Transparent Object

Transparent object perception is a challenging computer vision problem focusing on accurately capturing depth and geometric information from objects that refract and reflect light, hindering standard depth sensor performance. Current research heavily utilizes deep learning, employing architectures like vision transformers and convolutional neural networks within pipelines that often incorporate multi-view stereo, optical flow, and neural radiance fields to overcome these limitations. This research is crucial for advancing robotics, particularly in manipulation tasks requiring interaction with transparent objects in real-world settings, such as automated industrial processes or assistive robotics in healthcare. The development of large-scale synthetic and real-world datasets is also a key focus, enabling the training and evaluation of increasingly robust and accurate models.