  6 DoF poses estimation problem aims to estimate the rotation and translation
parameters between two coordinates, such as object world coordinate and camera
world coordinate. Although some advances are made with the help of deep
learning, how to full use scene information is still a problem. Prior works
tackle the problem by pixel-wise feature fusion but need to randomly selecte
numerous points from images, which can not satisfy the demands of fast
inference simultaneously and accurate pose estimation. In this work, we present
a novel deep neural network based on dynamic keypoints selection designed for
6DoF pose estimation from a single RGBD image. Our network includes three
parts, instance semantic segmentation, edge points detection and 6DoF pose
estimation. Given an RGBD image, our network is trained to predict pixel
category and the translation to edge points and center points. Then, a
least-square fitting manner is applied to estimate the 6DoF pose parameters.
Specifically, we propose a dynamic keypoints selection algorithm to choose
keypoints from the foreground feature map. It allows us to leverage geometric
and appearance information. During 6DoF pose estimation, we utilize the
instance semantic segmentation result to filter out background points and only
use foreground points to finish edge points detection and 6DoF pose estimation.
Experiments on two commonly used 6DoF estimation benchmark datasets, YCB-Video
and LineMoD, demonstrate that our method outperforms the state-of-the-art
methods and achieves significant improvements over other same category methods
time efficiency.
