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Paper Citation Record · LEDGER

SLAM-Former: Putting SLAM into One Transformer

As of 10 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2509.16909.

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

pith.paper-citation-record.v1
2509.16909 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:06:51.468152Z

measured 46 of 46 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:25:47.256701Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:56:29.669563Z

Reference resolution

44 of 44 outbound references displayed

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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 2e555092-5185-4d2c-b81b-51593a6de651 · outbound

This paper cites Orb- slam: A versatile and accurate monocular slam system,.

SLAM-Former: Putting SLAM into One Transformer Orb- slam: A versatile and accurate monocular slam system,

Reference 1

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source=pdf_text observed=2026-08-04T16:06:49.404237Z digest=sha256:d78ce29c5aad9fca2c44080c98fa5901b9d16dfc5156baa3ef1b06cb0208a2ce

Observation 10243952-d24e-41a9-b03b-af8c5901eaea · outbound

This paper cites Lsd-slam: Large-scale direct monocular slam,.

SLAM-Former: Putting SLAM into One Transformer Lsd-slam: Large-scale direct monocular slam,

Reference 2

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Observation 3e923cbb-01c4-4f88-b065-48bf3928ce88 · outbound

This paper cites One billion points in the cloud–an octree for efficient processing of 3d laser scans,.

SLAM-Former: Putting SLAM into One Transformer One billion points in the cloud–an octree for efficient processing of 3d laser scans,

Reference 3

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Observation bef30888-50c3-4f8d-927e-246990abed1b · outbound

This paper cites Kinectfusion: Real-time dense surface map- ping and tracking,.

SLAM-Former: Putting SLAM into One Transformer Kinectfusion: Real-time dense surface map- ping and tracking,

Reference 4

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Observation a348ec34-9768-45ab-8576-667abfa03577 · outbound

This paper cites Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,.

SLAM-Former: Putting SLAM into One Transformer Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,

Reference 5

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source=pdf_text observed=2026-08-04T16:06:49.692675Z digest=sha256:c3c432165e9e506f8b52e3d38e05212fc61e35397702cb509a9e96e6a3aeb484

Observation e1747c82-dea6-46cd-acc8-adabdde53348 · outbound

This paper cites Scenefactory: A workflow-centric and unified framework for incremental scene modeling,.

SLAM-Former: Putting SLAM into One Transformer Scenefactory: A workflow-centric and unified framework for incremental scene modeling,

Reference 6

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Observation 0f64b2da-6113-4436-9052-aacf774ad507 · outbound

This paper cites Mast3r-slam: Real-time dense slam with 3d reconstruction priors,.

SLAM-Former: Putting SLAM into One Transformer Mast3r-slam: Real-time dense slam with 3d reconstruction priors,

Reference 7

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Observation f4c34eb2-bea5-46b7-af73-8681217efd3f · outbound

This paper cites Vggt-slam: Dense rgb slam optimized on the sl(4) manifold,.

SLAM-Former: Putting SLAM into One Transformer Vggt-slam: Dense rgb slam optimized on the sl(4) manifold,

Reference 8

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source=pdf_text observed=2026-08-04T16:06:49.910299Z digest=sha256:c052e7831b863344d68a1ce820da9625f8b6e95c31ebc9a9bee5aaddce5b6e03

Observation b7867f40-e48b-4904-9a8b-ce988e609bc8 · outbound

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

SLAM-Former: Putting SLAM into One Transformer Dust3r: Geometric 3d vision made easy,

Reference 9

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source=pdf_text observed=2026-08-04T16:06:49.964135Z digest=sha256:b50cacdbf204d97a8e15e30375c11faf7e3660ba10639589dcfa08a1d6521afa

Observation 29d3b7fe-49bc-4fe9-a5ba-c353792b244c · outbound

This paper cites Vggt: Visual geometry grounded transformer,.

SLAM-Former: Putting SLAM into One Transformer Vggt: Visual geometry grounded transformer,

Reference 10

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Observation e0197dd6-7846-4cb8-ba3e-082951b898f1 · outbound

This paper cites Streaming 4d visual geometry transformer,.

SLAM-Former: Putting SLAM into One Transformer Streaming 4d visual geometry transformer,

Reference 11

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Observation 620530b2-46b0-4e11-bed6-b7fdf39e7f96 · outbound

This paper cites Stream3r: Scalable sequen- tial 3d reconstruction with causal transformer,.

