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OmniGS: Fast Radiance Field Reconstruction using Omnidirectional Gaussian Splatting
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Photorealistic reconstruction relying on 3D Gaussian Splatting has shown promising potential in various domains. However, the current 3D Gaussian Splatting system only supports radiance field reconstruction using undistorted perspective images. In this paper, we present OmniGS, a novel omnidirectional Gaussian splatting system, to take advantage of omnidirectional images for fast radiance field reconstruction. Specifically, we conduct a theoretical analysis of spherical camera model derivatives in 3D Gaussian Splatting. According to the derivatives, we then implement a new GPU-accelerated omnidirectional rasterizer that directly splats 3D Gaussians onto the equirectangular screen space for omnidirectional image rendering. We realize differentiable optimization of the omnidirectional radiance field without the requirement of cube-map rectification or tangent-plane approximation. Extensive experiments conducted in egocentric and roaming scenarios demonstrate that our method achieves state-of-the-art reconstruction quality and high rendering speed using omnidirectional images. The code will be publicly available.
Forward citations
Cited by 3 Pith papers
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Seam360GS: Seamless 360{\deg} Gaussian Splatting from Real-World Omnidirectional Images
Training 3D Gaussian splatting with a learnable dual-fisheye distortion model, then turning it off at inference, renders seamless 360-degree novel views from imperfect panoramas.
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Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction
A hybrid invertible-network distortion field plus cubemap rendering lets Gaussian Splatting reconstruct scenes from uncalibrated fisheye photos, outperforming prior fisheye methods and reducing the number of captures needed.
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ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization
ErpGS improves omnidirectional 3D Gaussian Splatting with geometric, scale, and distortion-aware regularizations plus a viewpoint-dependent mask, beating prior methods on public benchmarks.
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