Object-level segmentation in dynamic 4D Gaussian scenes remains challenging due to complex motion, occlusions, and ambiguous boundaries. In this paper, we present an efficient learning-free 4D Gaussian segmentation framework that lifts video segmentation masks to 4D spaces, whose core is a two-stage iterative boundary refinement, TIBR4D. The first stage is an Iterative Gaussian Instance Tracing (IGIT) at the temporal segment level. It progressively refines Gaussian-to-instance probabilities through iterative tracing, and extracts corresponding Gaussian point clouds that better handle occlusions and preserve completeness of object structures compared to existing one-shot threshold-based methods. The second stage is a frame-wise Gaussian Rendering Range Control (RCC) via suppressing highly uncertain Gaussians near object boundaries while retaining their core contributions for more accurate boundaries. Furthermore, a temporal segmentation merging strategy is proposed for IGIT to balance identity consistency and dynamic awareness. Longer segments enforce stronger multi-frame constraints for stable identities, while shorter segments allow identity changes to be captured promptly. Experiments on HyperNeRF and Neu3D demonstrate that our method produces accurate object Gaussian point clouds with clearer boundaries and higher efficiency compared to SOTA methods.

翻译：在动态4D高斯场景中进行物体级分割，由于复杂的运动、遮挡以及模糊的边界，仍然是一项具有挑战性的任务。本文提出了一种高效的无学习4D高斯分割框架，该框架将视频分割掩码提升至4D空间，其核心是一个两阶段的迭代边界优化方法，即TIBR4D。第一阶段是在时间片段级别进行的迭代高斯实例追踪。它通过迭代追踪逐步优化高斯点到实例的概率，并提取对应的高斯点云。与现有基于单次阈值的方法相比，该方法能更好地处理遮挡并保持物体结构的完整性。第二阶段是通过帧级别的高斯渲染范围控制，在抑制物体边界附近高度不确定的高斯点的同时，保留其对核心区域的贡献，从而获得更精确的边界。此外，本文为IGIT提出了一种时序分割合并策略，以平衡身份一致性与动态感知能力。较长的片段施加更强的多帧约束以维持稳定的身份，而较短的片段则允许及时捕捉身份变化。在HyperNeRF和Neu3D数据集上的实验表明，与最先进的方法相比，我们的方法能够生成边界更清晰、效率更高的精确物体高斯点云。