Recent advancements in 3D Gaussian Splatting (3DGS), which lead to high-quality novel view synthesis and accelerated rendering, have remarkably improved the quality of radiance field reconstruction. However, the extraction of mesh from a massive number of minute 3D Gaussian points remains great challenge due to the large volume of Gaussians and difficulty of representation of sharp signals caused by their inherent low-pass characteristics. To address this issue, we propose DyGASR, which utilizes generalized exponential function instead of traditional 3D Gaussian to decrease the number of particles and dynamically optimize the representation of the captured signal. In addition, it is observed that reconstructing mesh with Generalized Exponential Splatting(GES) without modifications frequently leads to failures since the generalized exponential distribution centroids may not precisely align with the scene surface. To overcome this, we adopt Sugar's approach and introduce Generalized Surface Regularization (GSR), which reduces the smallest scaling vector of each point cloud to zero and ensures normal alignment perpendicular to the surface, facilitating subsequent Poisson surface mesh reconstruction. Additionally, we propose a dynamic resolution adjustment strategy that utilizes a cosine schedule to gradually increase image resolution from low to high during the training stage, thus avoiding constant full resolution, which significantly boosts the reconstruction speed. Our approach surpasses existing 3DGS-based mesh reconstruction methods, as evidenced by extensive evaluations on various scene datasets, demonstrating a 25\% increase in speed, and a 30\% reduction in memory usage.

翻译：近年来，三维高斯泼溅（3DGS）技术取得了显著进展，实现了高质量的新视角合成与加速渲染，极大地提升了辐射场重建的质量。然而，从海量微小的三维高斯点中提取网格仍然面临巨大挑战，这主要源于高斯点数量庞大以及其固有的低通特性导致难以准确表示尖锐信号。为解决这一问题，我们提出了DyGASR，该方法采用广义指数函数替代传统的三维高斯函数，以减少粒子数量并动态优化对捕获信号的表示。此外，我们观察到，未经修改的广义指数泼溅（GES）进行网格重建常导致失败，因为广义指数分布的中心点可能无法精确对齐场景表面。为克服此问题，我们借鉴Sugar的方法，引入了广义表面正则化（GSR），该技术将每个点云的最小缩放向量减小至零，并确保法线垂直于表面对齐，从而为后续的泊松表面网格重建提供便利。同时，我们提出了一种动态分辨率调整策略，该策略在训练阶段使用余弦调度将图像分辨率从低到高逐步提升，从而避免了始终使用全分辨率，显著提高了重建速度。我们在多个场景数据集上的广泛评估表明，我们的方法超越了现有的基于3DGS的网格重建方法，在速度上提升了25%，内存使用量减少了30%。