  This paper addresses the problem of image matting for transparent objects.
Existing approaches often require tedious capturing procedures and long
processing time, which limit their practical use. In this paper, we formulate
transparent object matting as a refractive flow estimation problem, and propose
a deep learning framework, called TOM-Net, for learning the refractive flow.
Our framework comprises two parts, namely a multi-scale encoder-decoder network
for producing a coarse prediction, and a residual network for refinement. At
test time, TOM-Net takes a single image as input, and outputs a matte
(consisting of an object mask, an attenuation mask and a refractive flow field)
in a fast feed-forward pass. As no off-the-shelf dataset is available for
transparent object matting, we create a large-scale synthetic dataset
consisting of $178K$ images of transparent objects rendered in front of images
sampled from the Microsoft COCO dataset. We also capture a real dataset
consisting of $876$ samples using $14$ transparent objects and $60$ background
images. Besides, we show that our method can be easily extended to handle the
cases where a trimap or a background image is available.Promising experimental
results have been achieved on both synthetic and real data, which clearly
demonstrate the effectiveness of our approach.
