Dynamic scene reconstruction via 3D Gaussian Splatting (3DGS) has emerged as a compelling approach for representing evolving environments, yet understanding trade-offs between methodologies remains crucial. This paper presents a comprehensive analysis of dynamic 3DGS methods, categorizing them into two paradigms: structure-guided methods employing auxiliary representations (deformation fields, canonical spaces, grids) to model temporal changes, and gaussian-centric methods encoding dynamics directly into primitives via continuous functions or 4D representations. We evaluate representative methods from both paradigms on the D-NeRF benchmark. Our findings reveal that structure-guided methods achieve superior reconstruction fidelity and compact model sizes, while gaussian-centric approaches demonstrate significantly higher rendering speeds enabling real-time performance, though with greater quality variability and potentially substantial storage overhead. This analysis highlights a fundamental trade-off between reconstruction quality/compactness versus rendering speed, providing insights to guide future research and application development in dynamic scene reconstruction.

翻译：通过三维高斯散射（3DGS）进行动态场景重建已成为表示演化环境的重要方法，但深入理解各方法论之间的权衡仍至关重要。本文全面分析了动态3DGS方法，将其归纳为两大范式：结构引导方法通过辅助表示（形变场、规范空间、网格）对时间变化进行建模，高斯中心方法则通过连续函数或四维表征直接将动态编码到基元中。我们基于D-NeRF基准对两种范式的代表性方法进行了评估。研究结果表明，结构引导方法在重建保真度和模型紧凑性方面表现更优，而高斯中心方法显著提升了渲染速度从而实现实时性能，尽管伴随较大的质量差异和潜在存储开销。该分析揭示了重建质量/紧凑性与渲染速度之间的根本性权衡，为动态场景重建领域的未来研究与应用开发提供了重要启示。