We reconstruct a 3D model of the right ventricle from short- and long-axis image data and evaluate the benefits compared to quantification based on the 2D image stack. Deep learning is used to extract short-axis contours. An initial surface representation based on the contours is refined using long-axis images. Using a deformable model, the surface around the basal plane is adapted to image data. The resulting models capture the shape of the right ventricle better than segmentation from short-axis images alone and allow for a more precise volumetry.