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.
在这项工作中，我们探索了在大规模、高分辨率数据集上训练高参数三维高斯喷洒（3DGS）模型的可能性。我们设计了一种针对3DGS的通用模型并行训练方法，命名为RetinaGS，该方法使用了恰当的渲染方程，可应用于任何场景和任意分布的高斯基元。它使我们能够探索之前难以探索的3DGS在基元数量和训练分辨率方面的扩展行为，并超越了以往的最先进重建质量。我们观察到使用我们的方法增加基元数量时视觉质量明显提升的正向趋势。我们还展示了首次尝试在全MatrixCity数据集上训练拥有超过十亿基元的3DGS模型，并取得了有希望的视觉质量。