OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval

Abstract

Articulated objects are pervasive in daily life. However, due to the intrinsic high-DoF structure, the joint states of the articulated objects are hard to be estimated. To model articulated objects, two kinds of shape deformations namely the geometric and the pose deformation should be considered. In this work, we present a novel categoryspecific parametric representation called Object Model with Articulated Deformations (OMAD) to explicitly model the articulated objects. In OMAD, a category is associated with a linear shape function with shared shape basis and a non-linear joint function. Both functions can be learned from a large-scale object model dataset and fixed as categoryspecific priors. Then we propose an OMADNet to predict the shape parameters and the joint states from an object's single observation. With the full representation of the object shape and joint states, we can address several tasks including category-level object pose estimation and the articulated object retrieval. To evaluate these tasks, we create a synthetic dataset based on PartNet-Mobility. Extensive experiments show that our simple OMADNet can serve as a strong baseline for both tasks. Our code and dataset are available in https://sites.google.com/view/omad-bmvc/.

Cdhn: Cross-Domain Hallucination Network for 3d Keypoints Estimation

“…point-level clustering, object's skeleton, consistency between object's symmetry, part contrasting, etc. [15,33,24,9,37].…”