Paper Citation Record · LEDGER

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2505.00980.

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

pith.paper-citation-record.v1
2505.00980 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:34:13.635678Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T19:07:12.616928Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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verified fuzzy15
unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d7c0c6be-a3a1-4bbe-a047-9ca1f34b7781 · outbound

This paper cites Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians

Reference 1

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Observation 83cdaba9-0341-4c02-9273-d8c06181c6f1 · outbound

This paper cites Pseudo rgb- d for self-improving monocular slam and depth prediction,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Pseudo rgb- d for self-improving monocular slam and depth prediction,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:14.056297Z

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Observation 25c6e563-f3fd-4c0c-b0f2-78dced0ea0ec · outbound

This paper cites Physical 3d adversarial attacks against monocular depth estimation in autonomous driving,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Physical 3d adversarial attacks against monocular depth estimation in autonomous driving,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:14.044837Z

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Observation 8cc8276e-9b75-4bad-809e-714b15de5bb7 · outbound

This paper cites Mgnet: Monocular geo- metric scene understanding for autonomous driving,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Mgnet: Monocular geo- metric scene understanding for autonomous driving,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.498923Z

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Observation cf87d978-a6c2-4743-a910-a1934659764c · outbound

This paper cites J-mod 2: Joint monocular obstacle detection and depth estimation,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment J-mod 2: Joint monocular obstacle detection and depth estimation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:14.026639Z

Source-reported events for the cited work

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Observation 894c4061-b189-4642-a534-df04cbce47b7 · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Deeper depth prediction with fully convolutional residual networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.507239Z

Source-reported events for the cited work

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Observation 8dba9fc4-e3ba-4890-b8b1-381f9d4304ce · outbound

This paper cites Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.511221Z

Source-reported events for the cited work

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Observation fcf22cf3-70ea-4794-8e27-2018422ea13d · outbound

This paper cites Monocular depth estimation using laplacian pyramid-based depth residuals,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Monocular depth estimation using laplacian pyramid-based depth residuals,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.515071Z

Source-reported events for the cited work

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Observation d34e7506-bc6c-4164-8733-ddb4194ff938 · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Adabins: Depth estimation using adaptive bins,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.999981Z

Source-reported events for the cited work

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Observation e5338553-a89c-402c-941d-d45ea90d226a · outbound

This paper cites Neural window fully- connected crfs for monocular depth estimation,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Neural window fully- connected crfs for monocular depth estimation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.988156Z

Source-reported events for the cited work

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Observation 91a6b5c0-fc48-4b35-b518-0fd3a5509280 · outbound

This paper cites Iebins: Itera- tive elastic bins for monocular depth estimation,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Iebins: Itera- tive elastic bins for monocular depth estimation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.975630Z

Source-reported events for the cited work

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Observation 88b39232-c3b1-41e6-97b1-d20369225f0a · outbound

This paper cites Fastdepth: Fast monocular depth estimation on embedded systems,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Fastdepth: Fast monocular depth estimation on embedded systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.962520Z

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Observation 9498d66a-d033-424d-a904-16e8e530a159 · outbound

This paper cites Efficient monocular depth estimation for edge devices in internet of things,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Efficient monocular depth estimation for edge devices in internet of things,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.948236Z

Source-reported events for the cited work

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Observation 9f4f764f-319e-46ae-9f8c-4bc2013d209d · outbound

This paper cites Lightweight monocular depth estimation through guided decoding,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Lightweight monocular depth estimation through guided decoding,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.936829Z

Source-reported events for the cited work

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Observation 0385dbd4-24f0-48bf-a9ac-2ec561d0f2c1 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 15

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Observation a03f8aa0-b109-4da9-9bed-0f9edd473c1d · outbound

This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.543778Z

Source-reported events for the cited work

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Observation c37dd97a-d313-4d33-a6b7-aaca36ad3695 · outbound

This paper cites Remam- ber: Referring image segmentation with mamba twister,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Remam- ber: Referring image segmentation with mamba twister,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.925542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 10e70c9c-5427-452d-be6c-e11dd89f060e · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 18

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Observation 7ab56837-66ba-4ca1-81a2-09b5e639ea53 · outbound

