The Gaussian splatting methods are getting popular. However, their loss function only contains the $\ell_1$ norm and the structural similarity between the rendered and input images, without considering the edges in these images. It is well-known that the edges in an image provide important information. Therefore, in this paper, we propose an Edge Guided Gaussian Splatting (EGGS) method that leverages the edges in the input images. More specifically, we give the edge region a higher weight than the flat region. With such edge guidance, the resulting Gaussian particles focus more on the edges instead of the flat regions. Moreover, such edge guidance does not crease the computation cost during the training and rendering stage. The experiments confirm that such simple edge-weighted loss function indeed improves about $1\sim2$ dB on several difference data sets. With simply plugging in the edge guidance, the proposed method can improve all Gaussian splatting methods in different scenarios, such as human head modeling, building 3D reconstruction, etc.

翻译：高斯散斑方法正逐渐流行。然而，现有方法的损失函数仅包含渲染图像与输入图像之间的$\ell_1$范数和结构相似性，未考虑图像中的边缘信息。众所周知，图像边缘包含重要信息。因此，本文提出了一种利用输入图像边缘信息的边缘引导高斯散斑方法（EGGS）。具体而言，我们为边缘区域赋予比平坦区域更高的权重。通过此类边缘引导，生成的高斯粒子将更聚焦于边缘而非平坦区域。此外，该边缘引导机制在训练和渲染阶段不会增加计算成本。实验表明，这种简单的边缘加权损失函数在不同数据集上可提升约$1\sim2$ dB的性能。通过简单引入边缘引导，所提方法能够改进各类场景下的高斯散斑方法，例如人像建模、建筑三维重建等。