Exploring 6D Object Pose Estimation with Deformation

We present DeSOPE, a large-scale dataset designed for Deformed Six-DoF Object Pose Estimation. Most existing 6D object pose approaches assume rigid or articulated objects, leaving deformed daily objects largely unexplored. This gap limits the realism and robustness of current pose estimation methods, which often fail when objects deviate from their canonical shapes due to wear, collision, or deformation. To address this issue, we present DeSOPE, a large-scale real-world dataset specifically designed for deformed object pose estimation. DeSOPE contains two major components: (1) a collection of high-fidelity 3D scans of 26 common object categories, each captured in one canonical and three deformed states using a non-rigid alignment framework; and (2) a real-scene RGB-D dataset comprising 133K frames and 665K pose annotations across 104 deformed instances, recorded in both static and dynamic scenarios. The varying degrees of deformation introduce substantial geometric and textural changes, presenting new challenges for existing methods. We benchmark several state-of-the-art algorithms on DeSOPE and demonstrate significant performance degradation as deformation increases, highlighting the limitations of current pose estimators. As the first large-scale dataset designed for systematic study of deformed object pose estimation, DeSOPE lays the groundwork for developing 6D pose estimators capable of handling real-world deformation and variability.