Gaussian Splatting has been considered as a novel way for view synthesis of dynamic scenes, which shows great potential in AIoT applications such as digital twins. However, recent dynamic Gaussian Splatting methods significantly degrade when only sparse input views are available, limiting their applicability in practice. The issue arises from the incoherent learning of 4D geometry as input views decrease. This paper presents GC-4DGS, a novel framework that infuses geometric consistency into 4D Gaussian Splatting (4DGS), offering real-time and high-quality dynamic scene rendering from sparse input views. While learning-based Multi-View Stereo (MVS) and monocular depth estimators (MDEs) provide geometry priors, directly integrating these with 4DGS yields suboptimal results due to the ill-posed nature of sparse-input 4D geometric optimization. To address these problems, we introduce a dynamic consistency checking strategy to reduce estimation uncertainties of MVS across spacetime. Furthermore, we propose a global-local depth regularization approach to distill spatiotemporal-consistent geometric information from monocular depths, thereby enhancing the coherent geometry and appearance learning within the 4D volume. Extensive experiments on the popular N3DV and Technicolor datasets validate the effectiveness of GC-4DGS in rendering quality without sacrificing efficiency. Notably, our method outperforms RF-DeRF, the latest dynamic radiance field tailored for sparse-input dynamic view synthesis, and the original 4DGS by 2.62dB and 1.58dB in PSNR, respectively, with seamless deployability on resource-constrained IoT edge devices.

翻译：高斯泼溅已被视为动态场景视图合成的一种新颖方法，在数字孪生等人工智能物联网应用中展现出巨大潜力。然而，当仅有稀疏输入视图可用时，现有的动态高斯泼溅方法性能显著下降，限制了其实际应用。该问题源于输入视图减少时4D几何学习的不一致性。本文提出GC-4DGS，一种将几何一致性融入4D高斯泼溅的新型框架，能够从稀疏输入视图实现实时高质量的动态场景渲染。尽管基于学习的多视图立体视觉和单目深度估计器可提供几何先验，但由于稀疏输入4D几何优化本身的不适定性，直接将其与4D高斯泼溅结合会导致次优结果。为解决这些问题，我们引入动态一致性校验策略以降低多视图立体视觉在时空维度上的估计不确定性。此外，我们提出全局-局部深度正则化方法，从单目深度中提取时空一致的几何信息，从而增强4D体积内几何与外观学习的连贯性。在主流N3DV和Technicolor数据集上的大量实验验证了GC-4DGS在保持效率的同时提升渲染质量的有效性。值得注意的是，本方法在PSNR指标上分别超越最新面向稀疏输入动态视图合成的动态辐射场方法RF-DeRF和原始4DGS达2.62dB和1.58dB，且能无缝部署于资源受限的物联网边缘设备。