Monocular 3D shape estimation is crucial for autonomous driving, enabling accurate vehicle pose estimation from single images. This paper presents a deep learning-based approach utilizing a Variational Autoencoder (VAE) and Graph Convolutional Networks (GCNs) to estimate dense 3D meshes of vehicles. Unlike conventional keypoint-based methods, the proposed approach reconstructs complete car shapes, ensuring robust pose estimation even under occlusions and varying lighting conditions. A pipeline is introduced where a Vision Transformer extracts image features, followed by a Shape Head predicting a latent vector, which the VAE decoder converts into a full 3D mesh. The ApolloCar3D dataset is used for training and evaluation, demonstrating that the mesh-based method achieves improved accuracy of keypoint detection, while maintaining high accuracy of pose estimation. Results highlight the effectiveness of dense mesh prediction that can serve for enhancing vehicle detection, tracking, and collision avoidance in autonomous driving systems.