EGGS: 可交换的2D/3D高斯点扩散用于几何-外观平衡的新视角合成
EGGS: Exchangeable 2D/3D Gaussian Splatting for Geometry-Appearance Balanced Novel View Synthesis
AI总结：
EGGS通过融合2D和3D高斯点扩散，平衡外观与几何精度，提升新视角合成的渲染质量与效率。
摘要：
新视角合成（NVS）在计算机视觉和图形学中至关重要，广泛应用于AR、VR和自动驾驶领域。虽然3D高斯点扩散（3DGS）能够实现高逼真的实时渲染，但其存在多视角不一致的问题，限制了几何精度。相比之下，2D高斯点扩散（2DGS）强制多视角一致性，但牺牲了纹理细节。为了解决这些限制，我们提出了可交换的高斯点扩散（EGGS），一种融合2D和3D高斯的混合表示方法，以平衡外观和几何。为此，我们引入了混合高斯光栅化以实现统一渲染，自适应类型交换以在2D和3D高斯之间动态适应，以及频率解耦优化，有效利用每种高斯表示的优势。我们的CUDA加速实现确保了高效的训练和推理。大量实验表明，EGGS在渲染质量、几何精度和效率方面均优于现有方法，为高质量NVS提供了实用的解决方案。
英文摘要：
Novel view synthesis (NVS) is crucial in computer vision and graphics, with wide applications in AR, VR, and autonomous driving. While 3D Gaussian Splatting (3DGS) enables real-time rendering with high appearance fidelity, it suffers from multi-view inconsistencies, limiting geometric accuracy. In contrast, 2D Gaussian Splatting (2DGS) enforces multi-view consistency but compromises texture details. To address these limitations, we propose Exchangeable Gaussian Splatting (EGGS), a hybrid representation that integrates 2D and 3D Gaussians to balance appearance and geometry. To achieve this, we introduce Hybrid Gaussian Rasterization for unified rendering, Adaptive Type Exchange for dynamic adaptation between 2D and 3D Gaussians, and Frequency-Decoupled Optimization that effectively exploits the strengths of each type of Gaussian representation. Our CUDA-accelerated implementation ensures efficient training and inference. Extensive experiments demonstrate that EGGS outperforms existing methods in rendering quality, geometric accuracy, and efficiency, providing a practical solution for high-quality NVS.
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