  Despite the significant progress in six degrees-of-freedom (6DoF) object pose
estimation, existing methods have limited applicability in real-world scenarios
involving embodied agents and downstream 3D vision tasks. These limitations
mainly come from the necessity of 3D models, closed-category detection, and a
large number of densely annotated support views. To mitigate this issue, we
propose a general paradigm for object pose estimation, called Promptable Object
Pose Estimation (POPE). The proposed approach POPE enables zero-shot 6DoF
object pose estimation for any target object in any scene, while only a single
reference is adopted as the support view. To achieve this, POPE leverages the
power of the pre-trained large-scale 2D foundation model, employs a framework
with hierarchical feature representation and 3D geometry principles. Moreover,
it estimates the relative camera pose between object prompts and the target
object in new views, enabling both two-view and multi-view 6DoF pose estimation
tasks. Comprehensive experimental results demonstrate that POPE exhibits
unrivaled robust performance in zero-shot settings, by achieving a significant
reduction in the averaged Median Pose Error by 52.38% and 50.47% on the LINEMOD
and OnePose datasets, respectively. We also conduct more challenging testings
in causally captured images (see Figure 1), which further demonstrates the
robustness of POPE. Project page can be found with
https://paulpanwang.github.io/POPE/.
