연구성과
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
- 작성자관리자
- 작성일2023.09.21
- 조회수204
Taeyeop Lee, Jonathan Tremblay, Valts Blukis, Bowen Wen, Byeong-Uk Lee, Inkyu Shin, Stan Birchfield, In So Kweon, Kuk-Jin Yoon
상세내용
BibTeX
- 저자 : Taeyeop Lee, Jonathan Tremblay, Valts Blukis, Bowen Wen, Byeong-Uk Lee, Inkyu Shin, Stan Birchfield, In So Kweon, Kuk-Jin Yoon
- 논문명 : TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
- 학회명 : IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)
- 발간년도 : 2023
- 발간년도 : 2023
- 발간월 : June
- 초록 : Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requiring labels on the target data. In this paper, we propose a method of test-time adaptation for category-level object pose estimation called TTA-COPE. We design a pose ensemble approach with a self-training loss using pose-aware confidence. Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime. Extensive experimental results demonstrate that the proposed pose ensemble and the self-training loss improve category-level object pose performance during test time under both semi-supervised and unsupervised settings.