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OmniPrior: A Multi-Prior-Guided Omnidirectional Representation of Dynamic Scenes in Overlapping Ultra-Wide Multi-Fisheye Videos
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DOI：10.1109/tvcg.2026.3703852.png)
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Abstract
En 中文
Omnidirectional capture of dynamic scenes facilitates the creation of immersive virtual reality assets and holistic scene understanding. Outward-facing multi-fisheye camera rigs offer an efficient solution for full-scene coverage, using fewer lenses than conventional pinhole arrays while enabling all-directional observation of complex, time-varying environments. By continuously recording scene evolution from every angle, these systems naturally enable a richer characterization of dynamic interactions. Despite these advantages, dynamic scene modeling in this setting remains underexplored. Existing methods, typically designed for fixed pinhole configurations or monocular setups, rely heavily on photometric cues and often neglect the strong geometric and semantic priors inherent in multi-fisheye omnidirectional data. To address this gap, we present OmniPrior, a Gaussian Splatting-based framework for outward-facing, multi-fisheye omnidirectional capture. Our approach incorporates metric-geometry-aware initialization with multi-prior guidance, introducing a dynamicness-aware Gaussian representation that encodes both object motion and subtle temporal variations. The resulting representations are physically consistent and temporally stable. Extensive experiments validate the effectiveness of our method in novel view synthesis across new viewpoints and timestamps. We demonstrate its utility in two representative applications derived from our learned representations: 6DoF rendering with flexible FoV and motion-freeze rendering.
Keywords:
Gaussian splatting
omnidirectional representation
multi-fisheye video handling
scene reconstruction
virtual reality
and multi-view dynamic scene
Journal
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6.5
Papers:
294
Citations:
2.2W
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Cited Papers