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Paper Citation Record · LEDGER

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models

As of 10 August 2026, this Paper Citation Record lists 100 of 137 outbound references and 4 inbound Pith citation observations for arXiv:2506.01933.

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

pith.paper-citation-record.v1
2506.01933 v3

Coverage vector

measured 100 of 137 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:36:12.054318Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:38:59.460980Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:53:47.161627Z

Reference resolution

100 of 137 outbound references displayed

  • verified exact2
  • verified fuzzy10
  • unresolved88
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2788ec11-e019-4366-8590-dfe65caccad7 · outbound

This paper cites Pixel- wise view selection for unstructured multi-view stereo.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Pixel- wise view selection for unstructured multi-view stereo

Reference 1

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source=pdf_text observed=2026-08-07T11:36:01.365416Z digest=sha256:25a58dea32148eea31c361a143c0142648710d7753d937e7ddcf0e95b095e352

Observation 9c177bd8-4a2b-4a84-9d4d-7d52f9a09849 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Mvsnet: Depth inference for unstructured multi-view stereo

Reference 2

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source=pdf_text observed=2026-08-07T11:36:01.476829Z digest=sha256:eb800319a03ae733950f8fc80d3e8b1975a019f9a2d71a927261c1414d10d522

Observation 8b278bdf-688a-4c2f-834c-cdd80ba6a74e · outbound

This paper cites Cascade cost volume for high-resolution multi-view stereo and stereo matching.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Cascade cost volume for high-resolution multi-view stereo and stereo matching

Reference 3

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source=pdf_text observed=2026-08-07T11:36:01.610903Z digest=sha256:b3bfb385de1d89aadd1323254fcbbb739676eeb6671b25ae6ad37fba58914028

Observation e1a7fbdf-0471-4bab-befb-cc80a4667e23 · outbound

This paper cites Dense visual slam for rgb-d cameras.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Dense visual slam for rgb-d cameras

Reference 4

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source=pdf_text observed=2026-08-07T11:36:01.725453Z digest=sha256:2aab7797bcbfef6c91c46ab372698288d4ce585d9e92dfb2c1e2b3efd74b1e00

Observation 34826399-5540-4559-85d8-d52ace84afcf · outbound

This paper cites Elasticfusion: Real-time dense slam and light source estimation.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Elasticfusion: Real-time dense slam and light source estimation

Reference 5

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source=pdf_text observed=2026-08-07T11:36:01.890157Z digest=sha256:70ad3c76d216155ed3228c5ab562c61abb0bcbe1afc941267e9d4a7fe2d5a346

Observation c9cb3744-1ef0-4bf5-bd9b-fb9483020ca5 · outbound

This paper cites Cnn-slam: Real-time dense monocular slam with learned depth prediction.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Cnn-slam: Real-time dense monocular slam with learned depth prediction

Reference 6

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source=pdf_text observed=2026-08-07T11:36:02.002068Z digest=sha256:54b07b028f4a576bdaffbba41b1510740254d7ff17ee71eb03254a7511aead9d

Observation 45504c4e-e751-4e9e-913f-2423ac8471a9 · outbound

This paper cites Consistent video depth estimation.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Consistent video depth estimation

Reference 7

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source=pdf_text observed=2026-08-07T11:36:02.119733Z digest=sha256:af54e827d1572619a6b9b7e8d257055bdb57f2dd83620543de9ca292f82e1c58

Observation 98187afb-3899-4946-9d0f-cdaa0fe3cb39 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

Reference 8

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source=pdf_text observed=2026-08-07T11:36:02.258859Z digest=sha256:938f52bf59e8e0fd5b76f8ee5049efdcd2658a647a61533ef856fd089e1d3f2f

Observation 7b4fde24-e68a-4242-8d99-b1ee37e05390 · outbound

This paper cites GPT-4 Technical Report.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models GPT-4 Technical Report

Reference 9

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source=pdf_text observed=2026-08-07T11:36:02.377812Z digest=sha256:f24aff1aa290c6376d424972d79b4f8d325eac76a6ea3d60be98226fee801c88

Observation f03e3376-3ab3-47db-827c-f973e7133c45 · outbound

This paper cites Segment anything.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Segment anything

Reference 10

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source=pdf_text observed=2026-08-07T11:36:02.500566Z digest=sha256:640ba1c4b665d502a3692604b08a9f58b5cf57cd4810b78cd2ef0800f34cd063

Observation 4a1085e6-f3aa-4d90-8b7f-dc8d684d1442 · outbound

This paper cites Byte Latent Transformer: Patches Scale Better Than Tokens.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 11

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source=pdf_text observed=2026-08-07T11:36:02.623401Z digest=sha256:cd40b50c8a1f504a090ea7f6a496435432a58ffca66a49c63f680682143fb3bc

Observation b47038e3-2dba-427e-801d-2335425032a1 · outbound

This paper cites VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment

Reference 12

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source=pdf_text observed=2026-08-07T11:36:02.714447Z digest=sha256:8f77029238a71f512c8b097629da11f0c8bdcb500cf8e346fd02b1c32624b9f4

Observation 73c37257-39d9-462d-9eb8-b58d079d1cd4 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Dust3r: Geometric 3d vision made easy

