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，用于在三维高斯喷溅（Gaussian Splatting）中系统地融合数据不确定性。作为神经渲染的新范式，高斯喷溅已在诸多应用中得到研究，主要致力于扩展其表征能力、优化其训练过程以及加速其运行速度。然而，一个正交且亟需探索但尚未充分研究的领域是数据不确定性。在标准4D高斯喷溅中，数据不确定性可表现为视角稀疏性、缺失帧、相机不同步等。迄今为止，鲜有研究能在统一框架下全面整合多种类型的数据不确定性。为此，我们提出图形X喷溅（GraphiXS），一种新的概率框架，该框架考虑了多种数据不确定性，旨在将当前4D高斯喷溅范式从根本上扩展至概率设定中。GraphiXS具有通用性，可实例化多种基元（例如高斯分布、学生t分布）。此外，GraphiXS可用于“升级”现有方法以处理数据不确定性。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种不确定性，在众多时空数据缺失或污染的场景中优于现有方法，因此是对当前4D高斯喷溅研究的重要泛化。