  The 3D Gaussian splatting methods are getting popular. However, they work
directly on the signal, leading to a dense representation of the signal. Even
with some techniques such as pruning or distillation, the results are still
dense. In this paper, we propose to model the gradient of the original signal.
The gradients are much sparser than the original signal. Therefore, the
gradients use much less Gaussian splats, leading to the more efficient storage
and thus higher computational performance during both training and rendering.
Thanks to the sparsity, during the view synthesis, only a small mount of pixels
are needed, leading to much higher computational performance ($100\sim
1000\times$ faster). And the 2D image can be recovered from the gradients via
solving a Poisson equation with linear computation complexity. Several
experiments are performed to confirm the sparseness of the gradients and the
computation performance of the proposed method. The method can be applied
various applications, such as human body modeling and indoor environment
modeling.
