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The authors introduce DeSOPE, a large-scale dataset for 6D object pose estimation of deformed objects, comprising 3D scans and an RGB-D dataset with 665K pose annotations. They capture 26 object categories in canonical and deformed states, with accurate 3D registration. Experiments show existing pose estimation methods struggle with increasing deformation, highlighting the need for robust methods.
Current 6D pose estimation methods fall apart when objects are deformed, as revealed by the new DeSOPE dataset.
We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption that fails in practice as objects deviate from their canonical shapes due to wear, impact, or deformation. To model this, we introduce the DeSOPE dataset, which features high-fidelity 3D scans of 26 common object categories, each captured in one canonical state and three deformed configurations, with accurate 3D registration to the canonical mesh. Additionally, it features an RGB-D dataset with 133K frames across diverse scenarios and 665K pose annotations produced via a semi-automatic pipeline. We begin by annotating 2D masks for each instance, then compute initial poses using an object pose method, refine them through an object-level SLAM system, and finally perform manual verification to produce the final annotations. We evaluate several object pose methods and find that performance drops sharply with increasing deformation, suggesting that robust handling of such deformations is critical for practical applications. The project page and dataset are available at https://desope-6d.github.io/}{https://desope-6d.github.io/.
