Paper Citation Record · LEDGER

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.22433.

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

pith.paper-citation-record.v1
2506.22433 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:11:28.900273Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

41 of 41 outbound references displayed

verified exact0
verified fuzzy35
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation def8c097-7e7a-42ee-a206-724f1fb3f100 · outbound

This paper cites Mip-nerf 360: Unbounded anti-aliased neural radiance fields.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Mip-nerf 360: Unbounded anti-aliased neural radiance fields

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:34.862653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4f9935d2-ad3a-4ef0-bf64-d61017433dc5 · outbound

This paper cites PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:11:25.666682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05f0832c-b92f-4241-8f02-5ccf1d34ceb3 · outbound

This paper cites Depth-supervised NeRF: Fewer views and faster training for free.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Depth-supervised NeRF: Fewer views and faster training for free

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:34.519382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4037ca4c-7dec-4685-85ad-5321c6bcc465 · outbound

This paper cites Accurate, dense, and ro- bust multiview stereopsis.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Accurate, dense, and ro- bust multiview stereopsis

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:34.283882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a2aaffee-95c9-4c2b-9254-c1ba3ac28d71 · outbound

This paper cites A survey of uncertainty in deep neural networks.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields A survey of uncertainty in deep neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:34.129690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d8a2a37c-a4c5-4c2d-855e-993a87324a25 · outbound

This paper cites an unresolved cited work.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:11:33.997866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a7224f1c-1b9d-473d-9b28-bfcb75e99072 · outbound

This paper cites Bayes’ Rays: Uncertainty quantifica- tion in neural radiance fields.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Bayes’ Rays: Uncertainty quantifica- tion in neural radiance fields

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:33.867607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd0dc827-2a17-44ee-b975-9d725335d85f · outbound

This paper cites Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:33.738589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 44b6fbc4-671a-4fb9-a505-06f77c2f9434 · outbound

This paper cites Scone: Surface coverage optimization in unknown environ- ments by volumetric integration.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Scone: Surface coverage optimization in unknown environ- ments by volumetric integration

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:33.625313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b2592955-f853-4afd-90f2-0c66139c735b · outbound

This paper cites Macarons: Mapping and coverage anticipation with rgb online self-supervision.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Macarons: Mapping and coverage anticipation with rgb online self-supervision

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:33.474835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6fff0351-67f1-4fbf-86c8-2a48cb3bf82c · outbound

This paper cites Cg-slam: Efficient dense rgb-d slam in a consistent uncertainty-aware 3d gaussian field.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Cg-slam: Efficient dense rgb-d slam in a consistent uncertainty-aware 3d gaussian field

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:33.341689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8a1d0812-d2ab-4f57-8b83-9a80cfa8f62f · outbound

This paper cites Fisherrf: Ac- tive view selection and mapping with radiance fields using fisher information.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Fisherrf: Ac- tive view selection and mapping with radiance fields using fisher information

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:33.210547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a2f563e7-2d63-4828-b370-27bbba9aa34b · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In NeurIPS,.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields What uncertainties do we need in bayesian deep learning for computer vision? In NeurIPS,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:33.083783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 40e0ca0c-6449-4fe9-bb30-8ac13b035360 · outbound

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

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields 3d gaussian splatting for real-time radiance field rendering

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:32.942611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f56e54a-6eee-4f4e-a47f-21cc8b1a79d5 · outbound

This paper cites 4d gaus- sian splatting in the wild with uncertainty-aware regulariza- tion.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields 4d gaus- sian splatting in the wild with uncertainty-aware regulariza- tion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:32.762259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b638d98e-5103-44f6-b646-a210bae2af91 · outbound

This paper cites Sources of uncertainty in 3d scene reconstruction.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Sources of uncertainty in 3d scene reconstruction

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:32.601451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1f2c5728-c91e-4ca2-accc-1fb816088b9b · outbound

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

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Tanks and temples: Benchmarking large-scale scene reconstruction

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:11:26.898851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33234e57-e7f0-4496-a239-fd01cb507dda · outbound

This paper cites Uncertainty guided pol- icy for active robotic 3d reconstruction using neural radiance fields.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Uncertainty guided pol- icy for active robotic 3d reconstruction using neural radiance fields

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:32.452128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4824f142-9e82-439a-a0ac-cfb0cea97aba · outbound

This paper cites Manifold sampling for differentiable uncer- tainty in radiance fields.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Manifold sampling for differentiable uncer- tainty in radiance fields

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:32.279983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 91880e5f-42e0-4018-97db-be6551a1de52 · outbound

This paper cites Srinivasan, Matthew Tancik, Jonathan T.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Srinivasan, Matthew Tancik, Jonathan T

