Accurate reconstruction of the right heart geometry and motion from time-resolved medical images is crucial for diagnostic enhancement and computational analysis of cardiac blood dynamics. Commonly used segmentation and/or reconstruction techniques, exclusively relying on short-axis cine-MRI, lack precision in critical regions of the right heart, such as the ventricular base and the outflow tract, due to its unique morphology and motion. Furthermore, the reconstruction procedure is time-consuming and necessitates significant manual intervention for generating computational domains. This study introduces an end-to-end hybrid reconstruction method specifically designed for computational simulations. Integrating information from various cine-MRI series (short/long-axis and 2/3/4 chambers views) with minimal user contribution, our method leverages registration- and morphing-based algorithms to accurately reconstruct crucial cardiac features and complete cardiac motion. The reconstructed data enable the creation of patient-specific computational fluid dynamics models, facilitating the analysis of the hemodynamics in healthy and clinically relevant scenarios. We assessed the accuracy of our reconstruction method against ground truth and a standard method. We also evaluated volumetric clinical parameters and compared them with the literature values. The method's adaptability was investigated by reducing the number of cine-MRI views, highlighting its robustness with varying imaging data. Numerical findings supported the reliability of the approach for simulating hemodynamics. Combining registration- and morphing-based algorithms, our method offers accurate reconstructions of the right heart chambers' morphology and motion. These reconstructions can serve as valuable tools as domain and boundary conditions for computational fluid dynamics simulations, ensuring seamless and effective analysis.