  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. Project page:
https://taeyeop.com/ttacope
