動画から物体の物理特性を予測する手法
Predicting Physical Object Properties from Video
物理エンジンと補正推定器を組み合わせ、動画から物体の物理パラメータを推定する汎用的な手法を提案。
著者: Martin Link, Max Schwarz, Sven Behnke
分類: cs.CV, cs.RO
原文アブストラクト
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.
