Head-Related Transfer Function (HRTF) measurement and extraction are important tasks for personalized-spatial audio. Many laboratories have their own apparatuses for data-collection but few studies have compared their results to a common subject or have modeled inter-dataset variances. We present a Bayesian fusion method based on Gaussian process (GP) modeling of joint spatial-frequency HRTFs over different spherical-measurement grids. Neumann KU-100 dummy HRTFs from 7 labs in the “Club Fritz” study are compared and fused to each other based on learning a set of transformations from the GP data-likelihood and covariance assumptions; parameter and hyperparameter training is automatic. Experimental results show that fused models for horizontal and median-plane HRTFs generalize the datasets better than pre-transformed ones.