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Geometry Fidelity for Spherical Images

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Anders Christensen, Nooshin Mojab, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther, Mar Gonzalez-Franco, Andrea Colaco

Computer Vision and Pattern Recognition · Submitted 25 Jul 2024

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

The authors' main claim In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID.

how it is stated · asserted 1.00 read the record →

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arXiv ID2407.18207 (version 1)
Pith IDASGIST53, Pith's permanent record of this paper and its review
CitationAnders Christensen, Andrea Colaco, Karan Ahuja, Khushman Patel, Mar Gonzalez-Franco, Nooshin Mojab, Ole Winther, Zeynep Akata. "Geometry Fidelity for Spherical Images." arXiv:2407.18207 (2024). https://pith.science/paper/ASGIST53
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,484 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.14 · human participants 0.03 · controlled comparison 0.62 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.91 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fr\'echet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely Omnidirectional FID (OmniFID) and Discontinuity Score (DS). OmniFID is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID.

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 Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. background 1.00 0.04 0.02 asserted 1.00
s1 However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. background 0.83 0.05 0.04 asserted 1.00
s2 Here, we show that direct application of Fr\'echet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. result 0.47 0.86 0.45 asserted 1.00
s3 We introduce two quantitative metrics accounting for geometric constraints, namely Omnidirectional FID (OmniFID) and Discontinuity Score (DS). contribution 0.98 0.95 0.28 asserted 1.00
s4 OmniFID is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. method 0.86 0.84 0.09 asserted 1.00
s5 DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. method 0.78 0.81 0.05 asserted 1.00
s6 In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID. result 1.00 0.94 0.62 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • 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

Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fr\'echet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely Omnidirectional FID (OmniFID) and Discontinuity Score (DS). OmniFID is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID.

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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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