Two-stage Monocular 6D Pose Estimation for Small Cubic Objects

Monocular 6D object pose estimation is an important per-
ception problem for robotic manipulation. For high-precision operations
such as block grasping, alignment, and placement, the perception mod-
ule must provide sufficiently accurate and stable pose estimates rather
than coarse object localization alone. This requirement is particularly
challenging for small cubic objects due to weak texture, limited visual
cues, strong rotational symmetry, and sensitivity to ROI quality.
In this paper, we study monocular 6D pose estimation of small cubic
objects from a single RGB image and propose a two-stage manipulation-
oriented framework. In the first stage, a detection-guided ROI-based re-
gression model is used to estimate object pose under symmetry-aware
supervision. In the second stage, we further explore a MuJoCo-based re-
rendering strategy to construct consistency-enhanced training samples
for refinement, aiming to improve adaptation to manipulation-relevant
visual conditions.
Experiments on a unified MuJoCo-generated dataset show that, on the
main single-block benchmark, the proposed method achieves the strongest
overall balance in ADD-S, translation accuracy, rotation stability, and
task-oriented usability metrics. Preliminary results in multi-block scenes
further suggest favorable generalization to more complex visual condi-
tions.