TransNet: Transparent Object Manipulation Through Category-Level Pose
  Estimation
Huijie Zhang Anthony Opipari Xiaotong Chen Jiyue Zhu Zeren Yu Odest Chadwicke Jenkins
Transparent objects present multiple distinct challenges to visual perception
systems. First, their lack of distinguishing visual features makes transparent
objects harder to detect and localize than opaque objects. Even humans find
certain transparent surfaces with little specular reflection or refraction,
like glass doors, difficult to perceive. A second challenge is that depth
sensors typically used for opaque object perception cannot obtain accurate
depth measurements on transparent surfaces due to their unique reflective
properties. Stemming from these challenges, we observe that transparent object
instances within the same category, such as cups, look more similar to each
other than to ordinary opaque objects of that same category. Given this
observation, the present paper explores the possibility of category-level
transparent object pose estimation rather than instance-level pose estimation.
We propose \textit{\textbf{TransNet}}, a two-stage pipeline that estimates
category-level transparent object pose using localized depth completion and
surface normal estimation. TransNet is evaluated in terms of pose estimation
accuracy on a large-scale transparent object dataset and compared to a
state-of-the-art category-level pose estimation approach. Results from this
comparison demonstrate that TransNet achieves improved pose estimation accuracy
on transparent objects. Moreover, we use TransNet to build an autonomous
transparent object manipulation system for robotic pick-and-place and pouring
tasks.