  It is often desired to train 6D pose estimation systems on synthetic data
because manual annotation is expensive. However, due to the large domain gap
between the synthetic and real images, synthesizing color images is expensive.
In contrast, this domain gap is considerably smaller and easier to fill for
depth information. In this work, we present a system that regresses 6D object
pose from depth information represented by point clouds, and a lightweight data
synthesis pipeline that creates synthetic point cloud segments for training. We
use an augmented autoencoder (AAE) for learning a latent code that encodes 6D
object pose information for pose regression. The data synthesis pipeline only
requires texture-less 3D object models and desired viewpoints, and it is cheap
in terms of both time and hardware storage. Our data synthesis process is up to
three orders of magnitude faster than commonly applied approaches that render
RGB image data. We show the effectiveness of our system on the LineMOD, LineMOD
Occlusion, and YCB Video datasets. The implementation of our system is
available at: https://github.com/GeeeG/CloudAAE.
