1. [Publications](/publications)
2. TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
 
 # TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation

  ![](/sites/default/files/styles/wide/public/publications/pic3.png?itok=aiav3rm-)

 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.



 ## Authors



Taeyeop Lee (KAIST)

[Jonathan Tremblay](/person/jonathan-tremblay)

[Valts Blukis](/person/valts-blukis)

[Bowen Wen](/person/bowen-wen)

Byeong-Uk Lee (KAIST)

Inkyu Shin (KAIST)

[Stan Birchfield](/person/stan-birchfield)

In So Kweon (KAIST)

Kuk-Jin Yoon (KAIST)

 

 

 ## Publication Date



Tuesday, June 20, 2023

 

 ## Published in



[CVPR 2023](https://cvpr2023.thecvf.com/)

 

 ## Research Area



[Computer Vision](/research-area/computer-vision)

 

 

 ## External Links



[project page](https://sites.google.com/view/taeyeop-lee/ttacope)