This project addresses the task of category-level pose estimation for
articulated objects from a single depth image. We present a novel
category-level approach that correctly accommodates object instances previously
unseen during training. We introduce Articulation-aware Normalized Coordinate
Space Hierarchy (ANCSH) - a canonical representation for different articulated
objects in a given category. As the key to achieve intra-category
generalization, the representation constructs a canonical object space as well
as a set of canonical part spaces. The canonical object space normalizes the
object orientation,scales and articulations (e.g. joint parameters and states)
while each canonical part space further normalizes its part pose and scale. We
develop a deep network based on PointNet++ that predicts ANCSH from a single
depth point cloud, including part segmentation, normalized coordinates, and
joint parameters in the canonical object space. By leveraging the canonicalized
joints, we demonstrate: 1) improved performance in part pose and scale
estimations using the induced kinematic constraints from joints; 2) high
accuracy for joint parameter estimation in camera space.

本文介绍了一种使用深度图像实现类别级姿态估计的新方法，该方法基于 Articulation-aware Normalized Coordinate Space Hierarchy（ANCSH）构建了一种针对不同类别中的关节控制物体各个部分的规范表示，并提出了基于 PointNet ++ 的深度网络来预测 ANCSH。实验结果表明，该模型能够实现更好的物体姿态估计和关节参数估计。