  3D Gaussian Splatting has garnered extensive attention and application in
real-time neural rendering. Concurrently, concerns have been raised about the
limitations of this technology in aspects such as point cloud storage,
performance, and robustness in sparse viewpoints, leading to various
improvements. However, there has been a notable lack of attention to the
fundamental problem of projection errors introduced by the local affine
approximation inherent in the splatting itself, and the consequential impact of
these errors on the quality of photo-realistic rendering. This paper addresses
the projection error function of 3D Gaussian Splatting, commencing with the
residual error from the first-order Taylor expansion of the projection
function. The analysis establishes a correlation between the error and the
Gaussian mean position. Subsequently, leveraging function optimization theory,
this paper analyzes the function's minima to provide an optimal projection
strategy for Gaussian Splatting referred to Optimal Gaussian Splatting, which
can accommodate a variety of camera models. Experimental validation further
confirms that this projection methodology reduces artifacts, resulting in a
more convincingly realistic rendering.
