SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
Authors: name: Marko Mihajlovic, name: Sergey Prokudin, name: Siyu Tang, name: Robert Maier, name: Federica Bogo, name: Tony Tung, name: Edmond Boyer

Digitizing 3D static scenes and 4D dynamic events from multi-view images has
long been a challenge in computer vision and graphics. Recently, 3D Gaussian
Splatting (3DGS) has emerged as a practical and scalable reconstruction method,
gaining popularity due to its impressive reconstruction quality, real-time
rendering capabilities, 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 severe in capturing dynamic scenes,
where deploying an extensive camera array can be prohibitively costly. In this
work, we identify the lack of spatial autocorrelation of splat features 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 effectively handles static and dynamic cases, as demonstrated by
extensive testing across different setups and scene complexities.