In this work, we explore the possibility of training high-parameter 3D
Gaussian splatting (3DGS) models on large-scale, high-resolution datasets. We
design a general model parallel training method for 3DGS, named RetinaGS, which
uses a proper rendering equation and can be applied to any scene and arbitrary
distribution of Gaussian primitives. It enables us to explore the scaling
behavior of 3DGS in terms of primitive numbers and training resolutions that
were difficult to explore before and surpass previous state-of-the-art
reconstruction quality. We observe a clear positive trend of increasing visual
quality when increasing primitive numbers with our method. We also demonstrate
the first attempt at training a 3DGS model with more than one billion
primitives on the full MatrixCity dataset that attains a promising visual
quality.

本研究探索了在大规模高分辨率数据集上训练高参数 3D 高斯喷溅（3DGS）模型的可能性，并设计了一种称为 RetinaGS 的通用模型并行训练方法，通过使用适当的渲染方程式可适用于任何场景和任意高斯图元分布，能够探索以前难于探索的 3DGS 的原始数量和训练分辨率的缩放行为以及超越以往最先进的重建质量，观察到使用我们的方法增加原始数量时视觉质量明显提高的积极趋势，同时我们还首次尝试在全矩阵城市数据集上训练了一个超过十亿个图元的 3DGS 模型，达到了有希望的视觉质量。