A Point Set Generation Network for 3D Object Reconstruction from a Single Image

Understand

Generation of 3D data by deep neural network has been attracting increasing attention in the research community.

• The majority of extant works resort to regular representations such as volumetric grids or collection of images; however, these representations obscure the natural invariance of 3D shapes under geometric transformations and also suffer from a number of other issues.
• In this paper we address the problem of 3D reconstruction from a single image, generating a straight-forward form of output -- point cloud coordinates.
• Along with this problem arises a unique and interesting issue, that the groundtruth shape for an input image may be ambiguous.