SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
This addresses the problem of robust and efficient pose estimation for robotics and AR/VR applications, offering a novel approach that is symmetry-agnostic and correspondence-free.
The paper tackles 6D object pose estimation from a single RGB image without needing 3D CAD models or symmetry knowledge, achieving state-of-the-art performance on the T-LESS dataset and improved computational efficiency compared to prior methods.
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.