融合2D与3D高斯点，实现更真实且几何准确的视角合成。
EGGS: Exchangeable 2D/3D Gaussian Splatting for Geometry-Appearance Balanced Novel View Synthesis
- 用2D和3D高斯点混合表示，动态切换以平衡画质与几何精度。
- 在多个数据集上优于现有方法，渲染质量与几何准确性双提升。
- 适合需要高保真视觉效果的AR/VR与自动驾驶场景使用。
新视角合成（NVS）在计算机视觉与图形学中至关重要，广泛应用于增强现实（AR）、虚拟现实（VR）及自动驾驶领域。尽管3D高斯点阵（3DGS）可实现实时渲染并保持高画质，但存在多视角不一致问题，影响几何精度；而2D高斯点阵（2DGS）虽保证多视角一致性，却牺牲纹理细节。为此，本文提出可交换高斯点阵（EGGS），一种融合2D与3D高斯点的混合表示，以平衡外观与几何表现。通过引入统一渲染的混合高斯光栅化、自适应类型交换机制以及频率解耦优化策略，有效发挥两类高斯表示的优势。基于CUDA加速的实现保障了高效训练与推理。大量实验表明，EGGS在渲染质量、几何精度与效率方面均超越现有方法，为高质量NVS提供了实用解决方案。
原文摘要 · Abstract (English)
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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