単一画像からの自己教師あり3D形状・視点推定とロボット把持への応用
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics
物体のシルエットのみを教師信号として、単一画像から3D形状と視点を自己教師あり学習で推定するCNNを提案し、実ロボットの把持性能向上に活用した。
著者: Oier Mees, Maxim Tatarchenko, Thomas Brox, Wolfram Burgard
分類: cs.RO, cs.CV, cs.LG, eess.IV
原文アブストラクト
We present a convolutional neural network for joint 3D shape prediction and viewpoint estimation from a single input image. During training, our network gets the learning signal from a silhouette of an object in the input image - a form of self-supervision. It does not require ground truth data for 3D shapes and the viewpoints. Because it relies on such a weak form of supervision, our approach can easily be applied to real-world data. We demonstrate that our method produces reasonable qualitative and quantitative results on natural images for both shape estimation and viewpoint prediction. Unlike previous approaches, our method does not require multiple views of the same object instance in the dataset, which significantly expands the applicability in practical robotics scenarios. We showcase it by using the hallucinated shapes to improve the performance on the task of grasping real-world objects both in simulation and with a PR2 robot.
