  We study the problem of recovering an underlying 3D shape from a set of
images. Existing learning based approaches usually resort to recurrent neural
nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to
fuse multiple deep features encoded from input images. However, GRU based
approaches are unable to consistently estimate 3D shapes given different
permutations of the same set of input images as the recurrent unit is
permutation variant. It is also unlikely to refine the 3D shape given more
images due to the long-term memory loss of GRU. Commonly used pooling
approaches are limited to capturing partial information, e.g., max/mean values,
ignoring other valuable features. In this paper, we present a new feed-forward
neural module, named AttSets, together with a dedicated training algorithm,
named FASet, to attentively aggregate an arbitrarily sized deep feature set for
multi-view 3D reconstruction. The AttSets module is permutation invariant,
computationally efficient and flexible to implement, while the FASet algorithm
enables the AttSets based network to be remarkably robust and generalize to an
arbitrary number of input images. We thoroughly evaluate FASet and the
properties of AttSets on multiple large public datasets. Extensive experiments
show that AttSets together with FASet algorithm significantly outperforms
existing aggregation approaches.
