Online free-viewpoint video (FVV) reconstruction is challenged by slow per-frame optimization, inconsistent motion estimation, and unsustainable storage demands. To address these challenges, we propose the Reconfigurable Continuum Gaussian Stream, dubbed ReCon-GS, a novel storage-aware framework that enables high fidelity online dynamic scene reconstruction and real-time rendering. Specifically, we dynamically allocate multi-level Anchor Gaussians in a density-adaptive fashion to capture inter-frame geometric deformations, thereby decomposing scene motion into compact coarse-to-fine representations. Then, we design a dynamic hierarchy reconfiguration strategy that preserves localized motion expressiveness through on-demand anchor re-hierarchization, while ensuring temporal consistency through intra-hierarchical deformation inheritance that confines transformation priors to their respective hierarchy levels. Furthermore, we introduce a storage-aware optimization mechanism that flexibly adjusts the density of Anchor Gaussians at different hierarchy levels, enabling a controllable trade-off between reconstruction fidelity and memory usage. Extensive experiments on three widely used datasets demonstrate that, compared to state-of-the-art methods, ReCon-GS improves training efficiency by approximately 15% and achieves superior FVV synthesis quality with enhanced robustness and stability. Moreover, at equivalent rendering quality, ReCon-GS slashes memory requirements by over 50% compared to leading state-of-the-art methods.

翻译：在线自由视点视频（FVV）重建面临着逐帧优化缓慢、运动估计不一致以及存储需求不可持续等挑战。为应对这些挑战，我们提出了可重构连续体高斯流（Reconfigurable Continuum Gaussian Stream），简称ReCon-GS，这是一种新颖的存储感知框架，能够实现高保真度的在线动态场景重建与实时渲染。具体而言，我们以密度自适应方式动态分配多级锚点高斯函数，以捕捉帧间几何形变，从而将场景运动分解为紧凑的从粗到细表示。随后，我们设计了一种动态层级重构策略，通过按需的锚点重分层来保持局部运动表达能力，同时通过层级内形变继承将变换先验约束在各自层级内，从而确保时间一致性。此外，我们引入了一种存储感知优化机制，可灵活调整不同层级锚点高斯函数的密度，实现重建保真度与内存使用之间的可控权衡。在三个广泛使用的数据集上进行的大量实验表明，与最先进方法相比，ReCon-GS将训练效率提升了约15%，并以增强的鲁棒性和稳定性实现了更优的FVV合成质量。此外，在同等渲染质量下，ReCon-GS相较于领先的先进方法将内存需求降低了超过50%。