In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
We present a novel method for single image depth estimation using surface normal constraints.
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds.
We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian.
Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications.