6D pose estimation pipelines that rely on RGB-only or RGB-D data show limitations for photometrically challenging objects with e.g. textureless surfaces, reflections or transparency. A supervised learning-based method utilising complementary polarisation information as input modality is proposed to overcome such limitations. This supervised approach is then extended to a self-supervised paradigm by leveraging physical characteristics of polarised light, thus eliminating the need for annotated real data. The methods achieve significant advancements in pose estimation by leveraging geometric information from polarised light and incorporating shape priors and invertible physical constraints.

翻译：仅依赖RGB或RGB-D数据的6D姿态估计管线在处理具有无纹理表面、反射或透明等光度挑战性物体时存在局限性。本文提出一种基于监督学习的方法，利用互补偏振信息作为输入模态以克服上述局限。进而通过利用偏振光的物理特性，将该监督方法扩展为自监督范式，从而消除对标注真实数据的需求。该方法通过挖掘偏振光中的几何信息，并结合形状先验与可逆物理约束，在姿态估计中取得了显著进展。