ENEA-GS: Enhancing Single-Image 3D Gaussian Splatting with Normal-guided Texture Smoothness and Entropy-based Alpha Regularization

Abstract

Recent advances in 3D content generation have demonstrated the effectiveness of optimization-based frameworks such as DreamGaussian, which combine 3D Gaussian Splatting (3DGS) with diffusion-guided Score Distillation Sampling (SDS) to efficiently synthesize 3D assets from a single image. Compared with earlier NeRF-based approaches, these methods provide significantly faster training and rendering while maintaining competitive visual quality. However, existing approaches still suffer from several limitations, including floating artifacts, unstable geometry reconstruction, noisy or over-smoothed textures, and inconsistent appearance across viewpoints. These issues mainly arise from the absence of explicit geometric and appearance constraints during optimization, resulting in reduced visual fidelity and multi-view consistency. To address these limitations, we propose two lightweight yet effective regularization modules for enhancing single-image 3DGS-based image-to-3D generation. We introduce an entropy-based alpha regularization that minimizes the binary entropy of rendered opacity maps, encouraging more confident occupancy predictions and suppressing ambiguous semi-transparent regions. This regularization effectively reduces floating artifacts and improves CLIP-Similarity by 1.81% compared with DreamGaussian. In addition, the proposed normal-guided texture smoothness loss enhances texture coherence, yielding an additional 0.41% improvement. By combining both regularization modules, our method achieves an overall performance gain of 2.26% over the baseline. These findings suggest that incorporating simple geometric and appearance priors can substantially enhance the robustness and fidelity of diffusion-guided 3D Gaussian Splatting for high-quality image-to-3D generation. Code is available at https://github.com/NamQuanProject/ENEA-GS