Smoothness Estimation

Smoothness estimation focuses on accurately determining the degree of smoothness in data, crucial for various applications requiring precise modeling of underlying functions or surfaces. Current research emphasizes developing robust algorithms, such as those based on Gaussian processes and orthogonal polynomial approximations, to estimate smoothness parameters locally or globally, adapting to varying levels of roughness within datasets. These advancements improve the accuracy and efficiency of tasks ranging from probability density estimation and Bayesian inference to ground segmentation in robotics and autonomous navigation. The resulting improvements in model fidelity have significant implications for diverse fields requiring accurate representation of complex, non-uniform data.