  Our work learns a unified model for single-view 3D reconstruction of objects
from hundreds of semantic categories. As a scalable alternative to direct 3D
supervision, our work relies on segmented image collections for learning 3D of
generic categories. Unlike prior works that use similar supervision but learn
independent category-specific models from scratch, our approach of learning a
unified model simplifies the training process while also allowing the model to
benefit from the common structure across categories. Using image collections
from standard recognition datasets, we show that our approach allows learning
3D inference for over 150 object categories. We evaluate using two datasets and
qualitatively and quantitatively show that our unified reconstruction approach
improves over prior category-specific reconstruction baselines. Our final 3D
reconstruction model is also capable of zero-shot inference on images from
unseen object categories and we empirically show that increasing the number of
training categories improves the reconstruction quality.