Reference 13

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source=pdf_text observed=2026-08-07T11:36:02.814578Z digest=sha256:d11c91d30811a6bb4d0d9445a2bb4b2b0537f167698d9d8b03f83e44f7a18326

Observation 74adf2a0-fb80-403f-b027-c4e0209c29c2 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Grounding image matching in 3d with mast3r

Reference 14

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source=pdf_text observed=2026-08-07T11:36:02.953388Z digest=sha256:90f02de19d6c262b300b8390f351bd1f5716b9307b4dfef877adbd423207bea8

Observation 54c1d3b6-5e8c-4dbb-ae74-6d9d7dabed19 · outbound

This paper cites Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli

Reference 15

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source=pdf_text observed=2026-08-07T11:36:03.099353Z digest=sha256:9644e4d4a75ebcbe8b2008c4c37fddff5d3bfa41c99e8150a61f86be62a6678a

Observation d43320c0-88a2-456a-832e-fb6f3704ac38 · outbound

This paper cites MonST3r: A simple approach for estimating geometry in the presence of motion.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models MonST3r: A simple approach for estimating geometry in the presence of motion

Reference 16

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source=pdf_text observed=2026-08-07T11:36:03.166023Z digest=sha256:cb097eeb124995ff75624c3535d2b2b05cd414e84e955770280cf070c911a816

Observation 8f3fb0ee-5583-4136-b0d4-57667a64df1a · outbound

This paper cites Vggt: Visual geometry grounded transformer.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Vggt: Visual geometry grounded transformer

Reference 17

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source=pdf_text observed=2026-08-07T11:36:03.242227Z digest=sha256:8761e16a059a0d092719814d51913ef395de1b333cb551c1cbb3c14161136702

Observation 39c30982-c6c5-46fb-85c0-767e11953135 · outbound

This paper cites Large spatial model: End-to-end unposed images to semantic 3d.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Large spatial model: End-to-end unposed images to semantic 3d

Reference 18

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source=pdf_text observed=2026-08-07T11:36:03.310291Z digest=sha256:c27138601047715999a4af6a664856e22c4b1f7e6b88abe5216d6ac888448e57

Observation a72bdfee-7b79-4a93-ad50-cc43176a7ab7 · outbound

This paper cites Continuous 3d perception model with persistent state.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Continuous 3d perception model with persistent state

Reference 19

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source=pdf_text observed=2026-08-07T11:36:03.398770Z digest=sha256:29eddc92357b4babcc20d9cd8cab7abcef53d7a38993b27cb7965b4a5653b954

Observation 668a1cf8-4468-4cdd-9c00-0206fcbbb801 · outbound

This paper cites Aether: Geometric-Aware Unified World Modeling.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Aether: Geometric-Aware Unified World Modeling

Reference 20

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source=pdf_text observed=2026-08-07T11:36:03.468801Z digest=sha256:bc299ddfa16adc00b9643795f708db75d89b018a93585babd1d69307d39b843a

Observation 00fcb1c3-96d3-4d15-b1e2-727be2819aaa · outbound

This paper cites Geo4d: Leverag- ing video generators for geometric 4d scene reconstruction, 2025.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Geo4d: Leverag- ing video generators for geometric 4d scene reconstruction, 2025

Reference 21

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source=pdf_text observed=2026-08-07T11:36:03.546876Z digest=sha256:b6544d700e6290092474d889ac3a9372d9f733e5a974f45b5f472911d3c0e912

Observation e0f58ec9-6174-4984-92d9-12b71bffbc20 · outbound

This paper cites No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images

Reference 22

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source=pdf_text observed=2026-08-07T11:36:03.621331Z digest=sha256:89645370f63503a5c06233bdc2d77e47569e421f9d5162e5fcc53fb9945d2360

Observation 05a0b996-dad4-4885-8d26-2590dd581b09 · outbound

This paper cites Align3r: Aligned monocular depth estimation for dynamic videos.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Align3r: Aligned monocular depth estimation for dynamic videos

Reference 23

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source=pdf_text observed=2026-08-07T11:36:03.680710Z digest=sha256:c772609abc1d26c6c66772fa503d0574c15403726c29adb84517e1c148fe7239

Observation 426ec396-0627-4cca-a2a3-366b71aad80b · outbound

This paper cites Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs, 2024.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs, 2024

Reference 24

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source=pdf_text observed=2026-08-07T11:36:03.772358Z digest=sha256:340f2655a8e5031d59501c2b0187e7d9d4198556092811fa60a27383e91a76e2

Observation d8c2fce6-74ce-460a-b84c-722993bb8265 · outbound

This paper cites Easi3r: Estimating disentangled motion from dust3r without training.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Easi3r: Estimating disentangled motion from dust3r without training

Reference 25

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source=pdf_text observed=2026-08-07T11:36:03.869245Z digest=sha256:aea2a0c88aeabc86746360104141a3f75e5f6567f10ff5eafbb566a29aca03dc

Observation b13c9f01-aabb-46ef-8d4e-c711e76c84bb · outbound

This paper cites 3d reconstruction with spatial memory.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models 3d reconstruction with spatial memory

Reference 26

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source=pdf_text observed=2026-08-07T11:36:03.945934Z digest=sha256:0a7067bc907b1cb0175acf7cdb7e8b67fb269707840719de11bbc64382760994

