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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
Published on: December 3, 2013
OmniPrior: A Multi-Prior-Guided Omnidirectional Representation of Dynamic Scenes in Overlapping Ultra-Wide
IEEE Transactions on Visualization and Computer Graphics
|June 15, 2026
Summary
OmniPrior captures dynamic scenes using multi-fisheye cameras, enabling realistic virtual reality and scene understanding. This Gaussian Splatting framework ensures physically consistent and temporally stable representations for novel view synthesis.
Area of Science:
- Computer Vision
- Computer Graphics
- Robotics
Background:
- Omnidirectional capture is crucial for immersive virtual reality (VR) and scene understanding.
- Outward-facing multi-fisheye camera rigs provide efficient full-scene coverage for dynamic environments.
- Existing dynamic scene modeling methods often overlook geometric and semantic priors in multi-fisheye data.
Purpose of the Study:
- To develop a novel framework for dynamic scene modeling using outward-facing multi-fisheye camera data.
- To leverage geometric and semantic priors inherent in multi-fisheye omnidirectional data.
- To create physically consistent and temporally stable dynamic scene representations.
Main Methods:
- Introduced OmniPrior, a Gaussian Splatting-based framework tailored for multi-fisheye omnidirectional capture.
- Incorporated metric-geometry-aware initialization and multi-prior guidance.
- Developed a dynamicness-aware Gaussian representation to encode object motion and temporal variations.
Main Results:
- Achieved effective novel view synthesis across new viewpoints and timestamps.
- Demonstrated physically consistent and temporally stable scene representations.
- Validated the method's effectiveness through extensive experiments.
Conclusions:
- OmniPrior effectively models dynamic scenes captured by multi-fisheye rigs, addressing limitations of existing methods.
- The framework enables high-quality novel view synthesis and realistic scene reconstruction.
- Learned representations support applications like 6DoF rendering and motion-freeze rendering.
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