Paper Citation Record · LEDGER

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.03851.

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

pith.paper-citation-record.v1
2608.03851 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:10:29.220185Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy23
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d816899c-f40e-42f0-b90b-311051521ca4 · outbound

This paper cites MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.045805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9995b2a9-7359-4a09-8ad1-7b5c60b91dc4 · outbound

This paper cites Transformerfusion: Monocular rgb scene reconstruction using transformers.Advances in Neural In- formation Processing Systems, 34:1403–1414, 2021.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Transformerfusion: Monocular rgb scene reconstruction using transformers.Advances in Neural In- formation Processing Systems, 34:1403–1414, 2021

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.985163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d204dec7-71a4-471f-892c-5df9c752cf99 · outbound

This paper cites MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.055982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6faf7283-a2ec-46de-95ae-9ca25da4c454 · outbound

This paper cites RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.061950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49f28371-44c7-4cd8-aa6f-1687a1b5ae04 · outbound

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

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.067528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be67dd5b-6a92-4423-b771-5e6c26768152 · outbound

This paper cites Deep- videomvs: Multi-view stereo on video with recurrent spatio- temporal fusion.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Deep- videomvs: Multi-view stereo on video with recurrent spatio- temporal fusion

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.951471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 83dacb93-e4b7-41b7-86ea-e19ec5a05f86 · outbound

This paper cites Predicting depth, surface nor- mals and semantic labels with a common multi-scale con- volutional architecture.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Predicting depth, surface nor- mals and semantic labels with a common multi-scale con- volutional architecture

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.930718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63a40deb-f677-464c-b378-a85139493eb8 · outbound

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

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Depth map prediction from a single image using a multi-scale deep net- work.Advances in neural information processing systems, 27, 2014

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.911566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7175e3a0-9b7c-4ce4-8527-dcf2c5066c21 · outbound

This paper cites Rpr-net: A point cloud-based rotation-aware large scale place recognition network.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Rpr-net: A point cloud-based rotation-aware large scale place recognition network

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.894529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af88f880-4e63-430c-beab-a68e991d471f · outbound

This paper cites Multi-view stereo: A tutorial.Foundations and trends® in Computer Graphics and Vision, 9(1-2):1–148, 2015.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Multi-view stereo: A tutorial.Foundations and trends® in Computer Graphics and Vision, 9(1-2):1–148, 2015

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.877525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eaffe0f9-c9c6-4a9c-95e1-a76311284113 · outbound

This paper cites Multi-view stereo by temporal nonparametric fusion.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Multi-view stereo by temporal nonparametric fusion

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.860377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2ea18035-e0d3-40e1-8acd-11e8671016cc · outbound

This paper cites DPSNet: End-to-end Deep Plane Sweep Stereo.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation DPSNet: End-to-end Deep Plane Sweep Stereo

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.102749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3624e518-6e4c-4cec-a431-cadaaada4a44 · outbound

This paper cites Mvsanywhere: Zero-shot multi-view stereo.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Mvsanywhere: Zero-shot multi-view stereo

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.844222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2d0d5a0-9018-4463-91ff-4719925bbe1b · outbound

This paper cites Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.114403Z

Source-reported events for the cited work

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Observation 36f7a18b-a4f3-441f-b5bd-41f316396f5a · outbound

This paper cites Segment any- thing.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Segment any- thing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.119910Z

Source-reported events for the cited work

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Observation a383b989-caff-462f-8f60-8e8c344311b4 · outbound

This paper cites Spatial forcing: Implicit spatial representation align- ment for vision-language-action model.arXiv preprint arXiv:2510.12276, 2025.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Spatial forcing: Implicit spatial representation align- ment for vision-language-action model.arXiv preprint arXiv:2510.12276, 2025

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.125020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06c19f4b-d283-438f-8831-6a94d2b8a05d · outbound

This paper cites Libero: Benchmarking knowl- edge transfer for lifelong robot learning.Advances in Neural Information Processing Systems, 36:44776–44791, 2023.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Libero: Benchmarking knowl- edge transfer for lifelong robot learning.Advances in Neural Information Processing Systems, 36:44776–44791, 2023

