Exploring 6D Object Pose Estimation with Deformation

2026-04-08

AI Summary

This work addresses the limitation of existing 6D object pose estimation methods, which typically assume objects are rigid or articulated and thus struggle with real-world deformations caused by wear, impact, or other factors. To bridge this gap, we introduce DeSOPE, the first large-scale dataset specifically designed for deformable object pose estimation, encompassing high-fidelity 3D models of 26 common object categories under standard and three realistic deformation states, along with 133,000 RGB-D frames. Leveraging a semi-automatic pipeline that integrates 2D instance segmentation, initial pose estimation, object-level SLAM refinement, and manual verification, we provide 665,000 high-accuracy pose annotations. Experiments demonstrate a significant performance drop in current methods as deformation intensifies, underscoring DeSOPE’s critical role as the first benchmark for evaluating 6D pose estimation under realistic object deformations.

Abstract

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/.

Problem

• 6D object pose estimation
• non-rigid deformation
• deformed objects
• pose robustness
• real-world applicability

Innovation

• 6D object pose estimation
• deformable objects
• large-scale dataset
• non-rigid deformation
• RGB-D SLAM