  Given a single scene image, this paper proposes a method of Category-level 6D
Object Pose and Size Estimation (COPSE) from the point cloud of the target
object, without external real pose-annotated training data. Specifically,
beyond the visual cues in RGB images, we rely on the shape information
predominately from the depth (D) channel. The key idea is to explore the shape
alignment of each instance against its corresponding category-level template
shape, and the symmetric correspondence of each object category for estimating
a coarse 3D object shape. Our framework deforms the point cloud of the
category-level template shape to align the observed instance point cloud for
implicitly representing its 3D rotation. Then we model the symmetric
correspondence by predicting symmetric point cloud from the partially observed
point cloud. The concatenation of the observed point cloud and symmetric one
reconstructs a coarse object shape, thus facilitating object center (3D
translation) and 3D size estimation. Extensive experiments on the
category-level NOCS benchmark demonstrate that our lightweight model still
competes with state-of-the-art approaches that require labeled real-world
images. We also deploy our approach to a physical Baxter robot to perform
grasping tasks on unseen but category-known instances, and the results further
validate the efficacy of our proposed model. Code and pre-trained models are
available on the project webpage.
