CVPR 2023 - TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation

In this episode we discuss TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation by Taeyeop Lee, Jonathan Tremblay, Valts Blukis, Bowen Wen, Byeong-Uk Lee, Inkyu Shin, Stan Birchfield, In So Kweon, Kuk-Jin Yoon. In this paper, the authors propose a method called "Test-Time Adaptation for Category-level Object Pose Estimation" or TTA-COPE, for addressing source-to-target domain gaps. They design a pose ensemble approach using pose-aware confidence and a self-training loss. Unlike previous methods, TTA-COPE processes test data in a sequential, online manner and does not require access to the source domain at runtime. Experimental results show improved category-level object pose performance under semi-supervised and unsupervised settings. The project page for TTA-COPE is available at https://taeyeop.com/ttacope.