Pixel2Mesh is a deep learning method that generates 3D triangular mesh models from a single RGB image. The method uses a graph-based convolutional neural network (GCN) to represent 3D mesh geometry, progressively deforming an initial ellipsoid to match the input image. It employs a coarse-to-fine strategy and various mesh-related losses to ensure visually appealing and physically accurate results. The method outperforms existing approaches in terms of detail preservation and 3D shape estimation accuracy. Key contributions include an end-to-end neural network architecture, a projection layer that integrates perceptual image features into the GCN, and a coarse-to-fine approach for 3D geometry prediction. The method is evaluated on ShapeNet and real-world images, showing superior performance compared to state-of-the-art methods. It addresses challenges in representing mesh models in neural networks and effectively integrates perceptual features for accurate 3D reconstruction. The method also demonstrates the effectiveness of graph-based representations for 3D shape learning and reconstruction.Pixel2Mesh is a deep learning method that generates 3D triangular mesh models from a single RGB image. The method uses a graph-based convolutional neural network (GCN) to represent 3D mesh geometry, progressively deforming an initial ellipsoid to match the input image. It employs a coarse-to-fine strategy and various mesh-related losses to ensure visually appealing and physically accurate results. The method outperforms existing approaches in terms of detail preservation and 3D shape estimation accuracy. Key contributions include an end-to-end neural network architecture, a projection layer that integrates perceptual image features into the GCN, and a coarse-to-fine approach for 3D geometry prediction. The method is evaluated on ShapeNet and real-world images, showing superior performance compared to state-of-the-art methods. It addresses challenges in representing mesh models in neural networks and effectively integrates perceptual features for accurate 3D reconstruction. The method also demonstrates the effectiveness of graph-based representations for 3D shape learning and reconstruction.