TTAPose: Test-Time Adaptation for Unseen Object Pose Estimation

Recent advances in the field of 6D pose estimation of unseen objects not present during training are promising, however, the performance gap between these general methods and object-specific methods remains significant. This paper introduces an innovative unsupervised test-time adaptation method, termed TTAPose, capable of adapting a pose estimator to any unseen object. TTAPose initially undergoes pre-training using a large synthetic dataset and thereafter refines the weights using unsupervised loss conducted on unseen real-world target objects. The network, based on a teacher-student architecture, leverages an RGB-D pose refinement pipeline to incrementally improve pseudo labels. Notably, TTAPose operates with no requirement for target data annotation, thus minimizing time and data expenditure. Experimental results show performance levels comparable to supervised methods, effectively narrowing the gap to object-specific baselines.