  Establishment of point correspondence between camera and object coordinate
systems is a promising way to solve 6D object poses. However, surrogate
objectives of correspondence learning in 3D space are a step away from the true
ones of object pose estimation, making the learning suboptimal for the end
task. In this paper, we address this shortcoming by introducing a new method of
Deep Correspondence Learning Network for direct 6D object pose estimation,
shortened as DCL-Net. Specifically, DCL-Net employs dual newly proposed Feature
Disengagement and Alignment (FDA) modules to establish, in the feature space,
partial-to-partial correspondence and complete-to-complete one for partial
object observation and its complete CAD model, respectively, which result in
aggregated pose and match feature pairs from two coordinate systems; these two
FDA modules thus bring complementary advantages. The match feature pairs are
used to learn confidence scores for measuring the qualities of deep
correspondence, while the pose feature pairs are weighted by confidence scores
for direct object pose regression. A confidence-based pose refinement network
is also proposed to further improve pose precision in an iterative manner.
Extensive experiments show that DCL-Net outperforms existing methods on three
benchmarking datasets, including YCB-Video, LineMOD, and Oclussion-LineMOD;
ablation studies also confirm the efficacy of our novel designs.

Computer Vision and Pattern Recognition