This paper cites Make3d: Learning 3d scene structure from a single still image,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Make3d: Learning 3d scene structure from a single still image,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.913299Z

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Observation 897bcd64-6f47-4e66-96ce-329625ee7bed · outbound

This paper cites Discrete-continuous depth estimation from a single image,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Discrete-continuous depth estimation from a single image,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.902392Z

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Observation a4ea9c81-120a-4f54-940a-e43e6cc18a5e · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Depth map prediction from a single image using a multi-scale deep network,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.561235Z

Source-reported events for the cited work

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Observation 891b65fc-cc14-43ed-92ee-8dca3b276f58 · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Predicting depth, surface normals and se- mantic labels with a common multi-scale convolutional architecture,

Reference 22

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Source-reported events for the cited work

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Observation fd27faf8-6e2f-4b99-baaa-b9f16f7859ad · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Towards robust monocular depth estimation: Mixing datasets for zero-shot cross- dataset transfer,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.568374Z

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Observation 338356bc-7046-4174-852a-d4793a1d31ba · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 24

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Observation aad1ba58-4be0-4684-8c56-cdbf381c43cc · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Combining recurrent, convolutional, and continuous-time models with linear state space layers,

Reference 25

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Observation 1cb60d28-c856-487d-8a97-d78227b88d47 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Efficiently Modeling Long Sequences with Structured State Spaces

Reference 26

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Source-reported events for the cited work

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Observation da4b937e-3fe6-4e32-be45-3421aa59c044 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Simplified State Space Layers for Sequence Modeling

Reference 27

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Observation b3453a79-68d9-4b3d-92fd-ebd30f79ba69 · outbound

This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 28

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Observation 9702d1c2-782a-49f1-8ffa-c53ff2d42153 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 29

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Observation f9ae2928-36a2-437f-a1ac-594625c41c51 · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,

Reference 30

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Observation 17a02526-0b89-4d82-84f4-05e984502046 · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 31

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Observation 2da4a8a0-6097-46be-86f0-c19fe3111967 · outbound

This paper cites U-shaped Vision Mamba for Single Image Dehazing.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment U-shaped Vision Mamba for Single Image Dehazing

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.600573Z

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Observation e715cc31-de5d-4e74-907e-ae8c6a0aead4 · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 33

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Observation 2cb0cbd7-36a6-4788-a84b-d878f763c74c · outbound

This paper cites Pyramid scene parsing network,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Pyramid scene parsing network,

Reference 34

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Observation ab5372e2-a658-4899-971a-2a609a4927cc · outbound

This paper cites Real-time joint semantic segmentation and depth estimation using asymmetric annotations,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Real-time joint semantic segmentation and depth estimation using asymmetric annotations,

Reference 35

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Observation cbb4e46e-60ce-4c3d-84c4-282f7973615d · outbound

This paper cites Enforcing geometric constraints of virtual normal for depth prediction,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Enforcing geometric constraints of virtual normal for depth prediction,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.841680Z

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Observation 51a68780-8b31-46db-87b4-0386e961796d · outbound

This paper cites Monocular depth distribution alignment with low computation,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Monocular depth distribution alignment with low computation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:34:13.829779Z

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Observation f51497ba-7b1c-4463-8926-848d935447dc · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Pytorch: An imperative style, high-performance deep learning library,

Reference 38

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Observation 832e0949-6bc1-45ef-9630-20c575af18b6 · outbound

This paper cites Adam: A method for stochastic optimization,.

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Adam: A method for stochastic optimization,

Reference 39

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Observation 77793639-fec5-4d50-8602-c90357bebdf0 · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Indoor segmentation and support inference from rgbd images,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.632167Z

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Observation 20855eae-03f1-4b9c-9919-618c43d7a234 · outbound

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

LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment Vision meets robotics: The kitti dataset,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T04:34:13.635678Z

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Pith citing papers

Observation 36db0408-6af5-4e00-bf1c-77340d1d2a67 · inbound

DepthART: Scaling Foundation Monocular Depth to Tiny Models cites this paper.

DepthART: Scaling Foundation Monocular Depth to Tiny Models LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T19:07:12.616928Z

Source-reported events for the cited work

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