  Humans have a remarkable ability to predict the effect of physical
interactions on the dynamics of objects. Endowing machines with this ability
would allow important applications in areas like robotics and autonomous
vehicles. In this work, we focus on predicting the dynamics of 3D rigid
objects, in particular an object's final resting position and total rotation
when subjected to an impulsive force. Different from previous work, our
approach is capable of generalizing to unseen object shapes - an important
requirement for real-world applications. To achieve this, we represent object
shape as a 3D point cloud that is used as input to a neural network, making our
approach agnostic to appearance variation. The design of our network is
informed by an understanding of physical laws. We train our model with data
from a physics engine that simulates the dynamics of a large number of shapes.
Experiments show that we can accurately predict the resting position and total
rotation for unseen object geometries.

Computer Vision and Pattern Recognition;Machine Learning