  We present a classification based approach for the next best view selection
and show how we can plausibly obtain a supervisory signal for this task. The
proposed approach is end-to-end trainable and aims to get the best possible 3D
reconstruction quality with a pair of passively acquired 2D views. The proposed
model consists of two stages: a classifier and a reconstructor network trained
jointly via the indirect 3D supervision from ground truth voxels. While
testing, the proposed method assumes no prior knowledge of the underlying 3D
shape for selecting the next best view. We demonstrate the proposed method's
effectiveness via detailed experiments on synthetic and real images and show
how it provides improved reconstruction quality than the existing state of the
art 3D reconstruction and the next best view prediction techniques.
