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Paper Citation Record · LEDGER

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos

As of 11 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2501.09347.

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

pith.paper-citation-record.v1
2501.09347 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

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measured 44 of 44 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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  • verified fuzzy15
  • unresolved28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0880eed1-cbbd-4596-87ac-6bfdc0a47da8 · outbound

This paper cites Score Distillation Sampling with Learned Manifold Corrective.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Score Distillation Sampling with Learned Manifold Corrective

Reference 1

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Observation 8ec197c6-3c9a-4de3-9121-bcda08c7901f · outbound

This paper cites Nope-nerf: Optimising neu- ral radiance field with no pose prior.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Nope-nerf: Optimising neu- ral radiance field with no pose prior

Reference 2

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Observation e367115c-e668-41cc-919d-61038729f4df · outbound

This paper cites Efficient geometry-aware 3d generative adversarial networks.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Efficient geometry-aware 3d generative adversarial networks

Reference 3

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Observation c69324da-c25c-4c8e-8420-ff24aa031e3e · outbound

This paper cites pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction

Reference 4

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Source-reported events for the cited work

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Observation f5c35db2-3044-4d28-b99c-9774701beea5 · outbound

This paper cites Explicit Correspondence Matching for Generalizable Neural Radiance Fields.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Explicit Correspondence Matching for Generalizable Neural Radiance Fields

Reference 5

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Observation c027b0ca-a9eb-4d47-9df8-6a51b9f6e038 · outbound

This paper cites MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images

Reference 6

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Observation aa28ce44-ce42-4cb3-bc68-d88fde35d4ba · outbound

This paper cites Objaverse: A Universe of Annotated 3D Objects.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Objaverse: A Universe of Annotated 3D Objects

Reference 7

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Observation a08bdf51-d95c-441f-82d0-c8b39fa6e323 · outbound

This paper cites Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024

Reference 8

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Observation 7e304e40-b405-4203-b429-f263b02a5856 · outbound

This paper cites COLMAP-Free 3D Gaussian Splatting.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos COLMAP-Free 3D Gaussian Splatting

Reference 9

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Observation 6c8f295b-25b0-433e-b165-eed181921f60 · outbound

This paper cites Get3d: A generative model of high quality 3d tex- tured shapes learned from images.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Get3d: A generative model of high quality 3d tex- tured shapes learned from images

Reference 10

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Source-reported events for the cited work

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Observation 7cc8c001-a1e5-46a4-bc0e-a38a9052b947 · outbound

This paper cites LRM: Large Reconstruction Model for Single Image to 3D.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos LRM: Large Reconstruction Model for Single Image to 3D

Reference 11

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Observation c8add9bf-c0e0-4521-8285-6f6e9a797e70 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos A style-based generator architecture for generative adversarial networks

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 63e59a69-5e1a-4da7-839a-f6bc631119ef · outbound

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

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos 3d gaussian splatting for real-time radiance field rendering

Reference 13

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Observation 9f907342-2f7b-4129-8235-9551bc17a3de · outbound

This paper cites Auto-Encoding Variational Bayes.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Auto-Encoding Variational Bayes

Reference 14

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Observation 8b6b5a6f-097d-42a4-95d4-4df0e8f10437 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Adam: A Method for Stochastic Optimization

Reference 15

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Observation 1d048aa7-68e8-4018-aa41-58d1cfc86ae6 · outbound

This paper cites Multi-View Large Reconstruction Model via Geometry-Aware Positional Encoding and Attention.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Multi-View Large Reconstruction Model via Geometry-Aware Positional Encoding and Attention

Reference 16

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Observation 0e40484f-6927-4f5f-83fb-1086d969259c · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object, 2023.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Zero-1-to-3: Zero-shot one image to 3d object, 2023

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 4ddc97e0-3a76-463c-847f-992ce749f8b9 · outbound

This paper cites Progressively optimized local radiance fields for robust view synthesis.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Progressively optimized local radiance fields for robust view synthesis

Reference 18

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Observation 44e67f67-4215-4c8f-986f-71f6253ba038 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 19

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Observation b1b35548-f94d-4824-a8f1-0b83c0c159fd · outbound

This paper cites GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers

Reference 20

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Observation f5a7225b-1670-4171-aca1-20f7f1cccb3c · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos DreamFusion: Text-to-3D using 2D Diffusion

Reference 21

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Observation 15d375ba-6586-4d54-af24-02e0b1b2e257 · outbound

This paper cites an unresolved cited work.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Unresolved cited work

Reference 22

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Observation 3e53a317-2751-4578-8179-877bfb11f0a5 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 23

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Observation cd67c323-8f77-458d-8778-34a867f989b1 · outbound

This paper cites Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

Reference 24

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Observation 7f65b900-42ba-4dda-8d38-03495595d2b4 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models, 2021.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos High-resolution image syn- thesis with latent diffusion models, 2021

