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ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field

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Kiyohiro Nakayama, Mikaela Angelina Uy, Yang You, Ke Li, Leonidas J. Guibas

Computer Vision and Pattern Recognition · Submitted 16 Jan 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show that modeling per-point provenance during the NeRF optimization enriches the model with information on triangulation leading to improvements in novel view synthesis and uncertainty estimation under the challenging sparse, unconstrained view setting against competitive baselines.

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arXiv ID2401.08140 (version 3)
Pith IDMLR3ZIZ2, Pith's permanent record of this paper and its review
CitationKe Li, Kiyohiro Nakayama, Leonidas J. Guibas, Mikaela Angelina Uy, Yang You. "ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field." arXiv:2401.08140 (2024). https://pith.science/paper/MLR3ZIZ2
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,370 input tokens.

What the abstract itself states, as probabilities: claims first 0.69 · states limitation 0.08 · human participants 0.02 · controlled comparison 0.85 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.92 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

Neural radiance fields (NeRFs) have gained popularity with multiple works showing promising results across various applications. However, to the best of our knowledge, existing works do not explicitly model the distribution of training camera poses, or consequently the triangulation quality, a key factor affecting reconstruction quality dating back to classical vision literature. We close this gap with ProvNeRF, an approach that models the \textbf{provenance} for each point -- i.e., the locations where it is likely visible -- of NeRFs as a stochastic field. We achieve this by extending implicit maximum likelihood estimation (IMLE) to functional space with an optimizable objective. We show that modeling per-point provenance during the NeRF optimization enriches the model with information on triangulation leading to improvements in novel view synthesis and uncertainty estimation under the challenging sparse, unconstrained view setting against competitive baselines.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Neural radiance fields (NeRFs) have gained popularity with multiple works showing promising results across various applications. background 1.00 0.03 0.02 asserted 0.98
s1 However, to the best of our knowledge, existing works do not explicitly model the distribution of training camera poses, or consequently the triangulation quality, a key factor affecting reconstruction quality dating back to classical vision literature. gap 1.00 0.13 0.04 asserted 0.87
s2 We close this gap with ProvNeRF, an approach that models the \textbf{provenance} for each point -- i.e., the locations where it is likely visible -- of NeRFs as a stochastic field. contribution 0.98 0.87 0.17 asserted 0.99
s3 We achieve this by extending implicit maximum likelihood estimation (IMLE) to functional space with an optimizable objective. method 0.99 0.92 0.05 asserted 0.99
s4 We show that modeling per-point provenance during the NeRF optimization enriches the model with information on triangulation leading to improvements in novel view synthesis and uncertainty estimation under the challenging sparse, unconstrained view setting against competitive baselines. result 0.99 0.95 0.88 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.81.

What was examined, and what was not

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  • references and citing sentences
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  • P abstract
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  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Neural radiance fields (NeRFs) have gained popularity with multiple works showing promising results across various applications. However, to the best of our knowledge, existing works do not explicitly model the distribution of training camera poses, or consequently the triangulation quality, a key factor affecting reconstruction quality dating back to classical vision literature. We close this gap with ProvNeRF, an approach that models the \textbf{provenance} for each point -- i.e., the locations where it is likely visible -- of NeRFs as a stochastic field. We achieve this by extending implicit maximum likelihood estimation (IMLE) to functional space with an optimizable objective. We show that modeling per-point provenance during the NeRF optimization enriches the model with information on triangulation leading to improvements in novel view synthesis and uncertainty estimation under the challenging sparse, unconstrained view setting against competitive baselines.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Cites as background 1 paper
  • Follow-ups on NeRF further tackle novel view synthesis under more difficult scenarios such as unconstrained photo collections [ref] , unbounded [ref] , dynamic [ref] and deformable [ref] scenes, and reflective objects [ref] .

    Neural Reflectance Fields for Appearance Acquisition · Related Works · 0.85

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