Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
Abstract
Understanding the 3D world is a fundamental problem in computer vision. However,
learning a good representation of 3D objects is still an open problem due
to the high dimensionality of the data and many factors of variation involved. In
this work, we investigate the task of single-view 3D object reconstruction from a
learning agent’s perspective. We formulate the learning process as an interaction
between 3D and 2D representations and propose an encoder-decoder network with
a novel projection loss defined by the perspective transformation. More importantly,
the projection loss enables the unsupervised learning using 2D observation without
explicit 3D supervision. We demonstrate the ability of the model in generating 3D
volume from a single 2D image with three sets of experiments: (1) learning from
single-class objects; (2) learning from multi-class objects and (3) testing on novel
object classes. Results show superior performance and better generalization ability
for 3D object reconstruction when the projection loss is involved.