  In the robotic industry, specular and textureless metallic components are
ubiquitous. The 6D pose estimation of such objects with only a monocular RGB
camera is difficult because of the absence of rich texture features.
Furthermore, the appearance of specularity heavily depends on the camera
viewpoint and environmental light conditions making traditional methods, like
template matching, fail. In the last 30 years, pose estimation of the specular
object has been a consistent challenge, and most related works require massive
knowledge modeling effort for light setups, environment, or the object surface.
On the other hand, recent works exhibit the feasibility of 6D pose estimation
on a monocular camera with convolutional neural networks(CNNs) however they
mostly use opaque objects for evaluation. This paper provides a data-driven
solution to estimate the 6D pose of specular objects for grasping them,
proposes a cost function for handling symmetry, and demonstrates experimental
results showing the system's feasibility.
