Unsupervised Learning of 3D Object Categories from Videos in the Wild

Understand

Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D.

• While several recent works have obtained analogous results using synthetic data or assuming the availability of 2D primitives such as keypoints, we are interested in working with challenging real data and with no manual annotations.
• We thus focus on learning a model from multiple views of a large collection of object instances.
• We contribute with a new large dataset of object centric videos suitable for training and benchmarking this class of models.