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Poster
Generative 3D Part Assembly via Dynamic Graph Learning
佳磊 黄 · Guanqi Zhan · Qingnan Fan · Kaichun Mo · Lin Shao · Baoquan Chen · Leonidas Guibas · Hao Dong

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1293
in Poster Session 4 »

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.

Author Information

佳磊 黄 (Peking University)
Guanqi Zhan (Peking University)
Qingnan Fan (Stanford University)
Kaichun Mo (Stanford University)

I am currently a fifth-year CS Ph.D. student advised by Prof. Leonidas J. Guibas. My research interests include 3D vision and graphics, deep learning, geometry processing, reinforcement learning and robotics.

Lin Shao (Stanford University)
Baoquan Chen (Shandong University)
Leonidas Guibas (stanford.edu)
Hao Dong (Peking University)

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