SLAM-Former: Putting SLAM into One Transformer Stream3r: Scalable sequen- tial 3d reconstruction with causal transformer,

Reference 12

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Observation 3c6695b3-d9c3-48c2-b7a1-24c2be7a2e81 · outbound

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

SLAM-Former: Putting SLAM into One Transformer Nicer-slam: Neural implicit scene encoding for rgb slam,

Reference 13

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Observation aaaaa258-a1c8-45dc-9890-9aeb8a970bfb · outbound

This paper cites Codeslam - learning a compact, optimisable representation for dense visual slam,.

SLAM-Former: Putting SLAM into One Transformer Codeslam - learning a compact, optimisable representation for dense visual slam,

Reference 14

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Observation 4a7c6fb8-c734-46de-8897-8721ec5e22ea · outbound

This paper cites Deepfactors: Real-time probabilistic dense monocu- lar slam,.

SLAM-Former: Putting SLAM into One Transformer Deepfactors: Real-time probabilistic dense monocu- lar slam,

Reference 15

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source=pdf_text observed=2026-08-04T16:06:50.365427Z digest=sha256:fd4b5c682a5797521d52ec6b3900c58953f5ce9b85dfb0644e2746052e71fced

Observation b407af49-2730-4960-90a0-9d22885283c6 · outbound

This paper cites Mvsnet: Depth inference for unstructured multi-view stereo,.

SLAM-Former: Putting SLAM into One Transformer Mvsnet: Depth inference for unstructured multi-view stereo,

Reference 16

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Observation 3eb9c1d8-7976-4577-94fd-59d59550802c · outbound

This paper cites Tandem: Tracking and dense mapping in real-time using deep multi- view stereo,.

SLAM-Former: Putting SLAM into One Transformer Tandem: Tracking and dense mapping in real-time using deep multi- view stereo,

Reference 17

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Observation ffd0e1b0-567c-48e6-b3c5-2624d2fd2809 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis,.

SLAM-Former: Putting SLAM into One Transformer Nerf: Representing scenes as neural radiance fields for view synthesis,

Reference 18

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Observation 537c6d69-dbef-4a52-80b3-94259e674b26 · outbound

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

SLAM-Former: Putting SLAM into One Transformer 3d gaussian splatting for real-time radiance field rendering,

Reference 19

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Observation 8f337d70-6ea7-4b22-ade6-a9e632d9ec45 · outbound

This paper cites Nerf-slam: Real- time dense monocular slam with neural radiance fields,.

SLAM-Former: Putting SLAM into One Transformer Nerf-slam: Real- time dense monocular slam with neural radiance fields,

Reference 20

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Observation 510a1280-760c-45dc-970c-0cc21fc12711 · outbound

This paper cites Gs-slam: Dense visual slam with 3d gaussian splatting,.

SLAM-Former: Putting SLAM into One Transformer Gs-slam: Dense visual slam with 3d gaussian splatting,

Reference 21

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source=pdf_text observed=2026-08-04T16:06:50.914767Z digest=sha256:0cd607708b7aeae0b460b04ea2c803a5b55f0f61887f8394648546161c07bc34

Observation f8a2ba70-fd82-43e0-8d2e-51c206bf3a20 · outbound

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

SLAM-Former: Putting SLAM into One Transformer Grounding image matching in 3d with mast3r,

Reference 22

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source=pdf_text observed=2026-08-04T16:06:51.065601Z digest=sha256:c31301408281a9cffa533854eb49047c8e210129227a68fb88d6c2f9c654d54a

Observation 18ca41b5-d227-4aa5-9b17-13c415dfd30a · outbound

This paper cites Fast3r: Towards 3d recon- struction of 1000+ images in one forward pass,.

SLAM-Former: Putting SLAM into One Transformer Fast3r: Towards 3d recon- struction of 1000+ images in one forward pass,

Reference 23

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Observation a81503d3-ad4b-429c-955d-1efe0f5ccd5c · outbound

This paper cites π 3: Scalable permutation- equivariant visual geometry learning,.

SLAM-Former: Putting SLAM into One Transformer π 3: Scalable permutation- equivariant visual geometry learning,

Reference 24

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Observation 0536bce7-9bee-458e-8b81-2a66bcf0ea59 · outbound

This paper cites 3d reconstruction with spatial memory,.

