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.

我们提出了一种新方法，通过建模原始信号的梯度来实现更高效的存储和计算性能，使用稀疏的梯度进行视图合成，从而使计算性能提高了 100-1000 倍，同时可以通过解决一个具有线性计算复杂度的泊松方程来恢复二维图像。该方法在人体建模和室内环境建模等各种应用中都可应用。