Observation 5a0db511-aa93-4c8e-8f77-1b56b3a25d52 · outbound

This paper cites GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors

Reference 27

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source=pdf_text observed=2026-08-07T11:36:04.031193Z digest=sha256:7afcaeb56b022fb64417298294c4c2153f7382726ea92835c01ff9c4ae614df0

Observation 58a263ef-8544-4f40-86c4-99ce1373e23f · outbound

This paper cites Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views

Reference 28

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source=pdf_text observed=2026-08-07T11:36:04.117668Z digest=sha256:e5489930543da6fb355aaa5303c7e9b79ff633566238bacd954cc1eb827c42da

Observation 8aa2bf5e-bf69-456e-a905-0a57f60b3a5c · outbound

This paper cites Emerging properties in self-supervised vision transformers.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Emerging properties in self-supervised vision transformers

Reference 29

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source=pdf_text observed=2026-08-07T11:36:04.215487Z digest=sha256:beed5a02347c6ff3be5675a6d843234ce1839f448c00f672359f60a4dab3aa09

Observation 804ec66a-997d-49d3-ab52-320a593707d5 · outbound

This paper cites Structure-from-motion revisited.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Structure-from-motion revisited

Reference 30

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source=pdf_text observed=2026-08-07T11:36:04.348886Z digest=sha256:d8102cc7a5fe18b20041f36eb3ae3a5a1ff7b06fa873fb29950770a9f1fc09b7

Observation d4870362-214c-4e7e-971b-dccd8686b82a · outbound

This paper cites Large scale multi-view stereopsis evaluation.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Large scale multi-view stereopsis evaluation

Reference 31

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source=pdf_text observed=2026-08-07T11:36:04.490185Z digest=sha256:8b0699e0e2c97880f8d79831d9011103bf47686da7a32807c5e981d83bdf8bf3

Observation fdc355f8-db12-4cda-a223-d94a3b8ac19c · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models A multi-view stereo benchmark with high-resolution images and multi-camera videos

Reference 32

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source=pdf_text observed=2026-08-07T11:36:04.642355Z digest=sha256:cf60dc3cf696253a1f8959e1298de34c5d5b63e6a4a39d6b3a7af3fe827da611

Observation 828a9932-566f-4721-8747-653315ca5e6d · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 33

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source=pdf_text observed=2026-08-07T11:36:04.740263Z digest=sha256:92c92acc6cb10a933356ed1f18fdd12f1390b673206c046e481c41a49fa65ad5

Observation 9761f506-62e3-46c1-a128-f8325261b866 · outbound

This paper cites Tanks and temples: Bench- marking large-scale scene reconstruction.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Tanks and temples: Bench- marking large-scale scene reconstruction

Reference 34

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source=pdf_text observed=2026-08-07T11:36:04.852691Z digest=sha256:43b33f2370866b812bda02c001368a77342288b46982772d9257b20ab1f47705

Observation 1524dc6d-3424-424e-ab22-bf08096a0237 · outbound

This paper cites Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner

Reference 35

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source=pdf_text observed=2026-08-07T11:36:05.024261Z digest=sha256:5b63024a46b65c5f21388a5802775f9c4fb3caa3742eb122ef7c1ea584c11944

Observation 81a2c0c5-d9fa-4c29-a836-48687d8bbda1 · outbound

This paper cites Sparsity invariant cnns.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Sparsity invariant cnns

Reference 36

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source=pdf_text observed=2026-08-07T11:36:05.129762Z digest=sha256:f6b95dcc85424424352e8797050f27a93d82ec26e862d7d8e5b9f1387091a0e4

Observation 942b1a5b-0aa6-455a-bf7b-c3d2ac5b3d85 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Depth map prediction from a single image using a multi-scale deep network

Reference 37

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source=pdf_text observed=2026-08-07T11:36:05.245726Z digest=sha256:05f085451d8bdc8a8f6a44b6f07c2188cdea9f0d77d58cd649e11c7c09e64038

Observation d0b01641-8117-47d8-b64d-0c09dde9b6db · outbound

This paper cites A benchmark for multi-view stereo depth estimation under view-point and lighting variations.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models A benchmark for multi-view stereo depth estimation under view-point and lighting variations

Reference 38

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source=pdf_text observed=2026-08-07T11:36:05.408033Z digest=sha256:9a50749515d12db7edddf20be5b8f45dde19e9769078e74fe151d1c6d85efff6

Observation 20c49517-c44d-42eb-9b8e-7aeb404e1b03 · outbound

This paper cites A benchmark for visual-inertial odometry in the presence of motion blur.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models A benchmark for visual-inertial odometry in the presence of motion blur

Reference 39

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source=pdf_text observed=2026-08-07T11:36:05.567751Z digest=sha256:e8454e4b6df5571eb25bf54ea3fd082583e6afdd68cbe42cec4102f22e970a2e

Observation 52b36415-75f4-43a2-84eb-2cccd63a5556 · outbound

This paper cites A benchmark for the evaluation of rgb-d slam systems.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models A benchmark for the evaluation of rgb-d slam systems

Reference 40

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source=pdf_text observed=2026-08-07T11:36:05.696413Z digest=sha256:4c7552ffd3576a20109184a817ff6ece3c714390b3f952de7ec55074f9941f86

