  6D pose estimation of textureless shiny objects has become an essential
problem in many robotic applications. Many pose estimators require high-quality
depth data, often measured by structured light cameras. However, when objects
have shiny surfaces (e.g., metal parts), these cameras fail to sense complete
depths from a single viewpoint due to the specular reflection, resulting in a
significant drop in the final pose accuracy. To mitigate this issue, we present
a complete active vision framework for 6D object pose refinement and
next-best-view prediction. Specifically, we first develop an optimization-based
pose refinement module for the structured light camera. Our system then selects
the next best camera viewpoint to collect depth measurements by minimizing the
predicted uncertainty of the object pose. Compared to previous approaches, we
additionally predict measurement uncertainties of future viewpoints by online
rendering, which significantly improves the next-best-view prediction
performance. We test our approach on the challenging real-world ROBI dataset.
The results demonstrate that our pose refinement method outperforms the
traditional ICP-based approach when given the same input depth data, and our
next-best-view strategy can achieve high object pose accuracy with
significantly fewer viewpoints than the heuristic-based policies.
