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纳入高斯飞溅方法。作为神经渲染的新范式，高斯飞溅已在众多应用中得到研究，主要工作集中于扩展其表示能力、优化其求解过程以及加速其运行效率。然而，一个正交且亟需探索却尚未充分研究的领域是数据不确定性。在标准四维高斯飞溅中，数据不确定性可表现为视角稀疏性、帧缺失、相机非同步等。目前，鲜有研究能在统一框架下整体纳入多种数据不确定性。为此，我们提出图形化X飞溅（GraphiXS）——一种考虑多种数据不确定性的新型概率框架，旨在将当前四维高斯飞溅范式从根本上扩展至概率化场景。GraphiXS具有通用性，可通过一系列基元（例如高斯分布、学生t分布）实例化。此外，GraphiXS可用于“升级”现有方法以适应数据不确定性。通过详尽的评估与对比，我们证明GraphiXS能系统建模数据中的各类不确定性，在数据时空缺失或受污染的多种场景下优于现有方法，因此是对当前四维高斯飞溅研究的重要泛化。