Digitizing static scenes and dynamic events from multi-view images has long been a challenge in the fields of computer vision and graphics. Recently, 3D Gaussian Splatting has emerged as a practical and scalable method for reconstruction, gaining popularity due to its impressive quality of reconstruction, real-time rendering speeds, and compatibility with widely used visualization tools. However, the method requires a substantial number of input views to achieve high-quality scene reconstruction, introducing a significant practical bottleneck. This challenge is especially pronounced in capturing dynamic scenes, where deploying an extensive camera array can be prohibitively costly. In this work, we identify the lack of spatial autocorrelation as one of the factors contributing to the suboptimal performance of the 3DGS technique in sparse reconstruction settings. To address the issue, we propose an optimization strategy that effectively regularizes splat features by modeling them as the outputs of a corresponding implicit neural field. This results in a consistent enhancement of reconstruction quality across various scenarios. Our approach adeptly manages both static and dynamic cases, as demonstrated by extensive testing across different setups and scene complexities.