DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martin-Martin, Cewu Lu, Li Fei-Fei, Silvio Savarese; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3343-3352

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Wang_2019_CVPR,
author = {Wang, Chen and Xu, Danfei and Zhu, Yuke and Martin-Martin, Roberto and Lu, Cewu and Fei-Fei, Li and Savarese, Silvio},
title = {DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}