SLAM-Former: Putting SLAM into One Transformer 3d reconstruction with spatial memory,

Reference 25

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Observation 54ecab08-7b0d-4efa-a325-9dd71fb8bfa0 · outbound

This paper cites Continuous 3d perception model with per- sistent state,.

SLAM-Former: Putting SLAM into One Transformer Continuous 3d perception model with per- sistent state,

Reference 26

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Observation 2f141395-cb64-41f0-94e4-6830b69f029e · outbound

This paper cites Long3r: Long sequence streaming 3d reconstruction,.

SLAM-Former: Putting SLAM into One Transformer Long3r: Long sequence streaming 3d reconstruction,

Reference 27

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Observation 5fc5422b-7dbd-42ea-b534-80e525712875 · outbound

This paper cites Deepv2d: Video to depth with differ- entiable structure from motion,.

SLAM-Former: Putting SLAM into One Transformer Deepv2d: Video to depth with differ- entiable structure from motion,

Reference 28

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Observation c687eb76-8256-48cd-b0be-45fd66020898 · outbound

This paper cites Deep patch visual slam,.

SLAM-Former: Putting SLAM into One Transformer Deep patch visual slam,

Reference 29

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Observation 5c43d292-3582-4e98-9112-6d466af01c58 · outbound

This paper cites Go-slam: Global optimization for consistent 3d instant reconstruction,.

SLAM-Former: Putting SLAM into One Transformer Go-slam: Global optimization for consistent 3d instant reconstruction,

Reference 30

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source=pdf_text observed=2026-08-04T16:06:51.434047Z digest=sha256:5de06c8d47aeefcbfc5f2acaad94b63da8665095512b9e616d7bca201c6069ac

Observation eea03207-17a9-40f6-a0bc-0907c7f8d509 · outbound

This paper cites A benchmark for the evaluation of rgb-d slam sys- tems,.

SLAM-Former: Putting SLAM into One Transformer A benchmark for the evaluation of rgb-d slam sys- tems,

Reference 31

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Observation b1bfca93-03b2-41c2-b25d-9971c5a58774 · outbound

This paper cites Real- time rgb-d camera relocalization,.

SLAM-Former: Putting SLAM into One Transformer Real- time rgb-d camera relocalization,

Reference 32

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source=pdf_text observed=2026-08-04T16:06:51.439105Z digest=sha256:2f432efcd2cec0f7643573db5c3e6c6fe8ee62b327c97326cb9f14c070c10842

Observation dde61634-9f3e-478b-995f-25015325de59 · outbound

This paper cites Slam3r: Real-time dense scene reconstruction from monocular rgb videos,.

SLAM-Former: Putting SLAM into One Transformer Slam3r: Real-time dense scene reconstruction from monocular rgb videos,

Reference 33

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Observation 2f9f2843-df0f-4b69-9e1f-748815739c53 · outbound

This paper cites The replica dataset: A digital replica of indoor spaces,.

SLAM-Former: Putting SLAM into One Transformer The replica dataset: A digital replica of indoor spaces,

Reference 34

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Observation 8630b2aa-00e8-4efd-959c-df4c6b2c53ed · outbound

This paper cites Arkitscenes: A diverse real-world dataset for 3d in- door scene understanding using mobile rgb-d data,.

SLAM-Former: Putting SLAM into One Transformer Arkitscenes: A diverse real-world dataset for 3d in- door scene understanding using mobile rgb-d data,

Reference 35

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Observation 46e02da9-9a52-4303-a3ca-f10af6777e55 · outbound

This paper cites Scannet: Richly-annotated 3d reconstruc- tions of indoor scenes,.

SLAM-Former: Putting SLAM into One Transformer Scannet: Richly-annotated 3d reconstruc- tions of indoor scenes,

Reference 36

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source=pdf_text observed=2026-08-04T16:06:51.448921Z digest=sha256:87e662d4535f94d2676bef14ca7e72fa23390aa8d3a34142c197c2ede3b2937e

Observation a73d273e-8dae-45e4-81a4-02758b50a991 · outbound

This paper cites Scan- net++: A high-fidelity dataset of 3d indoor scenes,.

SLAM-Former: Putting SLAM into One Transformer Scan- net++: A high-fidelity dataset of 3d indoor scenes,

Reference 37

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Observation fb54d606-5fe9-40d9-acf5-32545130f673 · outbound

This paper cites Hy- persim: A photorealistic synthetic dataset for holistic indoor scene understanding,.

