论文标题
通过视频预测物理对象属性
Predicting Physical Object Properties from Video
论文作者
论文摘要
我们提出了一种新颖的方法来估计视频中对象的物理特性。我们的方法由物理引擎和校正估计器组成。从最初观察到的状态开始，对象行为会在时间前进。基于模拟和观察到的行为，校正估计器然后确定每个对象的精制物理参数。该方法可以迭代以提高精度。我们的方法是通用的，因为它允许使用任意的（不一定是可区分的 - 物理引擎和校正器）。对于后者，我们评估了无梯度的高参数优化和深度卷积神经网络。我们在以bin情况为重点的几个模拟2D场景中，在几个模拟的2D方案中表现出了更快，更强大的收敛性。
We present a novel approach to estimating physical properties of objects from video. Our approach consists of a physics engine and a correction estimator. Starting from the initial observed state, object behavior is simulated forward in time. Based on the simulated and observed behavior, the correction estimator then determines refined physical parameters for each object. The method can be iterated for increased precision. Our approach is generic, as it allows for the use of an arbitrary - not necessarily differentiable - physics engine and correction estimator. For the latter, we evaluate both gradient-free hyperparameter optimization and a deep convolutional neural network. We demonstrate faster and more robust convergence of the learned method in several simulated 2D scenarios focusing on bin situations.
