Reconstructing 3D scenes with high fidelity and efficiency remains a central pursuit in computer vision and graphics. Recent advances in 3D Gaussian Splatting (3DGS) enable photorealistic rendering with Gaussian primitives, yet the modeling process remains governed predominantly by photometric supervision. This reliance often leads to irregular spatial distribution and indiscriminate primitive adjustments that largely ignore underlying geometric context. In this work, we rethink Gaussian modeling from a geometric standpoint and introduce Mini-Splatting2, an efficient scene modeling framework that couples structure-aware distribution and region-prioritized optimization, driving 3DGS into a geometry-regulated paradigm. The structure-aware distribution enforces spatial regularity through structured reorganization and representation sparsity, ensuring balanced structural coverage for compact organization. The region-prioritized optimization improves training discrimination through geometric saliency and computational selectivity, fostering appropriate structural emergence for fast convergence. These mechanisms alleviate the long-standing tension among representation compactness, convergence acceleration, and rendering fidelity. Extensive experiments demonstrate that Mini-Splatting2 achieves up to 4$\times$ fewer Gaussians and 3$\times$ faster optimization while maintaining state-of-the-art visual quality, paving the way towards structured and efficient 3D Gaussian modeling.

翻译：以高保真度和高效率重建三维场景始终是计算机视觉与图形学领域的核心目标。三维高斯泼溅（3DGS）的最新进展通过高斯基元实现了逼真的渲染，但其建模过程仍主要受光度监督主导。这种依赖性常导致不规则的空间分布和无差别的基元调整，很大程度上忽略了底层的几何上下文。本研究从几何角度重新思考高斯建模，提出了Mini-Splatting2——一个将结构感知分布与区域优先优化相结合的高效场景建模框架，从而将3DGS推向几何规整化的范式。结构感知分布通过结构化重组与表示稀疏性强制空间规整性，确保紧凑组织的平衡结构覆盖。区域优先优化则通过几何显著性与计算选择性提升训练判别力，促进快速收敛所需的适当结构涌现。这些机制缓解了表示紧凑性、收敛加速与渲染保真度之间长期存在的矛盾。大量实验表明，Mini-Splatting2在保持最先进视觉质量的同时，实现了高达4倍的高斯基元数量减少与3倍的优化加速，为结构化、高效的三维高斯建模开辟了新路径。