Paper Citation Record · LEDGER

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 2 inbound Pith citation observations for arXiv:2411.19458.

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

pith.paper-citation-record.v1
2411.19458 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:13:13.583780Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:49:54.935447Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact1
verified fuzzy3
unresolved21
parse uncertain0
malformed identifier3
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 084c799b-533d-4215-9c35-03011c8fda8e · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Deep ViT Features as Dense Visual Descriptors

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.447417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d1700c0-9bbf-47b1-8827-120f98e0ef94 · outbound

This paper cites Unsupervised Semantic Correspondence Using Stable Diffusion.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Unsupervised Semantic Correspondence Using Stable Diffusion

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.473403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3be2a29-69bf-4b3b-815f-916f3907dc15 · outbound

This paper cites CoTracker: It is Better to Track Together.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning CoTracker: It is Better to Track Together

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.483686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23437aa2-8912-41a0-95d3-1096b1070e3d · outbound

This paper cites ImageNet3D: Towards General-Purpose Object-Level 3D Understanding.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning ImageNet3D: Towards General-Purpose Object-Level 3D Understanding

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:13:13.764593Z

Source-reported events for the cited work

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

Observation 53b30ca6-05a5-4db6-8013-e65d5696cb8c · outbound

This paper cites FoundPose: Unseen Object Pose Estimation with Foundation Features.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning FoundPose: Unseen Object Pose Estimation with Foundation Features

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.507553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26dfd44e-798e-4e6b-9058-2ba3c40d5543 · outbound

This paper cites Indoor segmentation and sup- port inference from rgbd images.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Indoor segmentation and sup- port inference from rgbd images

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:13:13.994035Z

Source-reported events for the cited work

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

Observation 08f68525-986f-4763-96e7-eb404484e65e · outbound

This paper cites Emergent Correspondence from Image Diffusion.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Emergent Correspondence from Image Diffusion

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.517195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c5b601c-3725-4c33-bc8a-1205c4440c4a · outbound

This paper cites DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.521992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7390f044-d4a4-4a46-bffd-2cf64e4fcd16 · outbound

This paper cites Denoising Vision Transformers.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Denoising Vision Transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.536203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8459537-5555-43b6-81bc-e3fdb0cc6961 · outbound

This paper cites Improving 2D Feature Representations by 3D-Aware Fine-Tuning.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Improving 2D Feature Representations by 3D-Aware Fine-Tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.541440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aba06a8f-d19e-4f9b-93d4-05852ed85ff6 · outbound

This paper cites A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.546130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69609d72-5b4f-479b-8f2d-a27be9d48e61 · outbound

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

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Stereo Magnification: Learning View Synthesis using Multiplane Images

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.550805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd75ebbc-ded2-4c52-8a04-aaafb470e6a9 · outbound

This paper cites (2023) 22.60 36.84 58.88 19.12 finetuned 30.61 43.65 61.78 17.98 DINOv2-Reg Darcet et al.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning (2023) 22.60 36.84 58.88 19.12 finetuned 30.61 43.65 61.78 17.98 DINOv2-Reg Darcet et al

Reference 24

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T10:13:13.961678Z

Source-reported events for the cited work

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

Observation c7bae478-7674-4524-938f-1767a060a90e · outbound

This paper cites We hypothesize that FiT’s poor performance stems from its naive ap- proach to learning 3D consistency through an explicit 3D Gaussian field.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning We hypothesize that FiT’s poor performance stems from its naive ap- proach to learning 3D consistency through an explicit 3D Gaussian field

Reference 25

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T10:13:13.946905Z

Source-reported events for the cited work

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

Observation 06861564-164e-4c48-8219-9cd69a71f2bc · outbound

This paper cites Nonetheless, we do expect and observe improvements in non-ViT based methods like ConvNeXt.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Nonetheless, we do expect and observe improvements in non-ViT based methods like ConvNeXt

Reference 26

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T10:13:13.930568Z

Source-reported events for the cited work

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

Observation 13030a6b-bcb7-4bc8-8ee9-50c019393ecf · outbound

This paper cites an unresolved cited work.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:13:13.915903Z

Source-reported events for the cited work

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

Observation 6ffe59a6-2072-4762-bab9-c13f501693e3 · outbound

This paper cites an unresolved cited work.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:13:13.901049Z

Source-reported events for the cited work

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

Observation f2cecead-7354-404d-a014-64b1fff98527 · outbound

This paper cites WildGaussians: 3D Gaussian Splatting in the Wild.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning WildGaussians: 3D Gaussian Splatting in the Wild

Reference 1967

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.488728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57466fdb-0495-419f-8cff-26e69a4f5c2b · outbound

This paper cites SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation

Reference 1995

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.526681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 129f614e-147f-4a47-bf71-e0705ad614e3 · outbound

This paper cites PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.531520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe008135-9cde-4a39-b984-4efa60aa3b51 · outbound

This paper cites Vision Transformers Need Registers.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Vision Transformers Need Registers

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.458425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dda7c852-2792-401f-b8fd-9752bf18d101 · outbound

This paper cites • Average Pixel Error (APE): Suppose we have N objects, each rendered from k = 42different views.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning • Average Pixel Error (APE): Suppose we have N objects, each rendered from k = 42different views

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:13:13.978111Z

Source-reported events for the cited work

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

Observation fc891257-3dd9-445d-aff2-4e1c1fdf802e · outbound

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

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning DINOv2: Learning Robust Visual Features without Supervision

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.503323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa92ba13-574a-45f1-9839-aaa32e2530c7 · outbound

This paper cites Generative Models: What Do They Know? Do They Know Things? Let's Find Out!.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Generative Models: What Do They Know? Do They Know Things? Let's Find Out!

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.468618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f318672-5422-4b52-bedb-6ee3819a0eba · outbound

This paper cites Zero-Shot Image Feature Consensus with Deep Functional Maps.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Zero-Shot Image Feature Consensus with Deep Functional Maps

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.453290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6f91d1d-5b1f-4d0f-8417-ef1c309c5b5a · outbound

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

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.463667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a03c56c-7e59-4e35-80d2-91dd4374c4a7 · outbound

This paper cites Bop challenge 2020 on 6d object localization.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning Bop challenge 2020 on 6d object localization

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:13:14.007935Z

Source-reported events for the cited work

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

Observation a6d0d452-7e63-4645-9482-8149879299ec · outbound

This paper cites MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare.

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:13.493165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6a796680-a3f6-4364-9574-c59500630a8f · inbound

UniPose9D: Universal Category-Agnostic Object Pose Estimation cites this paper.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-14T01:15:46.555348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 527f767b-c365-4942-8bdf-2c944d854ef2 · inbound

SeeSE3: Emergence of 3D Space in Vision Features cites this paper.

SeeSE3: Emergence of 3D Space in Vision Features Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T02:49:54.935447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.