ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images
arxiv(2024)
摘要
With the advent of virtual reality technology, omnidirectional image (ODI)
rescaling techniques are increasingly embraced for reducing transmitted and
stored file sizes while preserving high image quality. Despite this progress,
current ODI rescaling methods predominantly focus on enhancing the quality of
images in equirectangular projection (ERP) format, which overlooks the fact
that the content viewed on head mounted displays (HMDs) is actually a rendered
viewport instead of an ERP image. In this work, we emphasize that focusing
solely on ERP quality results in inferior viewport visual experiences for
users. Thus, we propose ResVR, which is the first comprehensive framework for
the joint Rescaling and Viewport Rendering of ODIs. ResVR allows obtaining LR
ERP images for transmission while rendering high-quality viewports for users to
watch on HMDs. In our ResVR, a novel discrete pixel sampling strategy is
developed to tackle the complex mapping between the viewport and ERP, enabling
end-to-end training of ResVR pipeline. Furthermore, a spherical pixel shape
representation technique is innovatively derived from spherical differentiation
to significantly improve the visual quality of rendered viewports. Extensive
experiments demonstrate that our ResVR outperforms existing methods in viewport
rendering tasks across different fields of view, resolutions, and view
directions while keeping a low transmission overhead.
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