キーポイントとパーツアフィニティフィールドを用いた6D物体姿勢推定
6D Object Pose Estimation using Keypoints and Part Affinity Fields
RGB画像から既知物体の6自由度姿勢を推定する2段階パイプラインを提案。OpenPose CNNでキーポイントとパーツアフィニティフィールドを予測し、PnP-RANSACで2D-3D対応から姿勢を計算する。
著者: Moritz Zappel, Simon Bultmann, Sven Behnke
分類: cs.CV, cs.RO
原文アブストラクト
The task of 6D object pose estimation from RGB images is an important requirement for autonomous service robots to be able to interact with the real world. In this work, we present a two-step pipeline for estimating the 6 DoF translation and orientation of known objects. Keypoints and Part Affinity Fields (PAFs) are predicted from the input image adopting the OpenPose CNN architecture from human pose estimation. Object poses are then calculated from 2D-3D correspondences between detected and model keypoints via the PnP-RANSAC algorithm. The proposed approach is evaluated on the YCB-Video dataset and achieves accuracy on par with recent methods from the literature. Using PAFs to assemble detected keypoints into object instances proves advantageous over only using heatmaps. Models trained to predict keypoints of a single object class perform significantly better than models trained for several classes.
