  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. Our project page is
available at https://aibluefisher.github.io/DoGaussian.
