DoGaussian: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus
The recent advances in 3D Gaussian Splatting (3DGS) show promising results on the novel view synthesis (NVS) task. With its superior rendering performance and high-fidelity rendering quality, 3DGS is excelling at its previous NeRF counterparts. The most recent 3DGS method focuses either on improving the instability of rendering efficiency or reducing the model size. On the other hand, the training efficiency of 3DGS on large-scale scenes has not gained much attention. In this work, we propose DoGaussian, a method that trains 3DGS distributedly. Our method first decomposes a scene into K blocks and then introduces the Alternating Direction Method of Multipliers (ADMM) into the training procedure of 3DGS. During training, our DoGaussian maintains one global 3DGS model on the master node and K local 3DGS models on the slave nodes. The K local 3DGS models are dropped after training and we only query the global 3DGS model during inference. The training time is reduced by scene decomposition, and the training convergence and stability are guaranteed through the consensus on the shared 3D Gaussians. Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes while concurrently achieving state-of-the-art rendering quality.
最近在3D高斯投影（3DGS）方面的进展在新视角合成（NVS）任务上显示出有希望的结果。凭借其卓越的渲染性能和高保真渲染质量，3DGS在其先前的NeRF对应物中表现出色。最新的3DGS方法要么专注于改善渲染效率的不稳定性，要么致力于减小模型大小。另一方面，3DGS在大规模场景上的训练效率尚未受到太多关注。在这项工作中，我们提出了DoGaussian，一种分布式训练3DGS的方法。我们的方法首先将场景分解为K个块，然后在3DGS的训练过程中引入交替方向乘子法（ADMM）。在训练期间，我们的DoGaussian在主节点上维护一个全局3DGS模型，在从节点上维护K个本地3DGS模型。训练后会丢弃K个本地3DGS模型，我们只在推理期间查询全球3DGS模型。通过场景分解减少了训练时间，并通过对共享的3D高斯的一致性保证了训练的收敛和稳定性。我们的方法在对大规模场景进行评估时，将3DGS的训练速度提高了6倍以上，同时还实现了最先进的渲染质量。