  This paper proposes a deep learning based method for colored transparent
object matting from a single image. Existing approaches for transparent object
matting often require multiple images and long processing times, which greatly
hinder their applications on real-world transparent objects. The recently
proposed TOM-Net can produce a matte for a colorless transparent object from a
single image in a single fast feed-forward pass. In this paper, we extend
TOM-Net to handle colored transparent object by modeling the intrinsic color of
a transparent object with a color filter. We formulate the problem of colored
transparent object matting as simultaneously estimating an object mask, a color
filter, and a refractive flow field from a single image, and present a deep
learning framework for learning this task. We create a large-scale synthetic
dataset for training our network. We also capture a real dataset for
evaluation. Experiments on both synthetic and real datasets show promising
results, which demonstrate the effectiveness of our method.
