  Reconstructing 3D models from 2D images is one of the fundamental problems in
computer vision. In this work, we propose a deep learning technique for 3D
object reconstruction from a single image. Contrary to recent works that either
use 3D supervision or multi-view supervision, we use only single view images
with no pose information during training as well. This makes our approach more
practical requiring only an image collection of an object category and the
corresponding silhouettes. We learn both 3D point cloud reconstruction and pose
estimation networks in a self-supervised manner, making use of differentiable
point cloud renderer to train with 2D supervision. A key novelty of the
proposed technique is to impose 3D geometric reasoning into predicted 3D point
clouds by rotating them with randomly sampled poses and then enforcing cycle
consistency on both 3D reconstructions and poses. In addition, using
single-view supervision allows us to do test-time optimization on a given test
image. Experiments on the synthetic ShapeNet and real-world Pix3D datasets
demonstrate that our approach, despite using less supervision, can achieve
competitive performance compared to pose-supervised and multi-view supervised
approaches.
