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. For image generation, this poses a full-stack problem: from smaller data-sets to access to tools that can evaluate quality of models. Specifically, we show that direct application of the established evaluation metric Fréchet 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.
Citation
Christensen, A., Mojab, N., Patel, K., Ahuja, K., Akata, Z., Winther, O., Gonzalez-Franco, M. and Colaco, A. (2024). Geometry Fidelity for Spherical Images. In European Conference on Computer Vision. ECCV 2024. Springer, Cham.