  Transparent objects are ubiquitous in household settings and pose distinct
challenges for visual sensing and perception systems. The optical properties of
transparent objects leave conventional 3D sensors alone unreliable for object
depth and pose estimation. These challenges are highlighted by the shortage of
large-scale RGB-Depth datasets focusing on transparent objects in real-world
settings. In this work, we contribute a large-scale real-world RGB-Depth
transparent object dataset named ClearPose to serve as a benchmark dataset for
segmentation, scene-level depth completion and object-centric pose estimation
tasks. The ClearPose dataset contains over 350K labeled real-world RGB-Depth
frames and 5M instance annotations covering 63 household objects. The dataset
includes object categories commonly used in daily life under various lighting
and occluding conditions as well as challenging test scenarios such as cases of
occlusion by opaque or translucent objects, non-planar orientations, presence
of liquids, etc. We benchmark several state-of-the-art depth completion and
object pose estimation deep neural networks on ClearPose. The dataset and
benchmarking source code is available at https://github.com/opipari/ClearPose.
