让3D场景模型实时适应变化，无需重训
GaussianUpdate: Continual 3D Gaussian Splatting Update for Changing Environments
- 用多阶段更新策略动态调整高斯辐射场
- 实现实时渲染与随时间变化的可视化
- 无需存储图像即可自适应更新，适合长期监控场景
近年来，基于神经网络的新视角合成技术快速发展，但如何使模型适应场景变化仍是未解难题。现有方法或需大量人工干预和重新训练，或无法有效捕捉随时间变化的细节。本文提出GaussianUpdate，将3D高斯表示与持续学习结合，利用当前数据高效更新高斯辐射场，同时保留历史场景信息。不同于以往方法，GaussianUpdate通过新颖的多阶段更新策略显式建模不同类型的场景变化。此外，我们引入可见性感知的持续学习机制与生成回放，实现无需存储图像的自感知更新。在基准数据集上的实验表明，该方法在保持优异实时渲染性能的同时，具备可视化不同时刻变化的能力。
原文摘要 · Abstract (English)
Novel view synthesis with neural models has advanced rapidly in recent years, yet adapting these models to scene changes remains an open problem. Existing methods are either labor-intensive, requiring extensive model retraining, or fail to capture detailed types of changes over time. In this paper, we present GaussianUpdate, a novel approach that combines 3D Gaussian representation with continual learning to address these challenges. Our method effectively updates the Gaussian radiance fields with current data while preserving information from past scenes. Unlike existing methods, GaussianUpdate explicitly models different types of changes through a novel multi-stage update strategy. Additionally, we introduce a visibility-aware continual learning approach with generative replay, enabling self-aware updating without the need to store images. The experiments on the benchmark dataset demonstrate our method achieves superior and real-time rendering with the capability of visualizing changes over different times
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