Observation cc4a1686-f476-4525-813b-9f33aceba103 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models A naturalistic open source movie for optical flow evaluation

Reference 41

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source=pdf_text observed=2026-08-07T11:36:05.815141Z digest=sha256:5a6d0f700c54eedb04f589e3379790ecdd59d58fab72f03d35d4812b04466fce

Observation 24895f9c-f07a-4ad8-8c22-86ab01a6b97f · outbound

This paper cites Homoclinic Floer homology via direct limits.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Homoclinic Floer homology via direct limits

Reference 42

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source=pdf_text observed=2026-08-07T11:36:05.937664Z digest=sha256:da85879536b9c14130eccfee588cf9996e080332439e6cd803427b0ba8eba4e9

Observation e97f0efa-123b-42a1-bb66-7c2c4d52dcda · outbound

This paper cites Syndrone-multi- modal uav dataset for urban scenarios.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Syndrone-multi- modal uav dataset for urban scenarios

Reference 43

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source=pdf_text observed=2026-08-07T11:36:06.074969Z digest=sha256:6e731ebd5d7fbdce8329575999ff34057d6dc764880c02e50debeb9960e327c1

Observation 02a6ffb5-61ac-4584-91e1-58d98f7acf98 · outbound

This paper cites Video Depth Anything: Consistent Depth Estimation for Super-Long Videos.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

Reference 44

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source=pdf_text observed=2026-08-07T11:36:06.197375Z digest=sha256:c94728bfa727f5e96df8d8d6e5cc6d38242cf40d909ef82d7aa915d7c5e6dcc0

Observation 12060514-1175-4ec4-ada3-98881c7ec738 · outbound

This paper cites Depth Any Video with Scalable Synthetic Data.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Depth Any Video with Scalable Synthetic Data

Reference 45

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source=pdf_text observed=2026-08-07T11:36:06.357901Z digest=sha256:43479f7313b280bca02612a14b16b71b28636d4306aa3377eb5d40a56aef1984

Observation a8a8a0da-156b-4dd2-b772-1c2524958fed · outbound

This paper cites Marigold: Affordable adaptation of diffusion-based image generators for image analysis, 2025.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Marigold: Affordable adaptation of diffusion-based image generators for image analysis, 2025

Reference 46

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source=pdf_text observed=2026-08-07T11:36:06.480435Z digest=sha256:a1c869d8453d6b51db925d68f5628b7cc6ce37444c1afa2e00afe0483af158fb

Observation 9d695748-8202-4c08-a8c5-93a33b05bd8e · outbound

This paper cites DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos

Reference 47

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source=pdf_text observed=2026-08-07T11:36:06.571474Z digest=sha256:68b35ca58909a791b63f15f4a5a95ce2c39731c2c79966d230f8995b0289bcac

Observation e6e3bf84-2c3e-4657-9f42-8087adf77422 · outbound

This paper cites Learning object-centric representations of multi-object scenes from multiple views.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Learning object-centric representations of multi-object scenes from multiple views

Reference 48

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source=pdf_text observed=2026-08-07T11:36:06.630659Z digest=sha256:dafe6c94e9d9eba26e1a0ce58c1c2435f89bb854d3a0589625746646d16d258f

Observation 3dc5cf8b-17ce-427e-baa6-3623c2d8bf14 · outbound

This paper cites an unresolved cited work.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Unresolved cited work

Reference 49

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source=pdf_text observed=2026-08-07T11:36:06.690287Z digest=sha256:ce4122765e142466ca29fa0b9199a1c187d5468b70021baef5fc94363cd81805

Observation 10a1c49f-80b9-42c7-b66c-21cb17159da5 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 50

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source=pdf_text observed=2026-08-07T11:36:06.771511Z digest=sha256:e0187e460bd4dce4377a855ad85b5f75f80d3cb1843dbb9e5bc5fff3bd79af05

Observation 6c1ce0f1-b4dc-484e-ae99-a08b4e027f4b · outbound

This paper cites TAPVid-3D: A Benchmark for Tracking Any Point in 3D.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models TAPVid-3D: A Benchmark for Tracking Any Point in 3D

Reference 51

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source=pdf_text observed=2026-08-07T11:36:06.835239Z digest=sha256:4207fd85642050df8f98115d5dba39406dda8cd2f3c96a0843e9bb2b0911cbb2

Observation b823cd76-6c4a-4368-81c4-ba3654942bae · outbound

This paper cites Acid: Aerial-captured image dataset for visual localization.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Acid: Aerial-captured image dataset for visual localization

Reference 52

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source=pdf_text observed=2026-08-07T11:36:06.917721Z digest=sha256:27665ca9e8294a7df1cd6ade518ef5d3aaa4978b778e2cd14e8c260b46140fa5

Observation 965d2e2f-e037-443f-884d-52790ebc230d · outbound

This paper cites Ultrra challenge 2025, 2024.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Ultrra challenge 2025, 2024

Reference 53

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source=pdf_text observed=2026-08-07T11:36:06.998034Z digest=sha256:080e9902302fabebeae554ee23b5dffba593e8878e39a84891c224e83fb0a74c

