AtlasNet is a 3D deep learning paper implementing a papier-mâché approach to surface generation where a neural network learns to map 2D images or point clouds to 3D mesh geometry. The method uses multiple 2D parameterizable elements (like patches) that are deformed and assembled to form the final 3D surface. It is trained with losses such as Chamfer distance and Metro distance to optimize mesh quality.