Summary of Geometry Fidelity For Spherical Images, by Anders Christensen et al.
Geometry Fidelity for Spherical Images
by Anders Christensen, Nooshin Mojab, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther, Mar Gonzalez-Franco, Andrea Colaco
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces two novel metrics to quantify the geometric fidelity of spherical or omni-directional images in computer vision applications. These metrics, OmniFID and Discontinuity Score (DS), address the limitations of Fréchet Inception Distance (FID) when applied directly to spherical images. OmniFID is an extension of FID that captures field-of-view requirements through cubemap projections, while DS measures seam alignment across borders of 2D representations. The paper demonstrates the effectiveness of these metrics in detecting geometry fidelity issues undetected by FID. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spherical images are a new way to show pictures and videos that can be used for many computer vision tasks. However, these images have some special properties that make it hard to compare them to regular 2D images. This paper introduces two new ways to measure how well spherical images match the originals. These metrics, OmniFID and Discontinuity Score (DS), take into account the special features of spherical images. The paper shows that these metrics are better than Fréchet Inception Distance (FID) at detecting when spherical images don’t look like the real thing. |
Keywords
* Artificial intelligence * Alignment
