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.

该论文研究了修复 3D 物体的问题。作者提出了一个包含大约 5k 个物体的数据集 FixIt，并且设计了一个新框架 FixNet 来模拟人类的心理模拟过程，该框架包含感知模块、物理动态预测模块和功能预测模块，实验证明该框架的表现优于基准模型且具有良好的泛化性能。