Gaussian Splatting has recently emerged as the go-to representation for reconstructing and rendering 3D scenes. The transition from 3D to 2D Gaussian primitives has further improved multi-view consistency and surface reconstruction accuracy. In this work we highlight the similarity between 2D Gaussian Splatting (2DGS) and billboards from traditional computer graphics. Both use flat semi-transparent 2D geometry that is positioned, oriented and scaled in 3D space. However 2DGS uses a solid color per splat and an opacity modulated by a Gaussian distribution, where billboards are more expressive, modulating the color with a uv-parameterized texture. We propose to unify these concepts by presenting Gaussian Billboards, a modification of 2DGS to add spatially-varying color achieved using per-splat texture interpolation. The result is a mixture of the two representations, which benefits from both the robust scene optimization power of 2DGS and the expressiveness of texture mapping. We show that our method can improve the sharpness and quality of the scene representation in a wide range of qualitative and quantitative evaluations compared to the original 2DGS implementation.
高斯点云技术（Gaussian Splatting）近年来成为三维场景重建与渲染的首选表示方法。通过从三维高斯基元过渡到二维高斯基元（2DGS），多视图一致性和表面重建精度得到了进一步提升。在本研究中，我们指出二维高斯点云（2DGS）与传统计算机图形学中的广告牌（billboards）具有相似性。两者都使用平坦的半透明二维几何体，定位、定向并在三维空间中缩放。然而，2DGS 为每个点云分配了一个固定颜色，并通过高斯分布调节不透明度，而广告牌则更具表现力，利用 uv 参数化纹理调节颜色。 我们提出了 Gaussian Billboards，一种对 2DGS 的改进方法，通过每个点云的纹理插值实现空间变化的颜色。该方法将两种表示形式统一起来，结合了 2DGS 在场景优化中的鲁棒性和纹理映射的表现力。相比于原始 2DGS 实现，我们的方法在广泛的定性和定量评估中表现出更高的清晰度和场景表示质量，为场景渲染提供了更精细的解决方案。