@inproceedings{de8d03e87b464128bb587f5d90acc3e0,
title = "SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation",
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.",
keywords = "6D object pose, correspondence free, symmetry agnostic",
author = "Dingding Cai and Janne Heikkila and Esa Rahtu",
note = "Funding Information: This work was supported by the Academy of Finland under the project \#327910. Publisher Copyright: {\textcopyright} 2022 IEEE.; International Conference on 3D Vision ; Conference date: 12-09-2022 Through 15-09-2022",
year = "2022",
doi = "10.1109/3DV57658.2022.00065",
language = "English",
series = "Proceedings - 2022 International Conference on 3D Vision, 3DV 2022",
publisher = "IEEE",
pages = "536--546",
booktitle = "Proceedings - 2022 International Conference on 3D Vision, 3DV 2022",
address = "United States",
}