SMOC-Net: Leveraging Camera Pose for Self-Supervised Monocular Object Pose Estimation

Tao Tan, Qiulei Dong

研究成果: Conference contribution同行評審

摘要

Recently, self-supervised 6D object pose estimation, where synthetic images with object poses (sometimes jointly with un-annotated real images) are used for training, has attracted much attention in computer vision. Some typical works in literature employ a time-consuming differentiable renderer for object pose prediction at the training stage, so that (i) their performances on real images are generally limited due to the gap between their rendered images and real images and (ii) their training process is computationally expensive. To address the two problems, we propose a novel Network for Self-supervised Monocular Object pose estimation by utilizing the predicted Camera poses from unannotated real images, called SMOC-Net. The proposed network is explored under a knowledge distillation framework, consisting of a teacher model and a student model. The teacher model contains a backbone estimation module for initial object pose estimation, and an object pose refiner for refining the initial object poses using a geometric constraint (called relative-pose constraint) derived from relative camera poses. The student model gains knowledge for object pose estimation from the teacher model by imposing the relative-pose constraint. Thanks to the relative-pose constraint, SMOC-Net could not only narrow the domain gap between synthetic and real data but also reduce the training cost. Experimental results on two public datasets demonstrate that SMOC-Net outperforms several state-of-the-art methods by a large margin while requiring much less training time than the differentiable-renderer-based methods.

原文English
主出版物標題Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
發行者IEEE Computer Society
頁面21307-21316
頁數10
ISBN(電子)9798350301298
DOIs
出版狀態Published - 2023
對外發佈
事件2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Vancouver, Canada
持續時間: 18 6月 202322 6月 2023

出版系列

名字Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2023-June
ISSN(列印)1063-6919

Conference

Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
國家/地區Canada
城市Vancouver
期間18/06/2322/06/23

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