Spatiotemporal Sampling

Spatiotemporal sampling focuses on efficiently collecting data across both space and time, optimizing data acquisition for various applications. Current research emphasizes adaptive sampling techniques, leveraging machine learning models like neural radiance fields (NeRFs) and temporal graph neural networks (TGNNs), to intelligently select data points based on factors like motion, noise levels, and inherent data structure. These advancements improve the accuracy and efficiency of data processing in diverse fields, ranging from 3D scene reconstruction and dynamic graph analysis to environmental monitoring and action recognition in resource-constrained settings. The ultimate goal is to achieve high-fidelity representations from limited data, reducing computational costs and improving the quality of derived insights.