In this paper, we propose a novel real-time 6D object pose estimation
framework, named G2L-Net. Our network operates on point clouds from RGB-D
detection in a divide-and-conquer fashion. Specifically, our network consists
of three steps. First, we extract the coarse object point cloud from the RGB-D
image by 2D detection. Second, we feed the coarse object point cloud to a
translation localization network to perform 3D segmentation and object
translation prediction. Third, via the predicted segmentation and translation,
we transfer the fine object point cloud into a local canonical coordinate, in
which we train a rotation localization network to estimate initial object
rotation. In the third step, we define point-wise embedding vector features to
capture viewpoint-aware information. To calculate more accurate rotation, we
adopt a rotation residual estimator to estimate the residual between initial
rotation and ground truth, which can boost initial pose estimation performance.
Our proposed G2L-Net is real-time despite the fact multiple steps are stacked
via the proposed coarse-to-fine framework. Extensive experiments on two
benchmark datasets show that G2L-Net achieves state-of-the-art performance in
terms of both accuracy and speed.

本文提出了一种新颖的 6D 物体姿态估计框架，称为 G2L-Net，它可以通过 RGB-D 检测处理点云数据来进行实时姿态估计，并在两个基准数据集上取得了最先进的性能。