Self-supervised Transparent Liquid Segmentation for Robotic Pouring
Gautham Narasimhan,Kai Zhang,Ben Eisner,Xingyu Lin,David Held,Gautham Narasimhan,Kai Zhang,Ben Eisner,Xingyu Lin,David Held
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem. We propose a novel segmentation pipeline that can segment transparent liquids such as water from a static, RGB image without requiring any manual annotations or heating of the liquid for training. Instead, we use a generative model that is capable ...
