REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination

Abstract

Object 6D pose estimation is a fundamental task in many applications. Conventional methods solve the task by detecting and matching the keypoints, then estimating the pose. Recent efforts bringing deep learning into the problem mainly overcome the vulnerability of conventional methods to environmental variation due to the hand-crafted feature design. However, these methods cannot achieve end-to-end learning and good interpretability at the same time. In this paper, we propose REDE, a novel end-to-end object pose estimator using RGB-D data, which utilizes network for keypoint regression, and a differentiable geometric pose estimator for pose error back-propagation. Besides, to achieve better robustness when outlier keypoint prediction occurs, we further propose a differentiable outliers elimination method that regresses the candidate result and the confidence simultaneously. Via confidence weighted aggregation of multiple candidates, we can reduce the effect from the outliers in the final estimation. Finally, following the conventional method, we apply a learnable refinement process to further improve the estimation. The experimental results on three benchmark datasets show that REDE slightly outperforms the state-of-the-art approaches and is more robust to object occlusion.

References in corpus (8)

• Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
• Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
• Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
• Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
• Going Further with Point Pair Features
• REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination
• 6DoF Object Pose Estimation via Differentiable Proxy Voting Loss
• P${}^2$GNet: Pose-Guided Point Cloud Generating Networks for 6-DoF Object Pose Estimation

Cited by in corpus (3)

• REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination
• A Right Invariant Extended Kalman Filter for Object based SLAM
• Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation