  This paper addresses 3D shape recognition. Recent work typically represents a
3D shape as a set of binary variables corresponding to 3D voxels of a uniform
3D grid centered on the shape, and resorts to deep convolutional neural
networks(CNNs) for modeling these binary variables. Robust learning of such
CNNs is currently limited by the small datasets of 3D shapes available, an
order of magnitude smaller than other common datasets in computer vision.
Related work typically deals with the small training datasets using a number of
ad hoc, hand-tuning strategies. To address this issue, we formulate CNN
learning as a beam search aimed at identifying an optimal CNN architecture,
namely, the number of layers, nodes, and their connectivity in the network, as
well as estimating parameters of such an optimal CNN. Each state of the beam
search corresponds to a candidate CNN. Two types of actions are defined to add
new convolutional filters or new convolutional layers to a parent CNN, and thus
transition to children states. The utility function of each action is
efficiently computed by transferring parameter values of the parent CNN to its
children, thereby enabling an efficient beam search. Our experimental
evaluation on the 3D ModelNet dataset demonstrates that our model pursuit using
the beam search yields a CNN with superior performance on 3D shape
classification than the state of the art.
