MemPose: Category-level Object Pose Estimation with Memory

In the pursuit of robust and generalizable category-level ob-ject pose estimation, most existing methods adopt parametric formula-tions that learn effective representations from data, yet they primarilyencode category-level patterns into fixed shape priors or static param-eter weights, which limits their scalability to highly diverse instances.In this paper, we rethink category-level pose estimation from a memory-centric perspective and present MemPose, a memory-augmented frame-work that explicitly incorporates category-level geometric memory intothe pose estimation pipeline. We introduce an external memory bufferthat stores and dynamically updates structural representations frompreviously observed instances, enabling the model to leverage accumu-lated experience to support current perception. Extensive experimentson four challenging benchmarks (REAL275, CAMERA25, Housecat6Dand Wild6D) demonstrate the superiority of our proposed method overprevious state-of-the-art approaches.