Variational Bayes Gaussian Splatting
<|reference_start|>Variational Bayes Gaussian Splatting: 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.<|reference_end|>
@article{van de maele2024variational,
    title={Variational Bayes Gaussian Splatting},
    author={Toon Van de Maele, Ozan Catal, Alexander Tschantz, Christopher L.
  Buckley, Tim Verbelen},
    journal={arXiv preprint arXiv:2410.03592},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03592},
    primaryClass={cs.CV cs.AI}
}