  We propose a single-shot method for simultaneous 3D object segmentation and
6-DOF pose estimation in pure 3D point clouds scenes based on a consensus that
\emph{one point only belongs to one object}, i.e., each point has the potential
power to predict the 6-DOF pose of its corresponding object. Unlike the
recently proposed methods of the similar task, which rely on 2D detectors to
predict the projection of 3D corners of the 3D bounding boxes and the 6-DOF
pose must be estimated by a PnP like spatial transformation method, ours is
concise enough not to require additional spatial transformation between
different dimensions. Due to the lack of training data for many objects, the
recently proposed 2D detection methods try to generate training data by using
rendering engine and achieve good results. However, rendering in 3D space along
with 6-DOF is relatively difficult. Therefore, we propose an augmented reality
technology to generate the training data in semi-virtual reality 3D space. The
key component of our method is a multi-task CNN architecture that can
simultaneously predicts the 3D object segmentation and 6-DOF pose estimation in
pure 3D point clouds.
  For experimental evaluation, we generate expanded training data for two
state-of-the-arts 3D object datasets \cite{PLCHF}\cite{TLINEMOD} by using
Augmented Reality technology (AR). We evaluate our proposed method on the two
datasets. The results show that our method can be well generalized into
multiple scenarios and provide performance comparable to or better than the
state-of-the-arts.