Observation 75a05bc6-d768-4c1c-b005-2be81d8e0190 · outbound

This paper cites Least-squares estimation of transformation parameters between two point patterns.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Least-squares estimation of transformation parameters between two point patterns

Reference 54

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source=pdf_text observed=2026-08-07T11:36:07.081998Z digest=sha256:f6a619d83d77ffcdc7c9374c4837c9488a53765eb0ac0d862c1c885c11086e72

Observation 5ba23f86-3f3e-40f5-83a1-ed6a84b3c7b9 · outbound

This paper cites Aerialmegadepth: Learning aerial-ground reconstruction and view synthesis.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Aerialmegadepth: Learning aerial-ground reconstruction and view synthesis

Reference 55

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source=pdf_text observed=2026-08-07T11:36:07.179739Z digest=sha256:842bdee4d6b4c39098048b428e7e02afd893a2f91c2972ef364e0ff03dab219a

Observation 899557ff-fa2d-436c-8e2b-a22da70f00b4 · outbound

This paper cites Instantsplat: Sparse-view sfm-free gaussian splatting in seconds, 2024.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Instantsplat: Sparse-view sfm-free gaussian splatting in seconds, 2024

Reference 56

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source=pdf_text observed=2026-08-07T11:36:07.249082Z digest=sha256:ed511f6cf4b21e3962ac8b415a1d12ac5ff28431ac17b42b28d7fa48044fb443

Observation b059fa4c-cbd6-465d-b8d3-72a43725e5c9 · outbound

This paper cites Scene coordinate regression forests for camera relocalization in rgb-d images.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Scene coordinate regression forests for camera relocalization in rgb-d images

Reference 57

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source=pdf_text observed=2026-08-07T11:36:07.317761Z digest=sha256:11fd5a44ed675218e2abb3a20067a1d86f54672913775184a4e996c9eb2853cb

Observation 03b61bca-d242-4c98-8994-25f6de12ae42 · outbound

This paper cites Nrgbd: A large-scale dataset for novel view synthesis and 3d reconstruc- tion from rgb-d images.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Nrgbd: A large-scale dataset for novel view synthesis and 3d reconstruc- tion from rgb-d images

Reference 58

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source=pdf_text observed=2026-08-07T11:36:07.392723Z digest=sha256:1846779270e74b2d453cfb6f23c6f756543be749b2d3a0e886f0e1a275e0cb94

Observation e8037e00-d30c-48a3-96d7-7764b0c5e077 · outbound

This paper cites A benchmark for the evaluation of rgb-d slam systems.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models A benchmark for the evaluation of rgb-d slam systems

Reference 59

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source=pdf_text observed=2026-08-07T11:36:07.483267Z digest=sha256:0b8cbc59e4ed6dc88b24005b6b018de717825b69e0184e609d85623fdbf2632b

Observation 3a00823a-b699-4dee-b192-0942af5a9cd9 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Scannet++: A high-fidelity dataset of 3d indoor scenes

Reference 60

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source=pdf_text observed=2026-08-07T11:36:07.607282Z digest=sha256:05f168de7c1ff0084121a90f07686bc820313395ec7c43773f11d6586487fe6e

Observation 9faa0557-f794-44ea-b72e-54428873eb6f · outbound

This paper cites Image quality metrics: Psnr vs.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Image quality metrics: Psnr vs

Reference 61

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source=pdf_text observed=2026-08-07T11:36:07.702021Z digest=sha256:7f9d0d61f82136bee39f2d66bad7017beb82600971b0123c808f489e65334644

Observation 939ea70f-c727-4109-88ce-9b891df086ba · outbound

This paper cites Bovik, Hamid R.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Bovik, Hamid R

Reference 62

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source=pdf_text observed=2026-08-07T11:36:07.799082Z digest=sha256:ea0cc9f875a949704117671f953c69f63e526ef4276422afb1939c06fec17547

Observation 4f0a0ab9-4ee9-4365-aaca-5a0cddb0940d · outbound

This paper cites Efros, Eli Shechtman, and Oliver Wang.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Efros, Eli Shechtman, and Oliver Wang

Reference 63

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source=pdf_text observed=2026-08-07T11:36:07.879594Z digest=sha256:88d3dbda12713232a3704d742157b4d3ab9e883eaea8a2b0e5a22cb5fb53306c

Observation cc89ca25-c48d-4b56-a5f7-8ed01bc56d6d · outbound

This paper cites APOLLO: SGD-like Memory, AdamW-level Performance.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models APOLLO: SGD-like Memory, AdamW-level Performance

Reference 64

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source=pdf_text observed=2026-08-07T11:36:07.981342Z digest=sha256:1b13726ab25acbb12db947234acdd45b5ceb465d894092dc8551779bf82da74b

Observation 45791f33-d63e-4ba7-8e93-509253bf7cd1 · outbound

This paper cites Unsupervised learning of depth and ego-motion from video.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Unsupervised learning of depth and ego-motion from video

Reference 65

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source=pdf_text observed=2026-08-07T11:36:08.104194Z digest=sha256:4594409919637290295ee1d7de45d8dbbc9051157120ce5bfc8dd2d274147862

