Geometry Fidelity for Spherical Images
Anders Christensen*, Nooshin Mojab*, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther, Mar Gonzalez Franco, Andrea Colaco
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 Fréchet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely () and Discontinuity Score (DS). 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, and DS quantify geometry fidelity issues that are undetected by FID."
BibTeX
@inproceedings{eccv2024_geometryfidelity,
title = {Geometry Fidelity for Spherical Images},
author = {Anders Christensen* and Nooshin Mojab* and Khushman Patel and Karan Ahuja and Zeynep Akata and Ole Winther and Mar Gonzalez Franco and Andrea Colaco},
booktitle = {ECCV 2024},
year = {2024}
}