  Point cloud super-resolution is a fundamental problem for 3D reconstruction
and 3D data understanding. It takes a low-resolution (LR) point cloud as input
and generates a high-resolution (HR) point cloud with rich details. In this
paper, we present a data-driven method for point cloud super-resolution based
on graph networks and adversarial losses. The key idea of the proposed network
is to exploit the local similarity of point cloud and the analogy between LR
input and HR output. For the former, we design a deep network with graph
convolution. For the latter, we propose to add residual connections into graph
convolution and introduce a skip connection between input and output. The
proposed network is trained with a novel loss function, which combines Chamfer
Distance (CD) and graph adversarial loss. Such a loss function captures the
characteristics of HR point cloud automatically without manual design. We
conduct a series of experiments to evaluate our method and validate the
superiority over other methods. Results show that the proposed method achieves
the state-of-the-art performance and have a good generalization ability to
unseen data.
