论文标题
通过学习的物理模拟和功能预测来固定故障对象
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
论文作者
论文摘要
本文研究了解决故障3D对象的问题。尽管以前的作品着重于构建被动感知模型以从静态3D对象中学习功能，但我们认为功能是相对于对象与用户之间的物理互动的。给定一个错误的对象，人类可以进行心理模拟以推理其功能，并弄清楚如何修复它。受此启发，我们提出了FIXIT，该数据集包含约5K设计不佳的3D物理对象，并配对选择修复它们。为了模仿人类的心理模拟过程，我们提出了Fixnet，这是一个新颖的框架，无缝地包含感知和身体动态。具体而言，FIXNET由一个感知模块组成，该模块是从3D点云中提取结构化表示形式，一个物理动力学预测模块，以模拟3D对象上的交互结果以及一个功能预测模块，以评估功能并选择正确的修复。实验结果表明，我们的框架的表现优于基线模型，并且可以很好地推广到具有相似相互作用类型的对象。
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.
