A key technical challenge in performing 6D object pose estimation from RGB-D
image is to fully leverage the two complementary data sources. Prior works
either extract information from the RGB image and depth separately or use
costly post-processing steps, limiting their performances in highly cluttered
scenes and real-time applications. In this work, we present DenseFusion, a
generic framework for estimating 6D pose of a set of known objects from RGB-D
images. DenseFusion is a heterogeneous architecture that processes the two data
sources individually and uses a novel dense fusion network to extract
pixel-wise dense feature embedding, from which the pose is estimated.
Furthermore, we integrate an end-to-end iterative pose refinement procedure
that further improves the pose estimation while achieving near real-time
inference. Our experiments show that our method outperforms state-of-the-art
approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed
method to a real robot to grasp and manipulate objects based on the estimated
pose.

本文提出了 DenseFusion 框架，使用异构网络结构从 RGB-D 图像中估计已知物体的 6D 位姿，并通过端对端迭代位姿细化进一步提高了姿态估计的精度和实时性。实验结果表明，我们的方法在 YCB-Video 和 LineMOD 数据集上优于现有方法，并在实际机器人应用中实现了对物体的抓取和操作。