmvs: Learning multi-view stereopsis.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Deepmvs: Learning multi-view stereopsis

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:19.764848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c16d699b-14ee-4744-9158-966e23314739 · outbound

This paper cites NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:19.954736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0562eaba-0f2b-4b0e-8b05-25fcfd523f1a · outbound

This paper cites GPT-4o System Card.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources GPT-4o System Card

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.034739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f5a9b5d-79eb-4469-a937-04848eb95275 · outbound

This paper cites Perspective fields for single image camera calibration.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Perspective fields for single image camera calibration

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.112957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03ea1b76-5801-4294-b4f0-ad62eb608131 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Repurposing diffusion-based image generators for monocular depth estimation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.185658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afc43c63-e846-4dd7-a617-978ad8855d52 · outbound

This paper cites MapAnything: Universal Feed-Forward Metric 3D Reconstruction.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources MapAnything: Universal Feed-Forward Metric 3D Reconstruction

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.258449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65728406-6aed-4993-8eab-47e1342af673 · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources OpenVLA: An Open-Source Vision-Language-Action Model

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.317875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ae91015-3835-4e46-9f20-cfae69039cc2 · outbound

This paper cites Segment anything.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Segment anything

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.453626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd8695e7-7cd6-4aaa-8c99-0850ccb081b7 · outbound

This paper cites Evaluation of cnn-based single- image depth estimation methods.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Evaluation of cnn-based single- image depth estimation methods

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.539440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a037025-deed-481c-b34a-05ab21461645 · outbound

This paper cites Pulling things out of perspective.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Pulling things out of perspective

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.597544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad70ca40-903f-4b65-a076-02ef42233fcc · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Deeper depth prediction with fully convolutional residual networks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.689475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e3781d9-bde6-4ae5-8ed2-6bb998a3044d · outbound

This paper cites MolmoAct: Action Reasoning Models that can Reason in Space.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources MolmoAct: Action Reasoning Models that can Reason in Space

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.765971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc654eac-d8fa-4158-9ff4-499018458e92 · outbound

This paper cites Grounding image matching in 3d with mast3r.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Grounding image matching in 3d with mast3r

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.834831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bba93498-c49e-4ada-ab89-7aca288dd47b · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources LLaVA-OneVision: Easy Visual Task Transfer

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.909404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 703fdc8c-b012-4d00-b0c5-60c62405500c · outbound

This paper cites Radarcam-depth: Radar-camera fusion for depth estimation with learned metric scale.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Radarcam-depth: Radar-camera fusion for depth estimation with learned metric scale

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:20.974859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41e1c7fc-f633-4bea-bb5c-bd11c35873b3 · outbound

This paper cites Sparse beats dense: Rethinking supervision in radar-camera depth completion.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Sparse beats dense: Rethinking supervision in radar-camera depth completion

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:21.073279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1158a2be-f2f8-4dc3-b8b6-2ceec29ce052 · outbound

This paper cites Matrixcity: A large-scale city dataset for city-scale neural rendering and beyond.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Matrixcity: A large-scale city dataset for city-scale neural rendering and beyond

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:21.196071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fc01bca-25fb-4dee-82cf-c84c348995e7 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Megadepth: Learning single-view depth prediction from internet photos

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:21.298085Z

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Observation 1cc349c2-4c3b-4b67-b259-4c301fb34d45 · outbound

This paper cites Patchfusion: An end-to-end tile-based framework for high-resolution monocular metric depth estimation.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Patchfusion: An end-to-end tile-based framework for high-resolution monocular metric depth estimation

Reference 58

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Observation dcd18594-38e8-4e24-a5e3-42f1ef6719d4 · outbound

This paper cites Depth Anything 3: Recovering the Visual Space from Any Views.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Depth Anything 3: Recovering the Visual Space from Any Views

Reference 59

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Source-reported events for the cited work

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Observation dd2d8df1-3997-41e7-a376-a1b7c2d5736d · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Prompting depth anything for 4k resolution accurate metric depth estimation

Reference 60

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Observation 9c4a9b03-34ef-4e85-b9f4-aadb13701a0d · outbound

This paper cites Vila: On pre-training for visual language models.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26679–26689, 2023.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Vila: On pre-training for visual language models.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26679–26689, 2023

Reference 61

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Source-reported events for the cited work

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Observation e96992eb-d19d-4c80-b3a4-a4a3cbabd434 · outbound

This paper cites Depth estimation from monocular images and sparse radar data.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Depth estimation from monocular images and sparse radar data

Reference 62

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Source-reported events for the cited work

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Observation 2efcc26c-5a29-4ac4-9bdc-ec76d683fe1b · outbound

