DyGASR: Dynamic Generalized Exponential Splatting with Surface Alignment for Accelerated 3D Mesh Reconstruction
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.
最近在3D Gaussian Splatting (3DGS) 方面的进展显著提升了新视角合成的质量和渲染速度，加速了辐射场重建。然而，从大量微小的3D高斯点中提取网格仍然是一大挑战，主要原因在于高斯点数量庞大且其固有的低通特性难以表现出锐利信号。为了解决这个问题，我们提出了 DyGASR 方法，该方法采用广义指数函数代替传统的3D高斯分布，从而减少粒子数量，并动态优化捕获信号的表示能力。 此外，我们观察到直接使用 广义指数分布点渲染（Generalized Exponential Splatting, GES） 进行网格重建通常会失败，这是因为广义指数分布的质心可能无法准确对齐场景表面。为了解决这一问题，我们借鉴了 Sugar 方法，引入了 广义表面正则化（Generalized Surface Regularization, GSR）。该方法将每个点云的最小缩放向量减少到零，并确保法线垂直于表面对齐，从而促进后续的 Poisson 表面网格重建。 此外，我们提出了一种动态分辨率调整策略，在训练过程中通过余弦调度从低分辨率逐步提高至高分辨率，避免始终使用全分辨率，从而显著加快重建速度。 通过在多个场景数据集上的广泛评估，我们的方法在网格重建的速度上比现有的基于3DGS的方法提升了 25%，内存使用减少了 30%，展现了显著的优势。