Seam360GS: Seamless 360° Gaussian Splatting from Real-World Omnidirectional Images
This addresses the issue of visual artifacts in 360-degree content for applications like virtual reality and robotics, representing an incremental improvement over prior rendering methods.
The paper tackled the problem of imperfect 360-degree panoramas from consumer dual-fisheye cameras by introducing a calibration framework integrated with 3D Gaussian splatting, resulting in seamless renderings that outperform existing models in evaluations.
360-degree visual content is widely shared on platforms such as YouTube and plays a central role in virtual reality, robotics, and autonomous navigation. However, consumer-grade dual-fisheye systems consistently yield imperfect panoramas due to inherent lens separation and angular distortions. In this work, we introduce a novel calibration framework that incorporates a dual-fisheye camera model into the 3D Gaussian splatting pipeline. Our approach not only simulates the realistic visual artifacts produced by dual-fisheye cameras but also enables the synthesis of seamlessly rendered 360-degree images. By jointly optimizing 3D Gaussian parameters alongside calibration variables that emulate lens gaps and angular distortions, our framework transforms imperfect omnidirectional inputs into flawless novel view synthesis. Extensive evaluations on real-world datasets confirm that our method produces seamless renderings-even from imperfect images-and outperforms existing 360-degree rendering models.