  Recent methods for 6D pose estimation of objects assume either textured 3D
models or real images that cover the entire range of target poses. However, it
is difficult to obtain textured 3D models and annotate the poses of objects in
real scenarios. This paper proposes a method, Neural Object Learning (NOL),
that creates synthetic images of objects in arbitrary poses by combining only a
few observations from cluttered images. A novel refinement step is proposed to
align inaccurate poses of objects in source images, which results in better
quality images. Evaluations performed on two public datasets show that the
rendered images created by NOL lead to state-of-the-art performance in
comparison to methods that use 13 times the number of real images. Evaluations
on our new dataset show multiple objects can be trained and recognized
simultaneously using a sequence of a fixed scene.