Observation fddaa74c-bd97-4c65-bc4d-d39ca3eb163a · outbound

This paper cites D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry

Reference 66

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source=pdf_text observed=2026-08-07T11:36:08.225516Z digest=sha256:b7e10cac946e603ba152b13b8920cac106a889cd3e436a1ca37757a6eb53619e

Observation 4efd3108-a9f2-46a9-8bdc-5c5c5259b020 · outbound

This paper cites Ultrra: A benchmark for air-ground relative pose estimation.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Ultrra: A benchmark for air-ground relative pose estimation

Reference 67

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source=pdf_text observed=2026-08-07T11:36:08.351157Z digest=sha256:834e09c78a5fa6e4f9d06dfb609e52999359169199d50e8c88735c78ee98df9c

Observation 478f99e9-c840-459a-bb4e-4d5ad85979ba · outbound

This paper cites LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models

Reference 68

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source=pdf_text observed=2026-08-07T11:36:08.475134Z digest=sha256:58fc8ce167c0ada60064b455340f5fe069e8c9ec373c14d8894d2209eb9db0b0

Observation fd3c1d5f-652e-4132-a15d-eb9880c4f9c2 · outbound

This paper cites CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion

Reference 69

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source=pdf_text observed=2026-08-07T11:36:08.594880Z digest=sha256:acc05d646b4848394428658b4f0a4f2b2b5e8c4182195639502793794803e469

Observation 436c82a8-29e4-4176-99fd-2950d572eaa9 · outbound

This paper cites CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow

Reference 70

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source=pdf_text observed=2026-08-07T11:36:08.708301Z digest=sha256:bab994b6ee3346cbad7c1029d2de6faf91ed9b3c1a4c88177140f96bd14965a2

Observation bbbe0e4c-ec5e-4f92-9962-a0bb3c53bc6b · outbound

This paper cites Stereo4d: Learning how things move in 3d from internet stereo videos.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Stereo4d: Learning how things move in 3d from internet stereo videos

Reference 71

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source=pdf_text observed=2026-08-07T11:36:08.793335Z digest=sha256:f90b5179e3169ea7da5542de8c3f1f2710d3313acdbc98a8adeb2d59f753580d

Observation 581389f1-579e-470f-bc0d-3474b2c0621b · outbound

This paper cites Uni4d: Unifying visual foundation models for 4d modeling from a single video.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Uni4d: Unifying visual foundation models for 4d modeling from a single video

Reference 72

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source=pdf_text observed=2026-08-07T11:36:08.878970Z digest=sha256:c2315dc1c872a1ec52917c5ad32602168077bd5782f9dd7286e96d60d4bfea80

Observation 59946c0a-7049-44b3-a83a-46e6dd30e169 · outbound

This paper cites Zero-shot monocular scene flow estimation in the wild.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Zero-shot monocular scene flow estimation in the wild

Reference 73

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:08.966173Z digest=sha256:1f6e6bb4d4c1e392eb48de40b534f9a4db6f2dd77c96d801738f21d117a2cae8

Observation a534cb36-97d8-4755-b008-8e9d12381080 · outbound

This paper cites Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction

Reference 74

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:36:09.125738Z digest=sha256:b6763bdf8b68480566a6ed3e851c718a225d30fd36482045c000ebea731e86c0

Observation c8a02d75-922b-4f2c-873b-bb6916ca55e9 · outbound

This paper cites Dˆ 2ust3r: Enhancing 3d reconstruction with 4d pointmaps for dynamic scenes.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Dˆ 2ust3r: Enhancing 3d reconstruction with 4d pointmaps for dynamic scenes

Reference 75

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source=pdf_text observed=2026-08-07T11:36:09.279688Z digest=sha256:0afd11974e407930941fd59ed1f69b4aa8964e14c30d16220c46917998626cb3

Observation c826282e-f24f-415a-9f25-aa34f5ee9c56 · outbound

This paper cites Learning Multi-frame and Monocular Prior for Estimating Geometry in Dynamic Scenes.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Learning Multi-frame and Monocular Prior for Estimating Geometry in Dynamic Scenes

Reference 76

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local_arxiv, observed 2026-08-07T11:36:16.605646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:09.436608Z digest=sha256:9d37070eb9eb76079c1ddd45878f3fbdc943282321a9a0c8e438d7eb7a5e7a06

Observation c6e69485-858e-4c0e-a535-7b13fd2d1927 · outbound

This paper cites POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction

Reference 77

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:36:09.547923Z digest=sha256:ebd39fd75d04daca1c262131ccc1e3322861da36ccf96aa5e9067f364d37deb8

Observation eebdbe46-a9ad-4a25-ad97-b7a82e584366 · outbound

This paper cites Back on track: Bundle adjustment for dynamic scene reconstruction.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Back on track: Bundle adjustment for dynamic scene reconstruction

Reference 78

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:09.709817Z digest=sha256:316ac1447b0b0eab01d8bb12f44deacda8157f6656942c2c2dbcc8d79a813c90

Observation ed0abced-609e-40cf-a27a-4c63540f2131 · outbound

This paper cites STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes

Reference 79

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:09.882241Z digest=sha256:44b554a1fb7b8ee053cfe797fc7ec288d50d6bf254ea3fa2869272027a78d7fb