SLAM-Former: Putting SLAM into One Transformer Hy- persim: A photorealistic synthetic dataset for holistic indoor scene understanding,

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:06:51.453787Z digest=sha256:69998c3230cd6daafbbbadcb9744054aece5f63e59a00c9fffb6ea6e6104b083

Observation 996a99e5-1739-4338-a110-5cbb0c98d13c · outbound

This paper cites Blendedmvs: A large-scale dataset for generalized multi-view stereo networks,.

SLAM-Former: Putting SLAM into One Transformer Blendedmvs: A large-scale dataset for generalized multi-view stereo networks,

Reference 39

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unresolved
no resolver link, observed 2026-08-04T16:06:51.456443Z

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source=pdf_text observed=2026-08-04T16:06:51.456443Z digest=sha256:ff6209614e47b2304e2f51854eaf1b978ec5052c0fda59d10b6c9b7589ddcf95

Observation de9d48bf-a729-4502-b777-8ad178bfb4af · outbound

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

SLAM-Former: Putting SLAM into One Transformer Megadepth: Learning single-view depth prediction from internet photos,

Reference 40

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unresolved
no resolver link, observed 2026-08-04T16:06:51.458781Z

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source=pdf_text observed=2026-08-04T16:06:51.458781Z digest=sha256:42c8525ebb8b6630e6de74fb90237a33cf6d9adf420c81fa2542df7b4d236d79

Observation d4d9e6d3-22fe-4a13-a46d-cee35335a3e7 · outbound

This paper cites Deepmvs: Learning multi-view stereopsis,.

SLAM-Former: Putting SLAM into One Transformer Deepmvs: Learning multi-view stereopsis,

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-04T16:06:51.460958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:06:51.460958Z digest=sha256:9676374bc5e0c696dc49279002b05589d8e1340be149de03226dda39e77b9169

Observation 79fcf76b-3567-41bc-a98d-a7a6e1a43174 · outbound

This paper cites Orb-slam3: An accurate open-source li- brary for visual, visual–inertial, and multimap slam,.

SLAM-Former: Putting SLAM into One Transformer Orb-slam3: An accurate open-source li- brary for visual, visual–inertial, and multimap slam,

Reference 42

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unresolved
no resolver link, observed 2026-08-04T16:06:51.463152Z

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source=pdf_text observed=2026-08-04T16:06:51.463152Z digest=sha256:7044174d43773029b7dd0828ce67d81ac81ff00ccba46bc62e5e67e150e56957

Observation 618682a2-735d-40cf-ab77-87db874eba47 · outbound

This paper cites Dense rgb slam with neural implicit maps,.

SLAM-Former: Putting SLAM into One Transformer Dense rgb slam with neural implicit maps,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-04T16:06:51.465435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:06:51.465435Z digest=sha256:cab6fe1fcbc969811fe00b837d30754c96c02afa366423079b5d48e7ec849c65

Observation 75e43fac-7cfd-4552-a0e9-3d901ad8490e · outbound

This paper cites A benchmark for rgb-d visual odometry, 3d reconstruction and slam,.

SLAM-Former: Putting SLAM into One Transformer A benchmark for rgb-d visual odometry, 3d reconstruction and slam,

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T16:06:51.468152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:06:51.468152Z digest=sha256:946995cfd647d6d655458635285e99c451e67821cb6dae16e9c9cd8d8c936257

Pith citing papers

Observation f6790194-255a-45be-b8cc-3d668a46d7f0 · inbound

Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Model cites this paper.

Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Model SLAM-Former: Putting SLAM into One Transformer

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-07-28T02:23:18.112933Z

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=2026-05-20T13:03:41.389071Z digest=sha256:e88faf4bc627b840b0b1e366cd910423f167a567ab4eda2d0d476f495d6c668b

Observation 3857705a-5b5b-4736-9e3e-823d8b5d3a0e · inbound

BA-T: An Iterative Transformer for Two-View Bundle Adjustment cites this paper.

BA-T: An Iterative Transformer for Two-View Bundle Adjustment SLAM-Former: Putting SLAM into One Transformer

Reference 48

Resolution
metadata mismatch
arxiv_id, observed 2026-07-28T02:23:18.112933Z

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=2026-06-28T10:25:47.256701Z digest=sha256:d63418a230917ece0d119e150928ce3a0864139b50761165cc42a3b4f36e273e