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.

翻译：我们提出DeSOPE，一个用于6自由度变形物体的大规模数据集。大多数6D物体姿态方法假设物体为刚性或铰接物体，该假设在实践中并不成立，因为物体因磨损、冲击或变形而偏离其标准形状。为模拟这一现象，我们引入DeSOPE数据集，其包含26个常见物体类别的高保真3D扫描数据，每个物体均以一个标准状态和三种变形配置进行采集，并实现了与标准网格的精确3D配准。此外，该数据集还包含一个RGB-D数据集，涵盖多样场景下的133K帧图像以及通过半自动流水线生成的665K个姿态标注。我们首先为每个实例标注2D掩码，然后使用物体姿态方法计算初始姿态，通过物体级SLAM系统进行精细化调整，最后执行人工验证以生成最终标注。我们对多种物体姿态方法进行评估，发现随着变形程度增加，性能急剧下降，这表明对此类变形进行鲁棒处理对于实际应用至关重要。