Reference 25

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 1ee1a018-3139-4585-9ad5-c890d929d964 · outbound

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

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos High-resolution image synthesis with latent diffusion models

Reference 26

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Observation 172da6f9-6161-44e1-969b-60767a5f48d2 · outbound

This paper cites Structure-from-motion revisited.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Structure-from-motion revisited

Reference 27

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Observation ca59a7bd-29ba-44fe-8e40-db1d16dfa7c6 · outbound

This paper cites Structure- from-motion revisited.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Structure- from-motion revisited

Reference 28

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Observation cd014ddf-7b41-4cac-834a-c2ba6b265e70 · outbound

This paper cites Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS

Reference 29

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Observation c7a9357d-9da8-4baf-a799-b9ceac39751c · outbound

This paper cites DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

Reference 30

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Observation 93450e1b-0e83-48bc-b2ab-2d581ad6da6e · outbound

This paper cites Attention is all you need.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Attention is all you need

Reference 31

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Observation 973c0d4e-2821-47fe-beed-a0a1c3f1a228 · outbound

This paper cites NeuS: Learning neural im- plicit surfaces by volume rendering for multi-view recon- struction.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos NeuS: Learning neural im- plicit surfaces by volume rendering for multi-view recon- struction

Reference 32

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Source-reported events for the cited work

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Observation c9a36935-3970-4fbe-bc59-511dd46c617a · outbound

This paper cites PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction

Reference 33

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Observation 32c03ccc-827b-4f3c-b0a5-b53e2885be59 · outbound

This paper cites NeRF--: Neural Radiance Fields Without Known Camera Parameters.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos NeRF--: Neural Radiance Fields Without Known Camera Parameters

Reference 34

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Observation 71308d1f-1dbf-4e50-b715-8558dbf4891a · outbound

This paper cites latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction

Reference 35

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e0e4d6f4-eb73-4186-80d0-c35f16bcfc15 · outbound

This paper cites Synsin: End-to-end view synthesis from a sin- gle image.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Synsin: End-to-end view synthesis from a sin- gle image

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:13:22.093309Z digest=sha256:86c107c8d5b7c87f86ee3cd80277a1deb8ada9b2167c5065487d8b5dbaea8fa5

Observation 424c12c6-8c77-4470-9491-2e55d5155066 · outbound

This paper cites SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:13:22.214985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:13:22.098176Z digest=sha256:a96d2b401d774ed73bbd35af3ff51bb47c671489352a1ad602b97b1b086eacaa

Observation 32b22f58-4acc-4a94-92ba-640e0788c6b0 · outbound

This paper cites Murf: Multi-baseline radiance fields.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Murf: Multi-baseline radiance fields

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:13:22.485372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:13:22.102459Z digest=sha256:1e3108097cc71867e8b3ae8cf49e7b634742e936d35f4833324b85dc60894a03

Observation 5637f67b-993c-4561-9bee-ba326ce46ecb · outbound

This paper cites Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:13:22.470873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:13:22.106894Z digest=sha256:9877160b9152641e9091cd7ce0305ed8445190b09de295b4d43304defa9a0101

Observation 7d8d1ece-559a-427a-bf3a-d722a221e178 · outbound

This paper cites pixelnerf: Neural radiance fields from one or few images.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos pixelnerf: Neural radiance fields from one or few images

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-10T20:13:22.111080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:13:22.111080Z digest=sha256:d209063cf5fed97a83440bcc69fd40e6d939fa3258cab136d72f7c7f7646d555

Observation 51908134-4ccc-4f46-8ae5-77f5dade3c9e · outbound

This paper cites GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-10T20:13:22.115564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:13:22.115564Z digest=sha256:54ad1c00c70ce2ac75a3ac0790b87ec73864e8ffe33f4b90d48b242794a8e26f

Observation ce83135d-cc68-4096-b882-0f43b21f5d69 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos The unreasonable effectiveness of deep features as a perceptual metric

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-10T20:13:22.120052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:13:22.120052Z digest=sha256:1217e21b8efeaadb171a9d85b583e61a3acfd559aef505648d89450b5bba2bab

Observation 3f74014e-0f3c-497a-9530-88b736e170b8 · outbound

This paper cites Stereo Magnification: Learning View Synthesis using Multiplane Images.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos Stereo Magnification: Learning View Synthesis using Multiplane Images

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T20:13:22.124644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:13:22.124644Z digest=sha256:7d0551855b0d92b2649744c6e027b59fd5480ff625891c17cb0396564a4d1bbf

Observation 6c73a3e0-739f-471f-903b-1c59c129eee7 · outbound

This paper cites a cupcake with whipped cream.

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos a cupcake with whipped cream

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:13:22.441645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:13:22.130227Z digest=sha256:aeddfa801368a83f4a4e09db9055ec866772ffa76d289c7aa9d4be87e6f7bece

Pith citing papers

No inbound Pith citation observations are available.