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:32.116824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0e4037f1-c28d-4df3-9850-b1397c2935c8 · outbound

This paper cites Ac- tivenerf: Learning where to see with uncertainty estimation.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Ac- tivenerf: Learning where to see with uncertainty estimation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:31.902750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb1886dd-d561-47c7-be2b-62fae212c058 · outbound

This paper cites On the confidence of stereo matching in a deep- learning era: a quantitative evaluation.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields On the confidence of stereo matching in a deep- learning era: a quantitative evaluation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:31.747721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ebeee658-3c49-4f96-a865-b010ee7b3c1b · outbound

This paper cites Neurar: Neural uncertainty for autonomous 3d reconstruction with implicit neural representations.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Neurar: Neural uncertainty for autonomous 3d reconstruction with implicit neural representations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:31.571725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3527ab3d-e530-40f3-bf87-b7bf2a65ad09 · outbound

This paper cites Nerf on-the-go: Exploiting uncertainty for distractor-free nerfs in the wild.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Nerf on-the-go: Exploiting uncertainty for distractor-free nerfs in the wild

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:11:27.420286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68e91fc9-5843-49db-a913-4de4f674a585 · outbound

This paper cites Barron, Ben Mildenhall, Pratul P.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Barron, Ben Mildenhall, Pratul P

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:31.432625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7e3c652-e8b6-45df-8516-f747b8515d21 · outbound

This paper cites Self-evolving depth-supervised 3d gaussian splatting from rendered stereo pairs.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Self-evolving depth-supervised 3d gaussian splatting from rendered stereo pairs

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:31.301995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fccc7280-7578-4a71-a25b-be95611daaa6 · outbound

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

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields A multi-view stereo benchmark with high- resolution images and multi-camera videos

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:31.140749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f19dc2d4-d836-4261-931c-53c547e4c1f1 · outbound

This paper cites Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:11:27.784004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d74dc978-5238-46bf-a19c-6cd717449e41 · outbound

This paper cites Conditional-flow nerf: Accurate 3d mod- elling with reliable uncertainty quantification.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Conditional-flow nerf: Accurate 3d mod- elling with reliable uncertainty quantification

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:30.972856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3443b3b5-4cbe-4231-826e-ffda635dad12 · outbound

This paper cites Estimating 3d uncertainty field: Quantify- ing uncertainty for neural radiance fields.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Estimating 3d uncertainty field: Quantify- ing uncertainty for neural radiance fields

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:30.755384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 074a96e2-179d-429a-93ed-2df4367f977a · outbound

This paper cites imap: Implicit mapping and positioning in real-time.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields imap: Implicit mapping and positioning in real-time

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:30.589434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2c420039-5504-4103-b14a-6cb7c04db0b2 · outbound

This paper cites Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:11:28.145240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8acd6a10-c8af-46c6-b690-d2ee81e67fd2 · outbound

This paper cites Density-aware nerf ensembles: Quantifying predictive un- certainty in neural radiance fields.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Density-aware nerf ensembles: Quantifying predictive un- certainty in neural radiance fields

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:30.400567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9760d681-07d5-4109-9c04-3fadce90f9b3 · outbound

This paper cites Dn-splatter: Depth and normal priors for gaussian splatting and meshing.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Dn-splatter: Depth and normal priors for gaussian splatting and meshing

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:30.257868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d8e902e8-6390-46c2-9151-38a7273ad372 · outbound

This paper cites Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:30.066068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60b4d8b8-dcce-4d7c-87f7-8a47afd5d614 · outbound

This paper cites Neural visibility field for uncertainty-driven active mapping.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Neural visibility field for uncertainty-driven active mapping

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:29.897134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa995bb1-97c2-4b69-8511-60f46c241f5e · outbound

This paper cites Active implicit object reconstruction us- ing uncertainty-guided next-best-view optimization.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Active implicit object reconstruction us- ing uncertainty-guided next-best-view optimization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:29.737647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6aded81-3643-4dc9-8def-ff730c36275d · outbound

This paper cites Active neural mapping.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Active neural mapping

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:29.590024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 94048e4f-78ba-4a86-a276-442f9fed07fb · outbound

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

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Scannet++: A high-fidelity dataset of 3d indoor scenes

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:29.430941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d03f2cfe-b690-4108-af28-3677cbb7e944 · outbound

This paper cites Efficient 3d object segmentation from densely sampled light fields with applications to 3d re- construction.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields Efficient 3d object segmentation from densely sampled light fields with applications to 3d re- construction

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:29.265224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e8b4016-ce22-42d4-96f2-ac4862385750 · outbound

This paper cites WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields.

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:29.107172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.