カテゴリレベル関節物体の姿勢推定
Category-Level Articulated Object Pose Estimation
単一深度画像から未学習の関節物体インスタンスの姿勢を推定するため、関節を考慮した正規化座標空間階層(ANCSH)を提案し、PointNet++で部位分割・正規化座標・関節パラメータを予測する手法。
著者: Xiaolong Li, He Wang, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
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.
