GS++: Error Analyzing and Optimal Gaussian Splatting
arxiv(2024)
摘要
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
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 ϕ. 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. Experimental validation
further confirms that this projection methodology reduces artifacts, resulting
in a more convincingly realistic rendering.
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