This paper cites Libero: Bench- marking knowledge transfer for lifelong robot learning.Advances in Neural Information Processing Sys- tems, 36:44776–44791, 2023.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Libero: Bench- marking knowledge transfer for lifelong robot learning.Advances in Neural Information Processing Sys- tems, 36:44776–44791, 2023

Reference 63

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Observation 9f0a8311-59e5-4d31-bd42-81c154a2b466 · outbound

This paper cites Depthlab: From partial to complete.arXiv preprint arXiv:2412.18153, 2024.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Depthlab: From partial to complete.arXiv preprint arXiv:2412.18153, 2024

Reference 64

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Observation e231b1ca-a92f-48d2-ac33-aa84cb07cb9b · outbound

This paper cites Depth estimation from monocular images and sparse radar using deep ordinal regression network.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Depth estimation from monocular images and sparse radar using deep ordinal regression network

Reference 65

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Source-reported events for the cited work

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Observation e2b52a09-ef91-484d-915a-1381dee8ff21 · outbound

This paper cites Rcdpt: Radar-camera fusion dense prediction transformer.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Rcdpt: Radar-camera fusion dense prediction transformer

Reference 66

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Source-reported events for the cited work

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Observation 37467fb8-5a39-4935-a893-87fb0936a055 · outbound

This paper cites Radar-camera pixel depth association for depth completion.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Radar-camera pixel depth association for depth completion

Reference 67

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Source-reported events for the cited work

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Observation e818e544-f198-4c9d-8818-38a977c8169b · outbound

This paper cites You see it, you got it: Learning 3d creation on pose-free videos at scale.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources You see it, you got it: Learning 3d creation on pose-free videos at scale

Reference 68

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Source-reported events for the cited work

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Observation 50ee0b70-d170-4147-9658-ce1c2ad70f92 · outbound

This paper cites Spring: A high- resolution high-detail dataset and benchmark for scene flow, optical flow and stereo.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Spring: A high- resolution high-detail dataset and benchmark for scene flow, optical flow and stereo

Reference 69

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Source-reported events for the cited work

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Observation 4349758f-d64f-4060-bcfe-8296844e562a · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources DINOv2: Learning Robust Visual Features without Supervision

Reference 70

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Observation d0157747-8559-4b2b-9fa1-dc2e693e22d2 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Depth prompting for sensor-agnostic depth estimation

Reference 71

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Source-reported events for the cited work

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Observation e56f5475-a9fb-475b-a236-1c2f77b735ac · outbound

This paper cites Scalable diffusion models with transformers.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Scalable diffusion models with transformers

Reference 72

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Observation 9c8d4159-35f2-4fb4-848f-eb7f6e40c2e7 · outbound

This paper cites FAST: Efficient Action Tokenization for Vision-Language-Action Models.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 73

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d8622b5-ba42-4923-92f2-71cae114caec · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler

Reference 74

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Source-reported events for the cited work

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Observation 6d7f6894-3d7a-492b-b415-2d2205a7404b · outbound

This paper cites Unidepth: Universal monocular metric depth estimation.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Unidepth: Universal monocular metric depth estimation

Reference 75

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Source-reported events for the cited work

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Observation f475612a-16e3-40b6-ad7f-c120a4c0ebef · outbound

This paper cites SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model

Reference 76

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Source-reported events for the cited work

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Observation 30b0b3cf-35d7-4328-a3c5-1de00442289c · outbound

This paper cites Learning transferable visual models from natural language supervision.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Learning transferable visual models from natural language supervision

Reference 77

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Source-reported events for the cited work

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Observation add59cf4-0a10-4304-9171-84a0f8de3a0c · outbound

This paper cites Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:23.343293Z

Source-reported events for the cited work

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Observation c02b3b95-d226-4c00-b60e-e8a9cc213b04 · outbound

This paper cites Zero-shot text-to-image generation.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Zero-shot text-to-image generation

Reference 79

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Source-reported events for the cited work

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Observation 7c8632c3-6117-4f8b-bfb5-5d3de91f25a8 · outbound

This paper cites Booster: a benchmark for depth from images of specular and transparent surfaces.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):85–102, 2023.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Booster: a benchmark for depth from images of specular and transparent surfaces.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):85–102, 2023

Reference 80

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Source-reported events for the cited work

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Observation 445c3306-4f12-4bff-9c4d-10166b56fa25 · outbound

This paper cites Vision transformers for dense prediction.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Vision transformers for dense prediction

Reference 81

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25c368cc-f6a5-4b82-b1a3-11903c600d62 · outbound

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MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Unresolved cited work

Reference 82

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Source-reported events for the cited work

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Observation de376240-bf18-45fe-bdda-669292b73810 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 83

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Source-reported events for the cited work

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Observation 644c208e-0544-44ed-ba73-e66ae211fae7 · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding