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.814993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c646e42a-d21e-4bed-9cc0-483e5f9eb10b · outbound

This paper cites Mixture of ex- perts: a literature survey.Artificial Intelligence Review, 42 (2):275–293, 2014.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Mixture of ex- perts: a literature survey.Artificial Intelligence Review, 42 (2):275–293, 2014

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.797191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c789e0ba-4cfa-4707-bb96-0458094f892c · outbound

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

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation DINOv2: Learning Robust Visual Features without Supervision

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.141909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76db3f21-27c8-48dc-b316-e7e14395619c · outbound

This paper cites Simplere- con: 3d reconstruction without 3d convolutions.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Simplere- con: 3d reconstruction without 3d convolutions

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.780019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d23dc17-7e3a-4c56-9d02-a833f6841d48 · outbound

This paper cites Doubletake: Geometry guided depth estimation.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Doubletake: Geometry guided depth estimation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.764157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2ce235a6-5087-4e5c-88b2-7fc31155977d · outbound

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

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Scene co- ordinate regression forests for camera relocalization in rgb-d images

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.746926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 71067505-83e1-4cf5-983b-2ef1109e789d · outbound

This paper cites V ortx: V olumetric 3d reconstruction with trans- formers for voxelwise view selection and fusion.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation V ortx: V olumetric 3d reconstruction with trans- formers for voxelwise view selection and fusion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.728683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation caf10882-c026-46ba-abaf-1893e31ef27d · outbound

This paper cites Neuralrecon: Real-time coherent 3d re- construction from monocular video.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Neuralrecon: Real-time coherent 3d re- construction from monocular video

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.711453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb826d9b-0abb-48cc-b56b-d34ff07256ad · outbound

This paper cites Depth from motion for smartphone ar.ACM Transactions on Graphics (ToG), 37(6):1–19, 2018.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Depth from motion for smartphone ar.ACM Transactions on Graphics (ToG), 37(6):1–19, 2018

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.693815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9512cbed-47d7-4124-a9b0-58ece4c9846f · outbound

This paper cites Vggt: Vi- sual geometry grounded transformer.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Vggt: Vi- sual geometry grounded transformer

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.676093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7388717-a1eb-412b-917d-0595ef5e160a · outbound

This paper cites Mvdepthnet: Real-time multiview depth estimation neural network.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Mvdepthnet: Real-time multiview depth estimation neural network

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.657677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0de56fb5-1b57-43e2-8df7-0cafe9757f82 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Depth anything: Unleashing the power of large-scale unlabeled data

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.190381Z

Source-reported events for the cited work

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Observation e2f8c282-f936-4d47-8c1b-38547b5b1141 · outbound

This paper cites Depth any- thing v2.Advances in Neural Information Processing Sys- tems, 37:21875–21911, 2024.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Depth any- thing v2.Advances in Neural Information Processing Sys- tems, 37:21875–21911, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.623688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0378cfd1-0e97-42ea-b911-560b669e5d01 · outbound

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

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Mvsnet: Depth inference for unstructured multi-view stereo

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.604009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1968908e-f1df-4a93-8b4e-c2cb9bb79411 · outbound

This paper cites Stablenormal: Reducing diffusion variance for stable and sharp normal.ACM Transactions on Graphics (TOG), 43(6):1–18, 2024.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Stablenormal: Reducing diffusion variance for stable and sharp normal.ACM Transactions on Graphics (TOG), 43(6):1–18, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.585186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1cf5216d-145c-4a31-a20f-d76757a0db71 · outbound

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

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Scannet++: A high-fidelity dataset of 3d in- door scenes

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.564327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9c625a1d-907e-4a65-8856-2f07d5651d3a · outbound

This paper cites Sigmoid loss for language image pre-training.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation Sigmoid loss for language image pre-training

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:29.543100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1376e2ce-61eb-4b83-9661-6a4a3a35b0f7 · outbound

This paper cites MobileSAMv2: Faster Segment Anything to Everything.

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation MobileSAMv2: Faster Segment Anything to Everything

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:29.220185Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.