Estimation of patient-specific hemodynamic features, and in particular fractional flow reserve (FFR) in coronary arteries is an essential step in providing personalized and accurate diagnosis of coronary artery disease (CAD). In recent years, in the domain of computed tomography angiography (CTA), a virtual FFR (vFFR) derived from coronary CTA using computational fluid dynamics (CFD), has been used as a compelling, non-invasive, in-silico replacement for invasive diagnostic techniques. Unfortunately, the time and computational demands of CFD are major obstacles to introducing vFFR from CT as a commonly used prophylactic tool. In this work, we propose a novel geometric-based artificial deep learning (DL) architecture, CenterlinePointNet++, which acts as a surrogate for CFD engines for the task of hemodynamic features estimation of the coronary arteries. Our architecture works directly on the vessel geometry represented as a surface point cloud and a centerline graph. As a result of that, it utilizes implicit geometry embedding without the need for hand-crafted features to estimate directly hemodynamic features. We evaluate our approach on the task of pressure drops and vFFR estimation for a synthetically generated dataset of coronary arteries and showcase significant improvement over commonly used geometry-based approaches.