We propose a new framework to systematically incorporate data uncertainty in Gaussian Splatting. Being the new paradigm of neural rendering, Gaussian Splatting has been investigated in many applications, with the main effort in extending its representation, improving its optimization process, and accelerating its speed. However, one orthogonal, much needed, but under-explored area is data uncertainty. In standard 4D Gaussian Splatting, data uncertainty can manifest as view sparsity, missing frames, camera asynchronization, etc. So far, there has been little research to holistically incorporating various types of data uncertainty under a single framework. To this end, we propose Graphical X Splatting, or GraphiXS, a new probabilistic framework that considers multiple types of data uncertainty, aiming for a fundamental augmentation of the current 4D Gaussian Splatting paradigm into a probabilistic setting. GraphiXS is general and can be instantiated with a range of primitives, e.g. Gaussians, Student's-t. Furthermore, GraphiXS can be used to `upgrade' existing methods to accommodate data uncertainty. Through exhaustive evaluation and comparison, we demonstrate that GraphiXS can systematically model various uncertainties in data, outperform existing methods in many settings where data are missing or polluted in space and time, and therefore is a major generalization of the current 4D Gaussian Splatting research.

翻译：我们提出了一种新框架，用于系统性地将数据不确定性融入高斯溅射。作为神经渲染的新范式，高斯溅射已在众多应用中得到研究，主要工作集中于扩展其表示、改进其优化过程以及提升其速度。然而，一个正交、亟需但尚未充分探索的领域是数据不确定性。在标准的4D高斯溅射中，数据不确定性可表现为视角稀疏性、帧缺失、相机异步等问题。迄今为止，鲜有研究能够在一个统一框架下整体性地处理多种类型的数据不确定性。为此，我们提出了图形化X溅射（GraphiXS），这是一种新的概率框架，旨在将当前4D高斯溅射范式从根本上扩展至概率设置，同时考虑多种类型的数据不确定性。GraphiXS具有通用性，可通过一系列基元（如高斯分布、Student's-t分布）进行实例化。此外，GraphiXS可用于“升级”现有方法以适配数据不确定性。通过详尽的评估与比较，我们证明GraphiXS能够系统性地建模数据中的各类不确定性，在时空数据缺失或污染的多种场景中优于现有方法，因此是对当前4D高斯溅射研究的重要推广。