DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

Code

• github.com/cxt98/Densefusion-transparency
  pytorch★ 0
• github.com/shayeree96/Adversial-Attacks-on-densely-fused-point-clouds-for-6D-Pose-Estimation
  pytorch★ 0
• github.com/Yotonctu/densefusion_torch1.0
  pytorch★ 0
• github.com/j96w/DenseFusion
  pytorch★ 0
• github.com/Theopetitjean/DenseFusion_R_Invariant
  pytorch★ 0
• github.com/RiplleYang/DenseFusion
  pytorch★ 0
• github.com/caoquan95/6D-pose-project
  pytorch★ 0
• github.com/hz-ants/DenseFusion
  pytorch★ 0

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.

Tasks

Benchmark Results

Reproductions