Variational Bayes Gaussian Splatting

• URL: http://arxiv.org/abs/2410.03592v1
• Date: Fri, 4 Oct 2024 16:52:03 GMT
• Title: Variational Bayes Gaussian Splatting
• Authors: Toon Van de Maele, Ozan Catal, Alexander Tschantz, Christopher L. Buckley, Tim Verbelen,
• Abstract summary: 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians. We propose Variational Bayes Gaussian Splatting, a novel approach that frames training a Gaussian splat as variational inference over model parameters. 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.
• Score: 44.43761190929142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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