Observation b2895897-06d7-439e-a660-4b44204a2d45 · outbound

This paper cites Dynamic point maps: A versatile representation for dynamic 3d reconstruction, 2025.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Dynamic point maps: A versatile representation for dynamic 3d reconstruction, 2025

Reference 80

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raw_fallback, observed 2026-08-07T11:36:21.635208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:10.034426Z digest=sha256:234d585ee729e8455b7d2a6126a82a458cb4110b15aaf38646176597fcc9c973

Observation 0fda4dd6-ec69-4b3f-bce6-15653b1e8eb4 · outbound

This paper cites Regist3r: Incremental registration with stereo foundation model.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Regist3r: Incremental registration with stereo foundation model

Reference 81

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no resolver link, observed 2026-08-07T11:36:10.155444Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:10.155444Z digest=sha256:888bdc406990fb9c52f28c6c29072837059bbd283fbc830f76f66ee3c79b30fc

Observation b805e42f-c4d4-4815-9918-8d38a9a0bdff · outbound

This paper cites MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motion.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motion

Reference 82

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:10.297457Z digest=sha256:f3a55d0f65edbc051a24730ba8be3e6328387ec0c4262a8e406a23ac8980e8d9

Observation 4c229070-7a1d-443e-85ad-f9e749058d29 · outbound

This paper cites Must3r: Multi-view network for stereo 3d reconstruction.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Must3r: Multi-view network for stereo 3d reconstruction

Reference 83

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raw_fallback, observed 2026-08-07T11:36:21.541211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:10.371592Z digest=sha256:cb13aee0ed919e96e01bf4f6043c6d2a6aeddb7c257b8b266cc1e8197791a9ef

Observation 08e5d90b-410f-49eb-96f8-0d5b4a4066ad · outbound

This paper cites Light3r-sfm: Towards feed-forward structure- from-motion.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Light3r-sfm: Towards feed-forward structure- from-motion

Reference 84

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raw_fallback, observed 2026-08-07T11:36:21.175631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:10.453979Z digest=sha256:bd9422acf3cb27c6a4de569cc052445b34d5d6801b468b3bf90aacaeb07fde11

Observation 4058531e-b945-476f-9e70-f8d3b0e302d9 · outbound

This paper cites Mv-dust3r+: Single-stage scene reconstruction from sparse views in 2 seconds.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Mv-dust3r+: Single-stage scene reconstruction from sparse views in 2 seconds

Reference 85

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raw_fallback, observed 2026-08-07T11:36:20.753730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:10.531460Z digest=sha256:e6151e7c739858baec1ee79feed801faa81261807ca4678404fe1cfd771a60a1

Observation 602bac01-5c56-42f4-98e6-b0fed0add4fd · outbound

This paper cites Spatialsplat: Efficient semantic 3d from sparse unposed images.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Spatialsplat: Efficient semantic 3d from sparse unposed images

Reference 86

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:10.626751Z digest=sha256:48aaceffd6577af3bf9879dabffa1ba16c623fae6039d93b5ef3edcd1fb3da6f

Observation 8059f367-d566-4dee-b02f-2d3eea3e9674 · outbound

This paper cites Matrix3D: Large Photogrammetry Model All-in-One.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Matrix3D: Large Photogrammetry Model All-in-One

Reference 87

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:10.707160Z digest=sha256:b4e0124a47bd773af1b5d85d23104b004fffdc87880e294a938b764a6d5a030a

Observation 950a3fc3-475b-48d5-bc00-a043ddcbbb3e · outbound

This paper cites Anysplat: Feed-forward 3d gaussian splatting from unconstrained views.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Anysplat: Feed-forward 3d gaussian splatting from unconstrained views

Reference 88

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no resolver link, observed 2026-08-07T11:36:10.813603Z

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source=pdf_text observed=2026-08-07T11:36:10.813603Z digest=sha256:55e639d6e8c7edd42516cf07516955290b0f6916d52cc05a21f9be325c457fd9

Observation 6cb4e3b0-2c4a-4b7c-989c-bfb13c8a869c · outbound

This paper cites VicaSplat: A Single Run is All You Need for 3D Gaussian Splatting and Camera Estimation from Unposed Video Frames.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models VicaSplat: A Single Run is All You Need for 3D Gaussian Splatting and Camera Estimation from Unposed Video Frames

Reference 89

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no resolver link, observed 2026-08-07T11:36:10.927992Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:10.927992Z digest=sha256:6632a09062dc0a384e8604155a33da9f69142d3ebf2110d4a45e57180c422733

Observation 9913bf8a-5de2-4494-9621-7bac44c892f1 · outbound

This paper cites PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence

Reference 90

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no resolver link, observed 2026-08-07T11:36:11.039468Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:11.039468Z digest=sha256:c796150f0745ba611036d7eaff00aa6d31b5155a2f26906e2cbec48368c957ac

Observation fec45429-c198-4705-a45d-30b85b8c519b · outbound

This paper cites Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors

Reference 91

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:11.148451Z digest=sha256:0005217ac275ba2fe24e6dc7750fe7be39eb243820f8e1f0a0761af0aabc54b4

Observation 49b726ec-1356-44d9-9adb-efd8b3b15c56 · outbound

This paper cites Spurfies: Sparse surface reconstruction using local geometry priors, 2024.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Spurfies: Sparse surface reconstruction using local geometry priors, 2024

