SC6D : symmetry-agnostic and correspondence-free 6D object pose estimation
Cai, Dingding; Heikkilä, Janne; Rahtu, Esa (2023-02-22)
D. Cai, J. Heikkilä and E. Rahtu, "SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation," 2022 International Conference on 3D Vision (3DV), Prague, Czech Republic, 2022, pp. 536-546, doi: 10.1109/3DV57658.2022.00065.
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https://urn.fi/URN:NBN:fi-fe2023041135725
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Abstract
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. More-over, 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.
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