EGGS: Edge Guided Gaussian Splatting for Radiance Fields
Yuanhao Gong
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.