  This paper presents an efficient symmetry-agnostic and correspondence-free
framework, referred to as SC6D, for 6D object pose estimation from a single
monocular RGB image. SC6D requires neither the 3D CAD model of the object nor
any prior knowledge of the symmetries. The pose estimation is decomposed into
three sub-tasks: a) object 3D rotation representation learning and matching; b)
estimation of the 2D location of the object center; and c) scale-invariant
distance estimation (the translation along the z-axis) via classification. SC6D
is evaluated on three benchmark datasets, T-LESS, YCB-V, and ITODD, and results
in state-of-the-art performance on the T-LESS dataset. Moreover, SC6D is
computationally much more efficient than the previous state-of-the-art method
SurfEmb. The implementation and pre-trained models are publicly available at
https://github.com/dingdingcai/SC6D-pose.
