SynergyPose: Synergy Enhanced Feature and Projection Decoupling for 6-D Pose Estimation

Estimating the 6-D pose of texture-sparse objects is critical in vision-guided robotic manipulation and measurement. Existing single-RGB-based methods typically rely on multiple intermediate representations to improve accuracy, which increases computational and annotation costs. Alternatively, adopting a singular representation can mitigate these issues, but it still suffers from feature scarcity, resulting in unreliable intermediate representation and pose estimation accuracy. Embracing these challenges, we propose a novel single-RGB-based framework named SynergyPose, which achieves accurate pose estimation of texture-sparse objects using only one intermediate representation. SynergyPose consists of three innovative components: geometric enhancer (GEer), coordinate projection decoupling (CPD), and consistency constraint loss (CCLoss). Specifically, we design a novel clustering branch based on feature aggregation and allocation within GEer, which synergistically extracts complementary features with a convolution branch to enhance feature representation. Based on the interprojection coordinate consistency, we propose the CPD to capture the geometry of the object from different projection planes, which implicitly introduces multiple constraints to accurately calculate the dense correspondence without relying on other representations. Afterward, CCLoss enforces coordinate consistency between the projection planes, explicitly introducing an additional constraint. Extensive experiments demonstrate the effectiveness of SynergyPose, which achieves competitive results on four BOP benchmark datasets.