  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.
