  In this paper, we present a novel deep method to reconstruct a point cloud of
an object from a single still image. Prior arts in the field struggle to
reconstruct an accurate and scalable 3D model due to either the inefficient and
expensive 3D representations, the dependency between the output and number of
model parameters or the lack of a suitable computing operation. We propose to
overcome these by deforming a random point cloud to the object shape through
two steps: feature blending and deformation. In the first step, the global and
point-specific shape features extracted from a 2D object image are blended with
the encoded feature of a randomly generated point cloud, and then this mixture
is sent to the deformation step to produce the final representative point set
of the object. In the deformation process, we introduce a new layer termed as
GraphX that considers the inter-relationship between points like common graph
convolutions but operates on unordered sets. Moreover, with a simple trick, the
proposed model can generate an arbitrary-sized point cloud, which is the first
deep method to do so. Extensive experiments verify that we outperform existing
models and halve the state-of-the-art distance score in single image 3D
reconstruction.
