The project implements a method to reconstruct 3D objects from 2D images using unsupervised learning. It uses a neural renderer and PyTorch to train a model that learns probably symmetric 3D shape and appearance from unstructured image collections. The approach requires no 3D ground-truth, multiple views, or keypoint annotations, making it applicable to in-the-wild image datasets.