Multi-view mesh reconstruction remains a core challenge in computer graphics and vision, especially for recovering high-frequency geometry from sparse observations. Recent methods such as 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) rely on post-processing for mesh extraction, thereby limiting joint optimization of geometry and appearance. Implicit Moving Least Squares (IMLS) instead enables direct conversion of point clouds into signed distance and texture fields, supporting end-to-end reconstruction and rendering. However, existing IMLS formulations use exponential kernels that struggle with high-frequency detail. We introduce a compact polynomial kernel with local support and greater flexibility, allowing better control over frequency content and improved geometric fidelity. To further enhance fine details, we incorporate stochastic regularization with Laplacian filtering. Together, these improve the preservation of high-frequency structure while maintaining stable optimization. Experiments show state-of-the-art performance in both surface reconstruction and rendering, yielding more accurate geometry and sharper visuals from multi-view data.

翻译：多视角网格重建仍是计算机图形学与视觉领域的核心挑战，尤其体现在从稀疏观测数据中恢复高频几何结构方面。现有方法如3D高斯喷溅（3DGS）和神经辐射场（NeRF）依赖后处理进行网格提取，从而限制了几何与外观的联合优化。隐式移动最小二乘法（IMLS）可直接将点云转化为符号距离场与纹理场，支持端到端重建与渲染。然而，现有IMLS公式采用的指数核函数难以处理高频细节。我们提出一种具有局部支撑性与更高灵活性的紧凑多项式核函数，能够更好地控制频率内容并提升几何保真度。为进一步增强精细细节，我们引入结合拉普拉斯滤波的随机正则化技术。这些改进在保持优化稳定性的同时，显著提升了高频结构的保留能力。实验表明，该方法在表面重建与渲染方面均达到业界最优性能，从多视角数据中生成了更精确的几何结构与更清晰的视觉效果。