  Autonomous part assembly is a challenging yet crucial task in 3D computer
vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D
parts that can assemble a single shape, an intelligent agent needs to perceive
the 3D part geometry, reason to propose pose estimations for the input parts,
and finally call robotic planning and control routines for actuation. In this
paper, we focus on the pose estimation subproblem from the vision side
involving geometric and relational reasoning over the input part geometry.
Essentially, the task of generative 3D part assembly is to predict a 6-DoF part
pose, including a rigid rotation and translation, for each input part that
assembles a single 3D shape as the final output. To tackle this problem, we
propose an assembly-oriented dynamic graph learning framework that leverages an
iterative graph neural network as a backbone. It explicitly conducts sequential
part assembly refinements in a coarse-to-fine manner, exploits a pair of part
relation reasoning module and part aggregation module for dynamically adjusting
both part features and their relations in the part graph. We conduct extensive
experiments and quantitative comparisons to three strong baseline methods,
demonstrating the effectiveness of the proposed approach.