Reference 84

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Source-reported events for the cited work

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Observation d705fb3b-da33-4d45-8810-d1457db9094b · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources High-resolution image synthesis with latent diffusion models

Reference 85

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Source-reported events for the cited work

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Observation 719367b6-8e75-4ae4-b9b3-7a30b3e3d366 · outbound

This paper cites The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes

Reference 86

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Source-reported events for the cited work

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Observation a5e64b72-86c5-4b1e-9714-d7de5243b8d8 · outbound

This paper cites Numerische Isotropieoptimierung von FIR-Filtern mittels Querglättung.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Numerische Isotropieoptimierung von FIR-Filtern mittels Querglättung

Reference 87

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Source-reported events for the cited work

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Observation 41bf1b8d-aefc-4042-a366-bcc4c525cdaa · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources High-resolution stereo datasets with subpixel-accurate ground truth

Reference 88

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Observation c804757b-64f5-47e0-b243-9ef888dd2bf7 · outbound

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MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Polle- feys, and Andreas Geiger

Reference 89

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Source-reported events for the cited work

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Observation 04599f7b-38bb-4b29-aa41-a34bf404455a · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources A multi-view stereo benchmark with high-resolution images and multi-camera videos

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:24.442501Z

Source-reported events for the cited work

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Observation 2f58b818-16a4-4ec2-9496-cdc900cc406a · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Indoor segmentation and support inference from rgbd images

Reference 91

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Source-reported events for the cited work

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Observation 51e8d30c-4c30-4189-9c3d-0d338512ed80 · outbound

This paper cites DINOv3.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources DINOv3

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:24.779427Z

Source-reported events for the cited work

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Observation 64dd60e5-751d-4719-a817-e4a8c22f0937 · outbound

This paper cites Depth estimation from camera image and mmwave radar point cloud.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Depth estimation from camera image and mmwave radar point cloud

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:24.917228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0086bf5d-ce95-429d-94aa-5e73a1e9cdcf · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Sun rgb-d: A rgb-d scene understanding bench- mark suite

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-03T06:50:25.025637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b1ce239-6f95-4b7c-bda8-1d5645d8c4fa · outbound

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

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources The Replica Dataset: A Digital Replica of Indoor Spaces

Reference 95

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f13270bf-c462-4b45-a195-0d782e2bc042 · outbound

This paper cites Cafnet: A confidence-driven framework for radar camera depth estimation.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Cafnet: A confidence-driven framework for radar camera depth estimation

Reference 96

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Source-reported events for the cited work

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Observation b59ceef3-ad3f-4a57-b410-cb7644d9ee3f · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Scalability in perception for autonomous driving: Waymo open dataset

Reference 97

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Source-reported events for the cited work

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Observation c2822f6f-4d5e-43ba-b4b7-7e56b5b3b1f2 · outbound

This paper cites The bitter lesson.Incomplete Ideas (blog), 13(1):38, 2019.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources The bitter lesson.Incomplete Ideas (blog), 13(1):38, 2019

Reference 98

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Source-reported events for the cited work

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Observation 322d0f04-ffc3-41a3-91b7-0de1f9f3d26e · outbound

This paper cites Masked depth modeling for spatial perception.arXiv preprint arXiv:2601.17895, 2026.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Masked depth modeling for spatial perception.arXiv preprint arXiv:2601.17895, 2026

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Source-reported events for the cited work

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Observation cf678807-442a-437e-b537-bdf2be3530d5 · outbound

This paper cites Bilateral propagation network for depth comple- tion.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Bilateral propagation network for depth comple- tion

Reference 100

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72a3986e-1f6d-4899-8b15-fb818a8c85b2 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

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Pith citing papers

Observation dcb29b9b-c0cf-48d4-9ad7-0d074bd110f0 · inbound

Any to Full: Prompting Depth Anything for Depth Completion in One Stage cites this paper.

Any to Full: Prompting Depth Anything for Depth Completion in One Stage MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Reference 34

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Observation d4112ea8-919d-44c3-a35a-f67c52b2653a · inbound

DrivingDepth: Sparse-Prompted Pixel-wise Scale Correction for Driving Depth Estimation cites this paper.

DrivingDepth: Sparse-Prompted Pixel-wise Scale Correction for Driving Depth Estimation MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:16:03.006629Z

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Observation 9fc4ae17-572b-49ca-915d-1502717dbdc1 · inbound

X-Lens: Real-Time Metric Depth Estimation with Heterogeneous Cameras cites this paper.

X-Lens: Real-Time Metric Depth Estimation with Heterogeneous Cameras MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Reference 77

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Observation 411bc03b-dcee-4eff-a9ae-97328fe9bd38 · inbound

SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment cites this paper.

SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T00:20:07.908257Z

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