Recently, 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians. The predominant optimization method for these models relies on backpropagating gradients through a differentiable rendering pipeline, which struggles with catastrophic forgetting when dealing with continuous streams of data. To address this limitation, we propose Variational Bayes Gaussian Splatting (VBGS), a novel approach that frames training a Gaussian splat as variational inference over model parameters. By leveraging the conjugacy properties of multivariate Gaussians, we derive a closed-form variational update rule, allowing efficient updates from partial, sequential observations without the need for replay buffers. Our experiments show that VBGS not only matches state-of-the-art performance on static datasets, but also enables continual learning from sequentially streamed 2D and 3D data, drastically improving performance in this setting.
最近，3D高斯散射作为一种使用高斯混合物来建模3D场景的有前途方法得到了广泛关注。这些模型的主要优化方法依赖于通过可微渲染管道进行梯度反向传播，但在处理连续数据流时容易出现灾难性遗忘问题。为了解决这一局限性，我们提出了变分贝叶斯高斯散射（Variational Bayes Gaussian Splatting，VBGS），这是一种将高斯散射训练框架化为模型参数上的变分推断的新方法。通过利用多元高斯的共轭性，我们推导出封闭形式的变分更新规则，从而能够在没有重放缓冲区的情况下，高效地从部分、连续的观测中进行更新。我们的实验表明，VBGS不仅在静态数据集上达到了最先进的性能，还能够从连续流动的2D和3D数据中进行持续学习，在这一场景下显著提升了性能。