  State-of-the-art learning-based monocular 3D reconstruction methods learn
priors over object categories on the training set, and as a result struggle to
achieve reasonable generalization to object categories unseen during training.
In this paper we study the inductive biases encoded in the model architecture
that impact the generalization of learning-based 3D reconstruction methods. We
find that 3 inductive biases impact performance: the spatial extent of the
encoder, the use of the underlying geometry of the scene to describe point
features, and the mechanism to aggregate information from multiple views.
Additionally, we propose mechanisms to enforce those inductive biases: a point
representation that is aware of camera position, and a variance cost to
aggregate information across views. Our model achieves state-of-the-art results
on the standard ShapeNet 3D reconstruction benchmark in various settings.
