  One of the key advantages of 3D rendering is its ability to simulate
intricate scenes accurately. One of the most widely used methods for this
purpose is Gaussian Splatting, a novel approach that is known for its rapid
training and inference capabilities. In essence, Gaussian Splatting involves
incorporating data about the 3D objects of interest into a series of Gaussian
distributions, each of which can then be depicted in 3D in a manner analogous
to traditional meshes. It is regrettable that the use of Gaussians in Gaussian
Splatting is currently somewhat restrictive due to their perceived linear
nature. In practice, 3D objects are often composed of complex curves and highly
nonlinear structures. This issue can to some extent be alleviated by employing
a multitude of Gaussian components to reflect the complex, nonlinear structures
accurately. However, this approach results in a considerable increase in time
complexity. This paper introduces the concept of negative Gaussians, which are
interpreted as items with negative colors. The rationale behind this approach
is based on the density distribution created by dividing the probability
density functions (PDFs) of two Gaussians, which we refer to as Diff-Gaussian.
Such a distribution can be used to approximate structures such as donut and
moon-shaped datasets. Experimental findings indicate that the application of
these techniques enhances the modeling of high-frequency elements with rapid
color transitions. Additionally, it improves the representation of shadows. To
the best of our knowledge, this is the first paper to extend the simple
elipsoid shapes of Gaussian Splatting to more complex nonlinear structures.
