SilhoNet：単眼RGB画像からの6D物体姿勢推定手法
SilhoNet: An RGB Method for 6D Object Pose Estimation
単眼カメラ画像のみから物体の6自由度姿勢を推定するCNNベースの手法SilhoNetを提案し、シルエット表現と3D並進を予測して姿勢を回帰、YCB-Videoデータセットで既存手法を上回る性能を示した。
著者: Gideon Billings, Matthew Johnson-Roberson
分類: cs.CV, cs.RO
原文アブストラクト
Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.