Reference 92

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raw_fallback, observed 2026-08-07T11:36:20.420077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:11.259054Z digest=sha256:4f99321d7893de6357305d09462d55b432771a2068a35d9e998567c05ca67fd8

Observation a9517106-9c69-4f12-a347-6ec3c3326a54 · outbound

This paper cites Towards in-the-wild 3d plane reconstruction from a single image.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Towards in-the-wild 3d plane reconstruction from a single image

Reference 93

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raw_fallback, observed 2026-08-07T11:36:20.258361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:11.370271Z digest=sha256:5bfdc2da26c4d9489db0062e45a11997e03b99f4d88f2cccb8a4b57e8e71c909

Observation 35cc46c6-8f81-4c0e-aca8-103b70748be1 · outbound

This paper cites MegaSaM: Accurate, fast and robust structure and motion from casual dynamic videos.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models MegaSaM: Accurate, fast and robust structure and motion from casual dynamic videos

Reference 94

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raw_fallback, observed 2026-08-07T11:36:20.044609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:11.453529Z digest=sha256:6b8205b294ca33df45616ab1c3b44edb9f237319dafa8a3c869a541582a2af56

Observation 7bab6f5f-16e9-4184-8039-9bc633ee7642 · outbound

This paper cites VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold

Reference 95

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:36:11.553794Z digest=sha256:80ca40b799b4f8f3dfa06eaf495faeaaf8374631a6a83d704260dc18a9566c7b

Observation f4831670-a1fd-48a7-8dc3-13c7a5eae94b · outbound

This paper cites an unresolved cited work.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Unresolved cited work

Reference 96

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raw_fallback, observed 2026-08-07T11:36:19.811341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:11.686650Z digest=sha256:70c8b95952645e7bc6179285534ff85c6232727119464e184bb5b2ab95a6b8fc

Observation 0aa8acf9-5c05-4e20-a64b-b3c2842ef423 · outbound

This paper cites Wildgs-slam: Monocular gaussian splatting slam in dynamic environments.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Wildgs-slam: Monocular gaussian splatting slam in dynamic environments

Reference 97

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raw_fallback, observed 2026-08-07T11:36:19.547865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:11.787856Z digest=sha256:57ce7366ce8da48cfc48bc6d5907b477e15d107cb1be75d350a389707485f72e

Observation 53f4f5a1-e433-4f5e-9285-f56fd820ab32 · outbound

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

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Slam3r: Real-time dense scene reconstruction from monocular rgb videos

Reference 98

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verified fuzzy
raw_fallback, observed 2026-08-07T11:36:19.358261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:11.886670Z digest=sha256:e1a88942a07f1723b9977f6ff100d66aed93df50cb08a4eac869597eb3aba49f

Observation 21272799-123a-4b53-8403-9cf27e8e496c · outbound

This paper cites Hier-SLAM++: Neuro-Symbolic Semantic SLAM with a Hierarchically Categorical Gaussian Splatting.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Hier-SLAM++: Neuro-Symbolic Semantic SLAM with a Hierarchically Categorical Gaussian Splatting

Reference 99

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verified exact
local_arxiv, observed 2026-08-07T11:36:15.983269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:36:11.964834Z digest=sha256:25da14395a3cf96a2e35fc65383981e4cba31d1c72eaa44be161b588b3c61db9

Observation 6243f2f4-54f4-4875-9636-74ad48c12d8f · outbound

This paper cites Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving

Reference 100

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no resolver link, observed 2026-08-07T11:36:12.054318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:36:12.054318Z digest=sha256:f2fcca93035af78f3ae621dc75448e03019560e40f7e2edda723bb55cebf48cb

Pith citing papers

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

BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models? cites this paper.

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

Reference 41

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no resolver link, observed 2026-08-06T15:38:59.460980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:38:59.460980Z digest=sha256:4a43a157008656a855f03997d2b27c2a1580c88e967014dd3f9b10e8211b66d7

Observation 3c0444e5-78c2-4a3f-a7cc-1c5b2ce80026 · inbound

ViPE: Video Pose Engine for 3D Geometric Perception cites this paper.

ViPE: Video Pose Engine for 3D Geometric Perception E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models

Reference 15

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verified exact
arxiv_id, observed 2026-05-16T16:41:08.672657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T16:41:08.620285Z digest=sha256:c2e84952768003c86a736018a9c155aa762c832eb94b5f3d0a662323adb485d2

Observation b130b358-ff23-486c-9026-c556f02e42ea · inbound

Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection cites this paper.

Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models

Reference 22

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unresolved
no resolver link, observed 2026-08-02T18:10:37.881504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T18:10:37.881504Z digest=sha256:b2d11bc0934ecaa016a2085a9c040d2733c1e3bc2c26e7ebe32cf8b619ee910a

Observation cf0004d8-e17f-4ead-9e4b-58c8d930db10 · inbound

SpatialBench: Is Your Spatial Foundation Model an All-Round Player? cites this paper.

SpatialBench: Is Your Spatial Foundation Model an All-Round Player? E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:53:47.163011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T17:49:58.532910Z digest=sha256:71a1402fe9dc1388bb59442bda6447e534e2da7bc8956d55719a1cbed2715603