# ARGS: Advanced Regularization on Aligning Gaussians over the Surface

## TL;DR

This paper introduces two regularization strategies to enhance 3D Gaussian Splatting reconstructions, improving visual fidelity and surface consistency by balancing Gaussian shapes and integrating a neural SDF with Eikonal regularization.

## Contribution

It proposes novel regularizations—rank regularization and neural SDF integration—to improve 3D Gaussian Splatting surface reconstruction quality.

## Key findings

- Enhanced visual fidelity in 3D reconstructions.
- Improved surface coherence and stability.
- More accurate Gaussian primitive shapes.

## Abstract

Reconstructing high-quality 3D meshes and visuals from 3D Gaussian Splatting(3DGS) still remains a central challenge in computer graphics. Although existing models such as SuGaR offer effective solutions for rendering, there is is still room to improve improve both visual fidelity and scene consistency. This work builds upon SuGaR by introducing two complementary regularization strategies that address common limitations in both the shape of individual Gaussians and the coherence of the overall surface. The first strategy introduces an effective rank regularization, motivated by recent studies on Gaussian primitive structures. This regularization discourages extreme anisotropy-specifically, "needle-like" shapes-by favoring more balanced, "disk-like" forms that are better suited for stable surface reconstruction. The second strategy integrates a neural Signed Distance Function (SDF) into the optimization process. The SDF is regularized with an Eikonal loss to maintain proper distance properties and provides a continuous global surface prior, guiding Gaussians toward better alignment with the underlying geometry. These two regularizations aim to improve both the fidelity of individual Gaussian primitives and their collective surface behavior. The final model can make more accurate and coherent visuals from 3DGS data.