物理シミュレーションと機能予測で故障した3D物体を修理する
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
故障した3D物体を修理するため、知覚・物理ダイナミクス予測・機能予測を統合したFixNetを提案し、約5千体のデータセットFixItでベースラインを大きく上回る性能を示した。
著者: Yining Hong, Kaichun Mo, Li Yi, Leonidas J. Guibas, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan
分類: cs.CV, cs.AI, cs.GR, cs.LG, cs.RO
原文アブストラクト
This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D objects, we argue that functionality is reckoned with respect to the physical interactions between the object and the user. Given a malfunctional object, humans can perform mental simulations to reason about its functionality and figure out how to fix it. Inspired by this, we propose FixIt, a dataset that contains about 5k poorly-designed 3D physical objects paired with choices to fix them. To mimic humans' mental simulation process, we present FixNet, a novel framework that seamlessly incorporates perception and physical dynamics. Specifically, FixNet consists of a perception module to extract the structured representation from the 3D point cloud, a physical dynamics prediction module to simulate the results of interactions on 3D objects, and a functionality prediction module to evaluate the functionality and choose the correct fix. Experimental results show that our framework outperforms baseline models by a large margin, and can generalize well to objects with similar interaction types.
