Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for reconstructing three-dimensional meshes from two-dimensional images of objects with automatic coordinate system alignment. For example, the disclosed system can generate feature vectors for a plurality of images having different views of an object. The disclosed system can process the feature vectors to generate coordinate-aligned feature vectors aligned with a coordinate system associated with an image. The disclosed system can generate a combined feature vector from the feature vectors aligned to the coordinate system. Additionally, the disclosed system can then generate a three-dimensional mesh representing the object from the combined feature vector.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for reconstructing three-dimensional meshes from two-dimensional images of objects with automatic coordinate system alignment. For example, the disclosed system can generate feature vectors for a plurality of images having different views of an object. The disclosed system can process the feature vectors to generate coordinate-aligned feature vectors aligned with a coordinate system associated with an image. The disclosed system can generate a combined feature vector from the feature vectors aligned to the coordinate system. Additionally, the disclosed system can then generate a three-dimensional mesh representing the object from the combined feature vector.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for reconstructing three-dimensional object meshes from two-dimensional images of objects using multi-view cycle projection. For example, the disclosed system can determine a multi-view cycle projection loss across a plurality of images of an object via an estimated three-dimensional object mesh of the object. For example, the disclosed system uses a pixel mapping neural network to project a sampled pixel location across a plurality of images of an object and via a three-dimensional mesh representing the object. The disclosed system determines a multi-view cycle consistency loss based on a difference between the sampled pixel location and a cycle projection of the sampled pixel location and uses the loss to update the pixel mapping neural network, a latent vector representing the object, or a shape generation neural network that uses the latent vector to generate the object mesh.