Enabling machines to better sense transparent surfaces would not only improve safety, but could also open up a range of new interactions in unstructured applications — from robots handling kitchenware or sorting plastics for recycling, to navigating indoor environments or generating AR visualizations on glass tabletops.
To address this problem, we teamed up with researchers from Synthesis AI and Columbia University to develop ClearGrasp, a machine learning algorithm that is capable of estimating accurate 3D data of transparent objects from RGB-D images. This is made possible by a large-scale synthetic dataset that we are also releasing publicly today. ClearGrasp can work with inputs from any standard RGB-D camera, using deep learning to accurately reconstruct the depth of transparent objects and generalize to completely new objects unseen during training.
From the blog post: