ニューラル形状嵌合：敵対的形状事前分布を用いた自己教師あり物体組立
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors
未知カテゴリの2つの部品点群から、ぴったり嵌まる3D姿勢を予測する自己教師あり手法NSMを提案。形状再構成タスクと組み合わせ、20万組の切断データで学習する。
著者: Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg
分類: cs.CV, cs.GR, cs.LG, cs.RO
原文アブストラクト
Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit. By focusing on shape alignment rather than semantic cues, we can achieve across-category generalization. In this paper, we introduce a novel task, pairwise 3D geometric shape mating, and propose Neural Shape Mating (NSM) to tackle this problem. Given the point clouds of two object parts of an unknown category, NSM learns to reason about the fit of the two parts and predict a pair of 3D poses that tightly mate them together. We couple the training of NSM with an implicit shape reconstruction task to make NSM more robust to imperfect point cloud observations. To train NSM, we present a self-supervised data collection pipeline that generates pairwise shape mating data with ground truth by randomly cutting an object mesh into two parts, resulting in a dataset that consists of 200K shape mating pairs from numerous object meshes with diverse cut types. We train NSM on the collected dataset and compare it with several point cloud registration methods and one part assembly baseline. Extensive experimental results and ablation studies under various settings demonstrate the effectiveness of the proposed algorithm. Additional material is available at: https://neural-shape-mating.github.io/
