In recent years, there has been significant progress in robot manipulation research, but challenges persist when it comes to tracking transparent objects using RGB-D sensors. To address this, polarimetric imaging technology has gained popularity due to cost-effective sensors becoming more accessible. This technology, which analyzes the polarization properties of light, offers valuable insights into object properties, aids in material differentiation, depth estimation, and precise pose estimation, even in challenging lighting conditions. A novel multimodal perception approach is introduced in this study, utilizing a learning-based neural radiance field method based on multimodal data to overcome the limitations of RGB-D imaging when dealing with transparent objects during in-hand manipulation (Kerr et al.)