Recently, 3D Gaussian Splatting has emerged as a prominent research direction owing to its ultrarapid training speed and high-fidelity rendering capabilities. However, the unstructured and irregular nature of Gaussian point clouds poses challenges to reconstruction accuracy. This limitation frequently causes high-frequency detail loss in complex surface microstructures when relying solely on routine strategies. To address this limitation, we propose GSM-GS: a synergistic optimization framework integrating single-view adaptive sub-region weighting constraints and multi-view spatial structure refinement. For single-view optimization, we leverage image gradient features to partition scenes into texture-rich and texture-less sub-regions. The reconstruction quality is enhanced through adaptive filtering mechanisms guided by depth discrepancy features. This preserves high-weight regions while implementing a dual-branch constraint strategy tailored to regional texture variations, thereby improving geometric detail characterization. For multi-view optimization, we introduce a geometry-guided cross-view point cloud association method combined with a dynamic weight sampling strategy. This constructs 3D structural normal constraints across adjacent point cloud frames, effectively reinforcing multi-view consistency and reconstruction fidelity. Extensive experiments on public datasets demonstrate that our method achieves both competitive rendering quality and geometric reconstruction. See our interactive project page

翻译：近年来，3D高斯溅射因其极快的训练速度与高保真渲染能力，已成为一个重要的研究方向。然而，高斯点云的非结构化与不规则特性对重建精度提出了挑战。这一局限在仅依赖常规策略时，常导致复杂表面微结构中的高频细节丢失。为应对此局限，我们提出了GSM-GS：一种融合单视图自适应子区域加权约束与多视图空间结构优化的协同优化框架。在单视图优化中，我们利用图像梯度特征将场景划分为纹理丰富与纹理稀疏的子区域。通过由深度差异特征引导的自适应滤波机制，重建质量得以提升。该机制在保留高权重区域的同时，实施了一种针对区域纹理变化定制的双分支约束策略，从而改善了几何细节的表征。在多视图优化中，我们引入了一种几何引导的跨视点点云关联方法，并结合动态权重采样策略。该方法在相邻点云帧之间构建了三维结构法向约束，有效增强了多视图一致性与重建保真度。在公开数据集上进行的大量实验表明，我们的方法在渲染质量与几何重建方面均取得了具有竞争力的结果。请参见我们的交互式项目页面。