  Most existing methods for category-level pose estimation rely on object point
clouds. However, when considering transparent objects, depth cameras are
usually not able to capture meaningful data, resulting in point clouds with
severe artifacts. Without a high-quality point cloud, existing methods are not
applicable to challenging transparent objects. To tackle this problem, we
present StereoPose, a novel stereo image framework for category-level object
pose estimation, ideally suited for transparent objects. For a robust
estimation from pure stereo images, we develop a pipeline that decouples
category-level pose estimation into object size estimation, initial pose
estimation, and pose refinement. StereoPose then estimates object pose based on
representation in the normalized object coordinate space~(NOCS). To address the
issue of image content aliasing, we further define a back-view NOCS map for the
transparent object. The back-view NOCS aims to reduce the network learning
ambiguity caused by content aliasing, and leverage informative cues on the back
of the transparent object for more accurate pose estimation. To further improve
the performance of the stereo framework, StereoPose is equipped with a parallax
attention module for stereo feature fusion and an epipolar loss for improving
the stereo-view consistency of network predictions. Extensive experiments on
the public TOD dataset demonstrate the superiority of the proposed StereoPose
framework for category-level 6D transparent object pose estimation.
