W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose Regression

Abstract

Solving 6D pose estimation is non-trivial to cope with intrinsic appearance and shape variation and severe inter-object occlusion, and is made more challenging in light of extrinsic large illumination changes and low quality of the acquired data under an uncontrolled environment. This paper introduces a novel pose estimation algorithm W-PoseNet, which densely regresses from input data to 6D pose and also 3D coordinates in model space. In other words, local features learned for pose regression in our deep network are regularized by explicitly learning pixel-wise correspondence mapping onto 3D pose-sensitive coordinates as an auxiliary task. Moreover, a sparse pair combination of pixel-wise features and soft voting on pixel-pair pose predictions are designed to improve robustness to inconsistent and sparse local features. Experiment results on the popular YCB-Video and LineMOD benchmarks show that the proposed W-PoseNet consistently achieves superior performance to the state-of-the-art algorithms.

This work has been submitted to the IEEE for possible publication. Submitted to IROS 2021

References in corpus (1)

• Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors

Cited by in corpus (4)

• DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency
• Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
• PAM:Point-wise Attention Module for 6D Object Pose Estimation
• StablePose: Learning 6D Object Poses from Geometrically Stable Patches