DyGASR：面向加速三维网格重建的带表面对齐的动态广义指数抛雪球方法
DyGASR: Dynamic Generalized Exponential Splatting with Surface Alignment for Accelerated 3D Mesh Reconstruction
AI总结：
DyGASR 用广义指数函数替代 3D 高斯以减少粒子数，引入广义表面正则化使点云对齐表面，并通过余弦动态分辨率策略加速训练，在网格重建中实现速度提升 25%、内存减少 30%。
摘要：
三维高斯抛雪球（3DGS）的最新进展带来了高质量的新视角合成和加速渲染，显著提升了辐射场重建的质量。然而，由于高斯点数量庞大，且其固有的低通特性导致难以表示尖锐信号，从海量微小三维高斯点中提取网格仍然是一个巨大挑战。为了解决这一问题，我们提出了 DyGASR，该方法利用广义指数函数替代传统的三维高斯，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，在不做修改的情况下使用广义指数抛雪球（GES）重建网格经常导致失败，因为广义指数分布的质心可能无法精确对齐场景表面。为克服这一问题，我们采用 Sugar 的方法并引入广义表面正则化（GSR），将每个点云的最小缩放向量缩减为零，并确保法线垂直于表面，从而有利于后续的泊松表面网格重建。另外，我们提出了一种动态分辨率调整策略，该策略利用余弦调度在训练阶段逐步将图像分辨率从低到高增加，从而避免恒定使用全分辨率，显著提升了重建速度。我们在多种场景数据集上进行了广泛评估，结果表明我们的方法超越了现有基于 3DGS 的网格重建方法，速度提升了 25%，内存使用减少了 30%。
英文摘要：
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.
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