  We present a method to learn single-view reconstruction of the 3D shape,
pose, and texture of objects from categorized natural images in a
self-supervised manner. Since this is a severely ill-posed problem, carefully
designing a training method and introducing constraints are essential. To avoid
the difficulty of training all elements at the same time, we propose training
category-specific base shapes with fixed pose distribution and simple textures
first, and subsequently training poses and textures using the obtained shapes.
Another difficulty is that shapes and backgrounds sometimes become excessively
complicated to mistakenly reconstruct textures on object surfaces. To suppress
it, we propose using strong regularization and constraints on object surfaces
and background images. With these two techniques, we demonstrate that we can
use natural image collections such as CIFAR-10 and PASCAL objects for training,
which indicates the possibility to realize 3D object reconstruction on diverse
object categories beyond synthetic datasets.
