  Current methods for 3D object reconstruction from a set of planar
cross-sections still struggle to capture detailed topology or require a
considerable number of cross-sections. In this paper, we present, to the best
of our knowledge the first 3D shape reconstruction network to solve this task
which additionally uses orthographic projections of the shape. Our method is
based on applying a Reinforcement Learning algorithm to learn how to
effectively parse the shape using a trial-and-error scheme relying on scalar
rewards. This method cuts a part of a 3D shape in each step which is then
approximated as a polygon mesh. The agent aims to maximize the reward that
depends on the accuracy of surface reconstruction for the approximated parts.
We also consider pre-training of the network for faster learning using
demonstrations generated by a heuristic approach. Experiments show that our
training algorithm which benefits from both imitation learning and also self
exploration, learns efficient policies faster, which results the agent to
produce visually compelling results.
