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.

翻译：本研究探索了在大规模高分辨率数据集上训练高参数化3D高斯溅射（3DGS）模型的可能性。我们设计了一种通用的3DGS模型并行训练方法——RetinaGS，该方法采用精确的渲染方程，可适用于任意场景及任意分布的高斯基元。该技术使我们能够探索3DGS在基元数量和训练分辨率方面的扩展特性（这些特性以往难以研究），并超越了现有最优的重建质量。我们观察到，采用本方法增加基元数量时，视觉质量呈现明显的正向提升趋势。我们首次实现了在完整MatrixCity数据集上训练包含超十亿基元的3DGS模型，并获得了令人满意的视觉质量。