  Translucency is prevalent in everyday scenes. As such, perception of
transparent objects is essential for robots to perform manipulation. Compared
with texture-rich or texture-less Lambertian objects, transparency induces
significant uncertainty on object appearances. Ambiguity can be due to changes
in lighting, viewpoint, and backgrounds, each of which brings challenges to
existing object pose estimation algorithms. In this work, we propose LIT, a
two-stage method for transparent object pose estimation using light-field
sensing and photorealistic rendering. LIT employs multiple filters specific to
light-field imagery in deep networks to capture transparent material
properties, with robust depth and pose estimators based on generative sampling.
Along with the LIT algorithm, we introduce the light-field transparent object
dataset ProLIT for the tasks of recognition, localization and pose estimation.
With respect to this ProLIT dataset, we demonstrate that LIT can outperform
both state-of-the-art end-to-end pose estimation methods and a generative pose
estimator on transparent objects.
